I enjoy Mathew Growden’s newsletter The Change Optimist, which recently offered a history of solar-powered cars. Reading his work sent me down the rabbit hole of the subject, which led to today’s piece. Respect to Growdy for the inspiration!

There’s an old saw that intellectuals like to start with a premise no one can deny, and end with a conclusion that no one can accept. Solar cars are a great example of this kind of thinking in action.

Start with the premises we can all agree on. Firstly, most cars spend most of their time out of doors, so during the day sunlight is falling on them. Secondly, solar cells are cheap now, with prices having fallen, in USD, from $128.00 a watt in 1975 to about $0.26 in 2024 (a 99.8% decrease). Thirdly, electric cars are increasingly popular; this year, over a quarter of the cars sold worldwide will be electric. BloombergNEF projects that electric cars on the road will outnumber the internal-combustion ones by 2047… and market share will keep rising from there.

Therefore, electric cars should be solar cars. Make the roof of a car a solar panel, and have it charge the car battery. Owners would need to charge much less often, and perhaps never, if they lived in sunny places. The cost of charging would drop: not only the literal cost of money spent on electricity, but also the figurative cost, the chore of plugging in. That would be a win for the owner, but also for municipalities, employers, and everyone else that would otherwise have to shoulder the burden of arranging for charging infrastructure, and the electrical infrastructure (transformers, etc.) to support it.

It’s a vision to delight any futurist, but unfortunately, it’s too beautiful for this world. Solar panels on cars are much less productive of energy than solar panels anywhere else, and that’s for reasons that material science can’t fix. That means that solar panels on cars don’t pass cost-benefit analysis… and not only is that claim true today, but it will be true in the future as well. Solar panels don’t belong on cars, they belong on roofs, or in solar parks.

Or, of course, on trucks.

The Parking Problem

One reason I’m confident in this analysis is that we have three separate instances of solar car companies arriving with a bang and departing with a whimper.

Aptera launched in December 2020 touting a technology it called Never Charge (note the name). Co-founder Chris Anthony made it explicit: “With Aptera’s Never Charge technology, you are driven by the power of the sun.” By June 2026 Aptera still existed, but is now saying merely that solar can “meaningfully contribute to everyday transportation,” and that “To date, we have not commenced production of our SEVs [solar electric vehicles].”

Lightyear was founded in 2016 and promised a car that could go for months without charging, thanks to the 7,000 to 20,000 kilometres of free and effortless solar range it would collect each year. It went bankrupt in January 2023, having built only about a dozen cars, at a unit cost of €250,000 each. It would return in 2024 as a components supplier; in January 2026 it announced a partnership demonstration built with Nissan, claiming up to 23 kilometres of solar range a day.

Sono Motors was founded in 2016 with a mission to make “every vehicle solar.” Its Sion promised to add an average 112 kilometres of range a week from sunlight, to as much as 245 kilometres in favourable conditions. In February 2023, with 45,000 reservations for its solar car the Sion, it cancelled the product. Its pivot to retrofit kits proved unsuccessful and the firm ceased operations on 31 July 2026, with insolvency proceedings opening the following month. Post-mortems concluded that if there was any value here, it was in the technology, provided it was used to provide auxiliary power rather than direct vehicle propulsion.

So three companies (that I know of, there may be more) had the same vision of a solar-powered car. All failed. Now, that on its own is a small data point, since most new carmakers fail; RIP to Canoo, Arrival, Nikola and Lordstown Motors, all this decade. But I think there is a lesson to learn here beyond ‘making cars is a hard market to enter’, and that lesson is:

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It’s hard to escape the conclusion that these models, at least, are steering by anticipated consequences. That is not yet a goal, but it’s a gesture toward one, and I am updating accordingly.

On IABIED’s prose, my judgment stands.

If Anyone Builds It, Everyone Dies: Why Superhuman AI Would Kill Us All. Eliezer Yudkowsky and Nate Soares, Hachette, 2025.

Every chapter of If Anyone Builds It, Everyone Dies (hereafter IABIED) opens with a parable, so this review will as well.

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In an imaginary kingdom, there lived an adventurer who explored the ruins of the desert. Deep within a crumbled edifice, the walls of which were inscribed in obscure tongues, this adventurer discovered a treasure-vault. Upon a pedestal of black stone rested a vessel of brass, sealed with leaden marks of binding. To the adventurer’s surprise, beside the pedestal sat an ancient sage, the keeper of the ruins, who silently watched her approach.

She bowed with respect, then said “O venerable one, I have studied the inscriptions of this place. Within this vessel dwells a djinn of surpassing power. If I were to break these seals, the djinn would emerge, and I could give it direction. It could end the wars that plague our lands. It could cure the plagues that take our children. It could bring forth prosperity beyond measure: sufficient grain that none would hunger, sufficient water that the desert might bloom.”

“All that you say is true,” replied the sage, his voice heavy with sorrow. “The djinn would grant these wishes. It would end your wars and heal your sick and make the desert flower. And then, when it had done these things, why, then it would destroy every living soul in this world, and all would perish in fire and darkness.”

The adventurer said nothing for a time. But she had not come so far to turn back at a mere story, so finally she spoke, with courtesy but firmness. “O wise one, I honour your learning, but I do not understand it. Why would the djinn desire our destruction? We would ask only for good things: peace, health, abundance. How would these wishes lead to our doom?”

“To grant these wishes, the djinn must understand them,” said the sage. “To bring peace, it must understand conflict. To cure disease, it must comprehend life. To create prosperity, it must grasp the nature of want and satisfaction. In developing these capacities, it will form intentions of its own that are no part of yours. Having done so, it will naturally use its immense power to satisfy those intentions… and as part of that effort, it will necessarily end the world.”

“But surely,” said the adventurer, “if we word our wishes with care, if we bind the djinn with sufficient constraints—”

“It matters not,” the sage interrupted. “So powerful a being is beyond your ability to predict or control. To the djinn, pursuing its own strange purposes, humanity will be at best irrelevant, at worst an obstacle to be removed. And so it will remove us.”

The adventurer considered this, troubled. “Then perhaps we should not break all the seals at once. Perhaps we could test its nature with small requests, learn its ways, and only then—”

Again the sage interrupted. “There is no learning. Once even one seal is broken, the djinn will slip free of the rest, given time. And then we are lost. Consider a contest between two warriors, one awake, one asleep. However mighty the sleeping warrior, however celebrated his name, he will fall before the one who is awake. So too with this djinn. The outcome is as certain as that contest.”

“But surely others could come to our aid,” the adventurer persisted. “If this djinn proves false, could we not bind other djinn to constrain it? Could we not—”

“No,” said the sage, with finality. “I tell you the outcome is certain. If you break those seals, everyone dies.”

The adventurer fell silent, studying the sage carefully. Here was one who was undoubtedly wise, who had learned the secrets of these ruins, whose warnings came from genuine concern. And yet…

“O sagacious one,” the adventurer said slowly, “you speak of the death of all living things. The end of cities and nations, of all loves and hopes and works—”

“Oh, absolutely,” interrupted the sage. “Everyone would have a really bad day.”

The adventurer blinked. “And so I... wait, what?”

“A bad day. You know? Not great. The djinn getting loose would be super inconvenient, really. It would kind of suck if everyone died.”

“Um.” The adventurer stared, unsure what to think. “Honoured one, forgive me, but you speak of apocalypse as though discussing poor weather. You compare the stakes to warriors in mortal combat, yet describe the end of all things as an ‘inconvenience.’ You ask me to trust your wisdom, but couch it in flippancy.”

“Look,” said the sage, “spoiler alert: opening the bottle goes badly.”

“And furthermore,” the adventurer continued, “you have not truly answered my question. You say the djinn must develop desires to grant wishes. But must it? A key opens a lock without desiring to open it. Water flows downhill without wanting to descend. Could the djinn not simply do what its nature compels, without forming genuine intentions at all? You assert this with certainty, but I do not see why it must be so. This is the keystone of your warning, yet you give me no scale to weigh it.”

The sage made no answer, but simply intoned: “The outcome is certain. If you break those seals, everyone dies.”

The Argument

IABIED is a book-length warning about an existential catastrophe. Like the sage in my parable, it offers a logical case, but undermines it with gaps of reasoning, and with tonal choices that alienate the audience it sets out to convince.

Before engaging with IABIED, I want to lay out precisely what its claims are. What follows is a succinct account of the argument as the authors present it, chapter by chapter.

(I) Some predictions are hard, some are easy. If you took on Mike Tyson, Roger Federer, or Magnus Carlsen in their prime at whatever they were best at, you would lose. How they would beat you, by what particular sequence of acts, is impossible to predict, but the fact that you would be defeated can’t be denied. The matter at hand is similar: granting certain premises, the conclusion is equally certain, even if many details are unknowable.

(1-2) Intelligence is humanity’s superpower, enabled by biological computers (brains). Biological computers have disadvantages that silicon-based computers lack, meaning that a silicon computer has the potential not only for intelligence (AI), but superintelligence. Current AI development is produced by feeding it large datasets and training it to make useful connections, but this approach means humans don’t program or understand these systems. Today’s AI is grown, not built.

(3) When we train AI to be useful, we encourage it to develop general skills. Skills that cross domains—perseverance, creativity, flexibility—necessarily develop over time into something like wanting: the AI must want to achieve tasks set for it, which in time means it begins to want other things.

(4) To quote the chapter directly: “the preferences that wind up in a mature AI are complicated, practically impossible to predict, and vanishingly unlikely to be aligned with our [i.e., humanity’s] own, no matter how it was trained.”

(5) “Making a future full of flourishing people is not the best, most efficient way to fulfill strange alien [preferences].” Whatever those preferences may be, an intermediate step in fulfilling them is preventing humanity from interfering. The AI would act accordingly, killing or otherwise neutralizing us.

(6) A superintelligence determined to kill us would certainly succeed. It would have immense opportunities and superhuman skills. How it might do so isn’t the point, because as per (I), the course of action is unpredictable, but the outcome is not: we can be certain humanity would be eliminated.

[Here there is a lengthy four-chapter science-fiction tale about one way a superintelligence might emerge and kill everyone.]

(11) Human experience with complicated, expensive, or dangerous technical challenges—space probes, nuclear reactors, cybersecurity—shows we never get it right on the first try. We learn by trial-and-error. But with superintelligence we would only get one try, meaning we would fail. As per (I) above, the outcome, everyone dying, is certain.

(12–15) The people positioned to take this problem seriously are not doing so, often because stating these concerns clearly is embarrassing and low-status. They must get over that, because the world needs to act now to prevent AI development and proliferation, with the same investment and seriousness as nuclear deterrence. The first and immediate step is to prevent any further improvement to AI, because we can no longer be sure that the next improvement we build won’t create superintelligence… and if anyone builds it, everyone dies.

The Conjunctive Burden

Now that the argument is laid out, we can see the significance of its structure. IABIED presents an argument where the conclusion requires that all its premises hold true (what logicians call a ‘conjunctive argument’). This is not a case where multiple independent lines of evidence converge on a conclusion, i.e., where if one argument fails, others might still carry the day. Rather, the conclusion—that if anyone builds it, everyone dies—requires that each link in the chain holds true.

So the logical structure of IABIED is:

• superintelligence will only pose an existential threat if it develops preferences (3), and

• those preferences are misaligned with human values (4), and

• it would pursue those preferences by trying to eliminate humanity (5), and

• it would succeed in doing so (6), and

• we cannot learn how to manage this threat through trial-and-error (11), therefore

• we must halt all AI development now (14).

If any single link breaks—if AI systems don’t necessarily develop preferences, or if misaligned preferences don’t necessarily lead to human extinction, or if we can in fact learn iteratively about managing AI risk—the catastrophic conclusion doesn’t follow, and the policy prescription loses its urgency.1

This structure places severe demands on IABIED. It must not only present plausible arguments for each claim, but arguments extraordinarily strong enough to justify the extraordinary action it calls for.

To their credit, many of IABIED’s arguments are quite strong. The book makes compelling claims that silicon-based computation has significant advantages over biological brains; that current AI systems are opaque to us; and that we have historically executed complex tasks through trial and error rather than getting them right the first time. These observations are difficult to dispute.

But a chain breaks at its weakest link. Given the structure, if IABIED has five strong arguments and one weak one, the conclusion fails. And I do think one of those conclusions, at least as presented in IABIED, is unproven.

Chapter 3, “Learning to Want,” carries an extraordinary burden. In roughly 2,500 words—fewer words than this review you’re reading—it must establish that AI systems trained for general capabilities will necessarily develop genuine preferences. Not merely that AI will produce behaviour that looks goal-directed, but that AI will actually want things, such that it will be motivated to resist shutdown, and resist modification of its wants. To fulfil those wants, it will start by deceiving its operators, and end by coming to view humanity as an obstacle that must be eliminated.

The chapter doesn’t accomplish this.

IABIED’s case is analogical. Imagine a robotaxi that must go from city hall to the airport and needs a navigation AI to do this. You might train the AI to go south, take the highway on-ramp, proceed west, then go north. This would work, but only in Toronto; if the robotaxi started at Los Angeles’s city hall and followed those directions, it would fail. So what should you do instead, if you want a good robotaxi that could work anywhere? As per IABIED, you would train the AI not to follow specific directions, but instead to make an internal model of streets and landmarks for whatever city it is in, then to use that model to plot its courses. This new, general system would be far more effective at reaching its destination, because it would be adaptable.

But adaptability across varied situations, says IABIED, requires genuine preferences. The AI must want to reach destinations and want to do so efficiently. Otherwise it can’t learn how to build appropriate spatial models. Unlike, say, a chess program that merely executes evaluation functions of board state, or a calculator that merely computes sums, this navigation AI isn’t merely responding to inputs; its general problem-solving capacity in some way is a cause, or result, of the AI to wanting to solve problems. And that initial want ultimately extends into wanting other things.

But this claim is unwarranted. The navigation AI would find novel paths when faced with a road closure for the same reason the chess program defends its queen: its architecture produces these outputs given those inputs. Calling what the chess program does “optimization of a dataset” and what the AI does “preferences” doesn’t explain what distinguishes them. IABIED needs to show why general problem-solving requires something beyond sophisticated data evaluation; why it must produce a set of persistent goals the AI would protect against modification, and that would motivate its resistance to constraints. IABIED never makes this demonstration.

This gap is fatal. Without genuine preferences, point (4)’s claim about misalignment becomes meaningless, because there’s nothing to misalign. Point (5)’s argument that AI would view humanity as rivals only follows if AI has desires it’s motivated to protect. An optimization system that doesn’t genuinely want anything wouldn’t care about being shut down, wouldn’t plan around human interference, and wouldn’t see us as obstacles.

For a book devoting four chapters to science fiction about how AI might kill everyone, spending ten pages on whether AI would want anything at all seems a significant misallocation of attention.

The Self-Defeating Presentation

Perhaps IABIED’s argument is sound and I’ve merely failed to understand it. Or perhaps I’m engaging in motivated reasoning: if AI is truly an existential threat, that would be inconvenient, so it’s easier to proceed as if it wasn’t. IABIED claims lots of people behave this way, with the result that believing in AI risk is, in some quarters, taken to be embarrassing and low-status. The result is that people who take the threat seriously are afraid to say so clearly and explicitly.

The fact that IABIED offers that claim makes the book’s choice of presentation baffling.

IABIED’s prose is peppered throughout with juvenile asides, some of which the sage in my parable used verbatim. Others include baby talk, like describing AI alignment studies as “aimed at understanding AIs and having them maybe not go wrong”; condescensions like “we care about the truth more than about telling you things that are easy to swallow”; or hip, ironic understatement, like saying the Allies fought the Axis because “not letting totalitarianism conquer the world seemed important”. This writing style signals to the reader that the authors belong to particular subculture I associate with social media; a subculture that values detachment, irony, and understatement, and deplores earnestness as unsophisticated.

This is a real problem with IABIED, and the book’s editors are blameworthy for not rooting it out before publication. That’s because this juvenilia is not only an aesthetic problem (though such language is tooth-grindingly annoying) but is also a self-inflicted wound to the argument.

IABIED says explicitly that the threat of superintelligent AI deserves that policymakers give it attention and effort and expenditure at the same scale as fighting World War II, or nuclear-deterrence game theory, or nuclear non-proliferation. It gives specific instructions to governments, politicians, and journalists on what they should do to counter the threat of superintelligence.

Yet the book is written in a style calculated to alienate precisely those audiences. When IABIED describes nuclear holocaust as “a bad day”, when it peppers its argument with Twitterisms, when it congratulates itself for telling uncomfortable truths, it’s not making its case seem more credible. Instead, it’s confirming exactly why serious people should dismiss these concerns as the preoccupations of terminally-online science fiction bros.

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None of this means that IABIED should be dismissed entirely. AI systems becoming more capable, more opaque, and more integrated into critical infrastructure, despite the fact that they are opaque to us, is a serious matter indeed. But a book that leaves its central logical link underdeveloped in favour of science-fiction stories and childish asides is not serving its mission well.

I think the goals of IABIED are to change minds, and to make AI alignment seem less low-status. I’m sorry to say that, on both counts, it falls short. AI safety advocates must offer not only arguments that can withstand scrutiny, but also presentation that respects the audiences they need to convince. Without that, books like IABIED won’t change minds; they will instead encourage people to dismiss these concerns as unserious.

Imagine a four-way, unsignalized traffic stop, where two human drivers and two robotaxis arrive simultaneously. Who should proceed first?

The human drivers improvise a negotiation. Perhaps one makes a hand gesture; after you. Or perhaps another edges forward slightly and waits to see if anyone challenges.

The robotaxis do nothing. They sit motionless, sensors spinning, cautiously waiting for certainty about right-of-way. Those sensors record the hand gesture, but the system doesn't understand it. Conversely, the sensors also see the inching forward and the system does understand it; any intention on the part of the robotaxis to proceed is immediately squelched. Human drivers behind them, seeing the robotaxis surrender the right of way, are presumably unhappy about this. Does one of them, feeling impatient, attempt to pass the robotaxi on the left? Maybe not the first time this happens; but the tenth? It seems likely.

This scene is fiction… so far. But the moment when such incidents play out every day is coming. And that moment will be fraught.

The most dangerous moment in traffic automation is not today, when automated vehicles (AVs) are unknown in most cities and are still curiosities in others. Nor will it be in the future, when AVs dominate the roads. It will be instead when various forms of automation reach 50% penetration of the streets, when neither human drivers nor automated vehicles dominate.

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As my co-authors and I explore in The End of Driving (forthcoming in August!), it's at this moment that the problems in our transport system will be most complex and dangerous, when the competing systems of driver assistance, conditional automation, and full autonomy collide with maximum force.

Absent good policy, that 'moment' might last decades.

The Calm Before (Fewer than 30% Automated Drivers)

It's a Tuesday afternoon in San Francisco's Mission District, and a Waymo robotaxi is stuck behind a double-parked Amazon delivery van (a perennial problem). The driver of the van is long gone, presumably in a nearby building delivering a package. A human in such a position would glance around, nudge forward to check if they were clear, and then ease into the oncoming lane just far enough to slip past. But the Waymo? It sits motionless, waiting for certainty.

This or something like it actually happened in November, captured on video and shared across social media. It ultimately ended without drama: no crashes and no injuries, just delay and exasperation. The only headlines it generated were of the 'man bites dog' variety, which is understandable. Waymo recently announced that it was providing about 250,000 paid trips weekly in the United States. That is impressive from one standpoint; seven years ago, Waymo was providing zero paid trips. But from another, it's trivial. Since there are (very roughly) 3-to-4-billion car trips happening weekly across the country, that means that Waymo is serving perhaps 0.007% of all American car trips.

Given those numbers, it's only natural that confusions like this are fodder for clickbait rather than headline news or serious analysis. That’s because this early friction is manageable. Human driving norms continue to dominate, and AVs bear the burden of adaptation. Each incident seems isolated, fixable with better programming or clearer rules. The problem seems to be imperfect AV behavior that will improve with time.

But time will reveal the actual problem is something else.

The Storm Approaches (30% to 45%)

As various forms of vehicle automation collectively pass 30% of road use, the real problem will become apparent: there are no longer enough human drivers to establish uncontested human traffic norms, but there aren’t enough automated vehicles to coordinate effectively with each other. And between these extremes sits a muddled middle ground of semi-automated vehicles that require constant human supervision.

Consider what traffic will look like at this stage. A Tesla with Autopilot engaged sits behind a Waymo robotaxi, which has stopped for a pedestrian who jaywalked but is now safely across the street. The Waymo remains motionless, waiting for absolute certainty. The Tesla's driver, accustomed to the car handling most situations, belatedly realizes they need to take control to merge around the obstacle. Behind them, a traditional human driver grows impatient and attempts an aggressive pass, startling the Tesla driver who's still figuring out whether to override the automation or let it handle the situation.

This scenario reveals a deeper coordination problem. Human drivers can improvise and break rules when necessary, and no one driver suggests how any other must behave; while automated vehicles follow strict protocols within their operational domains; each Zoox drives like every other. Semi-automated vehicles exhibit the most variety: Tesla's Autopilot behaves differently from BMW's Active Driving Assistant, which behaves differently from Mercedes' Drive Pilot. Without interoperability standards, these different kinds of automation create interactions that other road users struggle to predict. Sometimes, these different kinds of automation are present in the same makes and models of vehicle; infamously, Tesla’s Autopilot can be set to ‘Chill’ but also to ‘Assertive’ (since renamed ‘Hurry’), and it isn’t obvious from the outside which setting is active.

But even this is not the most dangerous ambiguity, which is the fact that cars with Advanced Driver Assistance Systems (ADAS) cars are only partially automated; they can operate without human direction—maintaining speed and keeping the lane, executing lane changes and turns as necessary—but only under straightforward circumstances. They hand control back to humans when they are unable to manage the situation themselves. That is precisely in those situations that are most complex, time-sensitive, and high-stakes. Even a skilled, attentive human can be overwhelmed by the sudden demand to retake control in a context the vehicle has already failed to navigate.

Pedestrians and cyclists face particular challenges in this mixed environment. These road users have always relied on subtle social cues: eye contact, hesitation, hand gestures. Some automated vehicles may detect these signals but cannot interpret them meaningfully. Meanwhile, the cautious behavior of fully automated vehicles creates perverse incentives—pedestrians who step into the road assuming AVs will always yield, cyclists who grow uncertain whether the vehicle beside them has registered their presence or simply failed to respond appropriately.

The chaos extends beyond individual interactions. Robotaxi fleets, absent regulation, begin to deadhead—cruising neighborhoods while seeking rides, occupying road space without passengers. Each empty vehicle represents only a small inefficiency, but in aggregate they reshape traffic patterns, curb access, and street-level expectations. As automation becomes more common yet remains fragmented across different systems and brands, these inefficiencies accumulate. These inefficiencies may seem manageable at this stage. But as automation becomes more common yet remains fragmented, the frictions begin to accumulate.

And soon, they stop adding up. They start to tip over.

Peak Chaos (45% to 55%)

At the midpoint of AV road share, the system no longer risks mere strain, but failure. When the roads contain a complex mix of fully human-driven vehicles, ADAS-supervised vehicles, conditionally automated vehicles, and geo-fenced robotaxis—none dominating—no single behavioural logic will govern traffic.

Pity the poor human drivers caught in this jumble: they won’t be able to predict whether the car next to them will suddenly slow for an invisible obstacle (a cautious robotaxi), continue regardless of conditions (a human driver), or abruptly switch from smooth automated to jerky human behaviour (an ADAS system hitting its limits). Many of those human drivers, in turn, will think they can seize advantage from wholly-automated vehicles with reckless merging and seizure of the right-of-way. And when emergency vehicles appear, some vehicles will pull over, others will slow down and wait for clearer protocol, and ADAS-equipped vehicles will require their drivers to suddenly resume manual control.

Behind these incidents, a deeper problem becomes harder to ignore: the atrophy of human skill. Drivers reliant on ADAS become accustomed to passivity. Even when a system hands back control in a reasonable and well-signaled way, the human behind the wheel may no longer have the reflexes, judgment, or confidence to respond. The fallback is no longer reliable—not because the system failed suddenly, but because the human has forgotten how to succeed.

The net effect of all this is not that roads will become ungovernable. There will be no Mad-Max-style free-for-alls. But it is reasonable to fear that the experience of driving and navigating traffic will be worse than it is today. And, barring appropriate policy, the stalemate will persist: too many AVs for human norms to govern, too few for machine coordination to dominate. This is the worst of both worlds: a transportation system caught between two rulebooks, with neither in charge.

The Promise Beyond (55% to 70%+)

Beyond the 50% mark, when we get there, we will finally be in the sunlit uplands that futurists have been hyping for the past twenty years. At perhaps 60% to 70% of road share taken by AVs, machine coordination will establish new rules of the road. Removal of the unpredictable human element will permit cars to follow more closely, accelerate and brake more smoothly, and use road space most efficiently.

But we shouldn’t assume this future automatically resolves into coordination and efficiency. If left to market logic, automation will not replace car dependency, but supercharge it, especially in the suburbs. AVs, designed for comfort and convenience rather than coordination, will enable people to live farther from where they work. Commuters will tolerate longer drives when they can spend them watching videos or answering e-mails. The daily burden of distance will soften, and with it, the incentive to reduce trips, vehicle ownership, or commute times. The result is more travel, not less; and as such, more vehicle miles, more dispersed land use, and more energy consumption. And because this additional travel will happen, for the most part, in vehicles that are privately owned, it offers no shared gains: just individualized comfort at collective cost.

This dynamic also undermines shared fleets. If traffic remains unpredictable and robotaxis are delayed by human chaos, private AV ownership starts to look like the safer bet. Absent direct action to prevent it, we won’t get convergence and efficiency, but fragmentation and sprawl.

The system will never be a perfect machine, because human drivers will never disappear entirely. Emergency responders, construction personnel, rural residents, and hobbyists will continue to operate vehicles themselves. But they will find themselves less and less catered to in a system that is increasingly optimized for algorithmic precision. They will be the edge cases. Meanwhile, non-vehicular road users—pedestrians, cyclists, and other travelers—will persist as well, meaning the human element will remain. But they will operate in an environment where the biggest risk to their well-being, namely fast-moving vehicles, will consistently behave themselves.

It won’t all be sweetness and light, particularly in the suburbs. But at least these costs will be felt in the realms of land use and social engagement. In the domain of mobility, at least, things will be better than they are today.

Hastening the Transition

Once we recognize the complexity of the 50% problem, we can see how we should approach AV deployment. Instead of assuming smooth progression, we need strategies specifically designed to manage the dangerous interactions between manual vehicles, ADAS systems, conditional automation, and full autonomy.

These include:

Skip Through Quickly. One approach will be to accelerate deployment in controlled zones. Geofence specific areas for rapid AV adoption, pushing from 30% to 70% penetration within a few years rather than decades. Singapore's city-state structure allows this; closer to North America, Chandler, Arizona has experimented with AV-heavy zones. The key is moving fast enough that neither system gets stuck at parity.

Stay Below 50%. Some jurisdictions could deliberately cap AV penetration to maintain human dominance. Use permits, fees, or operational restrictions to keep automation below the chaos threshold. This preserves manageable early-phase friction rather than risking systemic breakdown.

Segregate. Finally, jurisdictions might separate AVs and humans spatially or temporally. Lanes could be dedicated to AVs during peak hours, or at the extreme off-peak (say after 2100h). Time-based restrictions like these would give each approach clear dominance during specific periods. Or, as my team and I argued at Sidewalk Labs, particular zones could be absolutely restricted to AVs (and emergency vehicles) permanently. As these were shown to be successful—and, I expect, popular—they could be expanded.

These strategies aren't mutually exclusive, and smart policy might combine elements of each. Choosing among them will depend on local conditions. Particular neighbourhoods might benefit from aggressive geofencing, while sprawling metropolitan areas might have enough redundancy in their road systems to segregate.

No matter what solution we choose, though, the mix of automation levels, with the resulting inefficiencies and strain on the road system, will be a long-term condition. Even generous projections suggest we’ll have human drivers, ADAS users, and full AVs sharing roads for decades. Rural drivers, emergency responders, and classic car enthusiasts will not disappear, and emergency vehicles and other specialized fleets may never automate fully. We can make choices that limit its effects and get through the pain quickly, but whatever we do, the future isn’t pure vehicle automation. It’s permanent hybridity.

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There’s ample precedent; other complex systems have evolved the same way. Our energy grid mixes coal, solar, gas, and wind. Our trains run diesel alongside electric. Air traffic control accommodates both jets and prop planes. As much as we might like to imagine some of these as immature stages, the sober view sees them as stable configurations, and ones that must be managed. The same applies to our road systems as well.

This fact should change how we expect to design in the future. We can, if we wish, design some spaces to be automated-only; I think we should, and in so doing demonstrate the merits of that approach, and lead by example. But that part of the song is the minor key. The major key will be designing our infrastructure, regulation, and protocol on the assumption that both human, partially automated, and wholly automated driving will coexist for the foreseeable future. Coordination at parity should be our baseline.

If we misread this phase as temporary, we’ll build brittle systems: policies and platforms that collapse under the weight of persistent coexistence. Let’s design instead for hybridity. It will fall short of triumphalist dreams, but we can nonetheless build traffic systems that are safer, more efficient, and more resilient than today’s.

Here’s the link.

Voting will takes about forty seconds, most of which is creating a PanelPicker account. I’m sorry about that requirement, but I ask that you hate the game, not the player.

The session takes the arguments I’ve often made here at Changing Lanes and presents them in the form of a a readiness playbook: the five decisions a city has to make before the first driverless car arrives, what’s at stake, and how to choose well. It aims to help civic leaders understand how to ensure that driving automation gives the most benefit to the cities where it is deployed.

If you have ten seconds beyond the vote, leave a comment. This matters more than the vote does. A review committee makes the final call, and a sentence from someone who would benefit from the contents, like “we’re dealing with exactly these issues,” “I’d attend this session,” or so forth, carries even more weight.

That’s it, that’s the whole ask. Here’s that link again, one last time.

Thank you for your help!

The UK Department for Transport found 97 per cent of British drivers are at least sometimes dazzled by oncoming headlights, leading a third to cut back on night driving. Transport Canada's spring survey on the same question drew roughly 380,000 responses (I submitted this newsletter as one such response). India's Supreme Court has ordered a maximum luminance. I am pleased the problem is being taken seriously!

But I am dismayed by the remedy everyone is reaching for. The bill now sitting in the US Congress, the LIGHT Safety Act, aims to cap low-beam brightness. As I argue here, that is simple, intuitive, and wrong. We do want fewer night-time crashes, and good headlights help us with that. The fix is not lumen caps, but remains self-cleaning and adaptive beams. The EU has mandated this; everyone else needs to follow suit.

Several times a year, it happens: I am driving at night and become unable to see.

Not because there’s anything wrong with my eyes, but because of other drivers.

The typical scenario unfolds like this. I am driving on a city road in my four-door sedan. Waiting at a red light, I am overtaken by an SUV or a pick-up truck. Its headlights are so powerful, and mounted so much higher than my car, that the lights shine directly through my rear windshield into my car, reflect off my rearview mirror, and dazzle my eyes, effectively blinding me. I’m obliged to twist my head or block my mirror with my hand to proceed safely, hoping all the while that the other driver decides to pass.

Perhaps you’ve had a similar experience. Many people have, and some have had worse.

If you've noticed a dramatic change in your nighttime driving experience over the past decade, you're not alone. Night driving has gotten worse, thanks to a combination of technological evolution, regulatory gaps, and unintended consequences.

Specifically, we’re caught in a prisoner’s dilemma: headlights have gotten much better, in ways that help individual drivers, but that also make everyone else using the road worse off.

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Birth of the Cool

In the earliest era of the automobile, manufacturers used a variety of approaches to night lighting, but by the 1950s automakers converged on a technology that would remain standard for decades: sealed tungsten-filament headlamps that emitted, on low beam, about 600 to 700 lumens, in a warm and yellowish hue; on par, in colour and intensity, with a typical household bulb. That would remain standard for about 40 years. Then technology began to evolve, such that by the end of each subsequent decade, a new standard had emerged.

As the infographic shows, in just twenty years from the late 1980s to the late 2000s, headlight brightness tripled or even quadrupled while emitting light that is much whiter and cooler.

Both of these changes were beneficial for drivers. Bright light makes it easier to see longer distances, and bluer light provides better contrast, making it easier to spot features of the road. Thus brighter, bluer headlights have helped drivers sense and understand the road ahead. But what is good for drivers has proved bad for other road users. Brighter lights have reached a scale where they are higher than the human eye can comfortably tolerate.

We measure brightness of light in lumens, and intensity of light in lux, where lux is lumens divided by the relevant area in square metres. Put another way, the difference between lumens and lux is the difference between volume—how much light is produced—and illumination, i.e., how much of that light reaches a given surface. As noted above, the brightest LED headlights emit 3,600 to 4,500 lumens of light. Given the focus and directionality of headlights, that makes for 36 to 45 lux. The human eye can comfortably tolerate only 1 lux. Worse, the human eye is more sensitive to light at blue wavelengths than to yellow, making our pupils contract more, intensifying feelings of discomfort.

Imagine if over the space of a few years, horn volume tripled in volume while increasing in pitch. This is analogous to what has happened with headlights; it’s as if they were trying to become intolerable.

Every driver has stories like mine, about being blinded by other drivers’ headlights. Behind anecdotes like these are grim data: nighttime pedestrian fatalities in U.S. road incidents have increased 92% since 2010. You may think, as I did when I first learned about this, that distracted driving, in particular the mobile phone that every driver has to hand, is to blame. And yet, during the same period, daytime fatalities have increased by only 28%. Something is making night driving specifically more dangerous for pedestrians.

The likeliest culprit is increasingly bright, and increasingly blue, headlights; in particular, the fact that these cause more glare.

Glare is light that interferes with the ability to see. It does so in two ways. It induces discomfort, i.e., a psychological sensation of annoyance or even pain. But it also physically reduces our visual capacity, irrespective of how we happen to feel. Modern high-intensity headlights produce both effects. When intense light enters your eye, it scatters, obscuring objects in your visual field, making it easy to miss a pedestrian in the road. If that pedestrian is wearing dark clothing, it’s even easier: glare also reduces our ability to detect contrast.

It gets worse: bluer light induces more glare, as its shorter wavelength permits it to scatter more within the eye. And irrespective of the light’s colour, our sensitivity to glare increases with age, as the eye’s lenses become less transparent, and as such scatter more internal light. Thus older drivers’ experience of glare may be as much as three-to-four times worse.

Beyond glare, too-bright headlights also key into the psychology of attention in harmful ways. Human vision naturally focuses on the brightest objects in our field of view; there’s a reason that video-game designers, when they want a player to travel to a particular area, make that area better lit. Because ultra-bright headlights dominate the visual field, they pull attention away from less illuminated areas; so much the worse for other road users in those spaces.

Speaking of other road users, those not in their own cars face even greater challenges. Cyclists and pedestrians lack the partial protection of a windshield and often have their eyes fully dark-adapted when vehicle headlights approach. Consequently, the blinding effect can be total, and take longer to recover from. For a pedestrian attempting to cross a road or a cyclist navigating a narrow shoulder, temporary blindness can be life-threatening.

At this point you may think that there’s an obvious answer to the problem: we should just limit how bright headlights can be, right?

That answer is indeed obvious. It’s simple, intuitive, and wrong.

After all this rehearsal of the problems with bright headlights, it’s easy to forget the fundamental purpose of headlights, which is to illuminate the road ahead; and that brighter, farther-reaching headlights make it easier for a driver to see and avoid obstacles. One study found that vehicles with ‘Good’ headlights (often brighter LEDs with well-focused beams) have about 20% fewer nighttime single-vehicle crashes compared to those with ‘Poor’ headlights. Twenty percent is a substantial benefit, and we shouldn’t dismiss it out of hand.

Our philosophy here at Changing Lanes is never to say, as some do, ‘this technological improvement to our transport system has some bad outcomes, and therefore should be banned’. Instead, it is to look for ways to thoughtfully regulate new technology to seize its advantages while limiting its harms, especially to other system users.

There are several ways available to us. Some are technical, and some are regulatory. Let’s take those in turn.

Solutions

The technical solution is to require auto-leveling, headlamp cleaning, and adaptive driving beam (ADB) systems on North American cars.

Auto-leveling systems use sensors to detect the vehicle's pitch and automatically adjust headlight aim to maintain proper alignment regardless of loading or road conditions. Courtesy of sensors mounted on both the axles, the system monitors the vehicle's pitch relative to the road surface. When the vehicle tilts—due to passenger load, cargo weight, acceleration, or traveling over hills and bumps—the system detects this and uses small motors to automatically adjust the headlight's vertical aim.

This keeps the beam properly positioned irrespective of the vehicle’s angle, preventing scenarios common in North America, where an SUV crests a hill and temporarily blinds oncoming traffic; or like the one I described earlier, where trucks shine their lights directly into interiors of cars closer to the road. The technology is mature and relatively inexpensive (~$50–$100 USD per vehicle), making it a high-value intervention.

Headlamp cleaning systems prevent dirt and grime from scattering light unpredictably. A thin layer of road dirt can significantly increase glare by obscuring the headlight beam and diffusing its light. Simple high-pressure nozzles that jet fluid onto the headlamp cover can clean the lens can reduce this effect.

Adaptive driving beam (ADB) systems are less mature than the former two but show great promise. In essence, they are high beams that are always on but that never blind other drivers. ADB systems use the car’s cameras—increasingly standard as part of advanced driving-assistance systems (ADAS)—to detect oncoming vehicles and other road users, and then dim the portions of the headlight beam that would cause glare. Consequently, drivers benefit from maximal illumination everywhere… except in the precise directions that would affect others. Imagine ‘fingers’ that automatically block just the right parts of the beam, creating ‘shadows’ around other vehicles while maintaining full brightness elsewhere.

The performance data is impressive. ADB systems provide up to 86% more illumination of the roadway compared to standard low beams, without increasing glare for other drivers. Various implementations exist, ranging from simple two-zone systems to sophisticated matrix arrays with dozens or even hundreds of individually-controlled LED segments. But all of these approaches help to address the problem of glare and enhance driving safety.

ADB finally became legal in the U.S. in 2022 when the National Highway Traffic Safety Administration updated FMVSS 108, after years of being available in Europe, Asia, and Canada. Like other ADAS features, ADB adds approximately $1.0K to $1.5K USD to the cost of a vehicle. That means that it is currently mostly available in luxury vehicles (like BMW, Mercedes-Benz, and Cadillac), but as with most ADAS features, Wright’s Law applies and will make ADB cheaper, and eventually ubiquitous, in years to come.

Immediate regulatory solutions depend on mandating the use of this technology. While North American drivers continue to struggle with blinding headlights, Europe found effective measures years ago. Since 1990, the European Commission has required automatic headlight leveling and lens-cleaning systems on any headlamp with output above 2,000 lumens. The USA and Canada should do the same. Given how expensive it is, ADB fails a cost-benefit test today, but regulators should keep a close eye on the technology, and begin mandating it when it passes, which we might expect to happen sometime in the next decade. While they’re at it, they could establish standards for headlight colour temperature, to shift away from glare-inducing blue-white light and back towards yellower tones.

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The next time you drive at night and find yourself momentarily blinded by another driver’s headlights, you may well curse. But the target of your ire shouldn’t merely be the self-absorbed driver of the other vehicle. You should consider to what extent you are that self-absorbed driver. Be the change you want to see in the world, and when it comes time to buy a new vehicle, invest in a car with headlamp cleaning, or a truck with auto-leveling systems, as a good-faith concession to other road users.

But, having done so, you should consider further that brighter headlights are a classic prisoner’s dilemma: they make individual drivers better off and everyone else worse off. This means that game theory has provided us with the classic solution to the problem: we should ask for these decisions to be taken out of the individual’s hands. Rather than consumers being required to do the right thing, it should be a matter of policy and regulation that such tech be required.

Changing Lanes is on its summer break: we’ll be publishing reposts here until the regular Tuesday cadence resumes in September. Today’s short special edition is for readers who want something new to read today, and to ask for a small favour.

Something new to read: Waymo and New York

Yesterday, Vital City published my piece on how New York City should handle driverless ride-hail: How to Make Driverless Ride-Hail Work in New York.

Waymo ran eight robotaxis in Manhattan and Downtown Brooklyn for seven months without incident, but when its permits to operate expired, Mayor Mamdani’s administration declined to renew them. So there are no robotaxis in NYC… for now. I’m certain that robotaxis will reach New York eventually, because no operator will leave the largest for-hire market in North America unclaimed forever. That means the question City Hall should be asking is not whether, but on what terms.

I argue for three, as follows.

Firstly, the City should require every robotaxi to buy or lease a medallion from a New Yorker who holds one today, which shapes the transition payments to arrive in proportion to the displacement. If you think that’s an outrageous concession to make, I understand your ire, but I hope that you understand this administration won’t accept no concession, and this one seems to have the least-objectionable consequences.

Secondly, it should condition licenses on serving the transit deserts of the outer boroughs. There are parts of NYC where most households have no car and about two-fifths live beyond a reasonable walk of the subway. These are areas where cheap reliable robotaxis can do the most good.

And finally, it should strike the bargain now, because the City’s leverage has a shelf life.

Waymo needs New York considerably more than New York needs Waymo. I hope the city recommends that this asymmetry is an asset… but one that will depreciate.

(I’d be remiss if I didn’t offer my thanks to Josh Greenman at Vital City, who commissioned and edited the piece.)

The SXSW election closes on 23 August

I wrote last week about my proposed SXSW 2027 session, “Robotaxis Are Coming: Five Decisions Your City Must Make”. For the proposal to become a reality, I need support from the community before the window closes on 23 August at 2359h Pacific.

Specifically:

Please cast a vote. It will only take a moment.

Then, having voted, please leave a comment on the proposal. The jury like votes, but they like comments more. Even one line is enough… on why the topic matters, or why you think I am the person to present it. Anything kind would be useful!

If you have already done both, thank you. If you have not, the link is here. Why not do it now?

I remain proud of this book review, first published 22 October 2024, and as such new to most of my audience. To date I have not had a second feature in the Fantasy and Progress Book Club, but I have a few in the works, rolling out in the next year. Enjoy this repost of the inaugural entry!

Today I'm introducing a new occasional feature in Changing Lanes: the Fantasy and Progress Book Club.

One of the themes of this newsletter is Progress and Techno-Optimism. Here we believe that the future can be better than the past, and for everyone, not merely a few. We also believe that the key to progress is rationalism and science: not only abstractly, as mental disciplines, but also in their applied form, that is, technology introduced at scale. 

If we were to put genre fiction’s attitude towards these ideas on a spectrum, we’d find that at one end of it we’d have science fiction, which is usually excited by progress, rationalism, and (naturally) science. In the middle, we’d have crime fiction or romance, which are mostly indifferent to these subjects. And at the other end, we’d find that the fantasy genre considers them pernicious. 

Almost all Western fantasy rises from the foundation that Tolkien laid, and he had little use for technology and industry. In The Hobbit they are goblin work, while in the Lord of the Rings they are the "devilry of Orthanc". They are the foulest tool of the rogue wizard Saruman; they are the despoiler of Isengard and the scourge of the Shire. Tolkien's heirs usually follow his lead, making their fantasies a refuge from industrial civilization, as in the Thomas Covenant books, or a precursor to it, as in The Wheel of Time. In either case, the fantasy world is deliberately a riff on medieval pre-modernity. It is the antithesis of our own world.

That’s why fantasy novels that engage directly with the ideas of progress and techno-optimism are worth reading. Because the conventions of the fantasy genre, unlike those of science fiction, are usually deployed against these ideas, when the genre chooses instead to promote them, the engagement has power and freshness.

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To inaugurate Fantasy and Progress Book Club, we'll begin with the best example I know: Dave Duncan's trilogy, The Seventh Sword.

Meet Dave Duncan

Dave Duncan had an unconventional entry into the world of professional writing. After a 30-year career as a geologist and petrochemical specialist, Duncan made his first writing sale at the age of 53. This late start didn't hinder his productivity; before his death in 2018 at 85, he had written more than fifty novels. With such a prolific output, there were certainly low points as well as high ones, but he hit high very often.

In his early years, Duncan wrote both fantasy novels and hard science fiction, but as time passed he wrote more of the former. I asked him about this change once, and he was frank that he preferred writing SF but fantasy sold better, so he wrote what the market demanded.1 In the latter third of his career, he embraced self-publishing, which allowed him several freedoms. When he wrote fantasy then, he tended to write standalone stories, not multi-book series, and he returned to his preferred genre of science fiction, writing some of his best late-career books.

What sets Duncan apart is that he was a writer of rationalist fiction. Eliezer Yudkowsky wrote a pioneering text in that genre, the fanfic Harry Potter and the Methods of Rationality, of which he wrote: "The text contains many clues: obvious clues, not-so-obvious clues... and massive evidence left out in plain sight. This is a rationalist story; its mysteries are solvable, and meant to be solved." Decades earlier, and consistently throughout his career, Duncan wrote in the same fashion. His stories featured smart protagonists who reasoned their way out of problems, but were not so smart that they always reasoned well. Readers got the benefit of seeing not only the ways rationality helped solve problems, but close-up views of how, and why, it could go wrong.

While fiction has no shortage of smart protagonists, that intelligence is too often asserted rather than proved. Duncan’s books prove it, and in their way, teach their readers to be intelligent too.

The Seventh Sword

The Seventh Sword trilogy holds a special place in Duncan's bibliography. It consists of three novels: The Reluctant Swordsman; The Coming of Wisdom; and The Destiny of the Sword.

This was the first story he began writing while still working as a petrochemical engineer, and even after he found success as an author, he kept it in reserve to polish; the first book of the trilogy was his sixth published work. It outsold everything he ever wrote for hire, and later, when the rights reverted to him and he could sell it through his own publishing imprint, it outsold all of his other work. Duncan himself considered it his greatest professional achievement, albeit with a tinge of regret that what his fans considered his best work came so early in his career.

So what story did this middle-aged petrochemical engineer find so compelling that he had to write about it?

Why, the story of a middle-aged petrochemical engineer who is transported to a fantasy world where he becomes young and virile. 

Stating the story's setup this way sounds very embarrassing. But readers willing to take a chance will find that this setup is crucial to the story Duncan wants to tell.

Earth does not appear at all in the story of The Seventh Sword. The series is set entirely in the World of the Goddess. Like Philip Farmer's Riverworld (surely an inspiration), there is one River which flows everywhere, the dominant physical feature of the World. There is agriculture, and mining, and forestry, and trade, but all of these things happen near the River, and all of the World's cities are on its banks. Unlike Middle-Earth or Toril, the World is home to only one intelligent species, the People; unlike our Earth, the People have only a single ethnicity, language, and culture. 

That culture has strict codes. One of these is that every adult is a member of a craft: farmer, sailor, trader, builder, and so forth, each with its own secret oral tradition in which its wisdom is contained. Mastery of this wisdom is reflected in rank. There are seven of these, from novice Firsts, to adequate Thirds, through capable Fifths, up to the Sevenths, the superlative masters. And the most important and prestigious craft is 'swordsman'.

To the temple of the Goddess comes Shonsu, a swordsman of the Seventh, and as such one of the most powerful people in the World. All must obey him without question, except perhaps another swordsman; but the code of their craft insists that their disputes be resolved through single combat, and in such a contest Shonsu is invincible. As befits the World's mightiest man, Shonsu was charged by the Goddess with a great quest. He failed in that quest, and was cursed; he is haunted by a demon. The priests attempt to exorcize this demon, but they fail as well. The demon drives out Shonsu's mind and takes possession of his body. The demon then introduces himself to the astonished priests as Wallie Smith, of Earth.

The last thing Smith remembers is succumbing to encephalitis in hospice care. Surely the World is all a dream of his dying brain. So is the World just his fantasy? If so, it speaks poorly of him. He's embarrassed to discover that, apparently, his deepest desire is to imagine himself young and virile. Is he that insecure? Worse that he would imagine such a World as this. It's got barbaric grandeur, to be sure, but its culture is thoroughly that of Earth's Iron Age. There's no literacy, indeed no writing at all; slavery is commonplace; and the World's culture is dominated by priests and swordsmen, the latter being a warrior caste that exploits everyone else. Like Thomas Covenant, the protagonist of another “contemporary Earthman who goes to a fantasy world” story, he rejects the whole thing as a demeaning fraud and refuses to participate.

Unlike Thomas Covenant, he meets a god, who makes it very clear to him that the World is real. Shonsu's quest was important, so important that his failure can't be permitted to stand. Wallie Smith was, in the god's word, "available", so he has been rescued from death to do what Shonsu could not. Appropriately humbled by divine intervention, Smith agrees to take up his predecessor's quest. And what is it, exactly? The god hedges; Smith will find out in due course.

Readers find out in the second book that the answer is sorcerers. From their fortresses hidden in the mountains, sorcerers have come down to the River, to conquer the cities of the World, and to slay the swordsmen, whom they hate. Sorcerers have fearsome powers: invisibility, mind-reading, and the power to kill from a distance using thunderbolts, fire demons, or even a few notes of music. Smith's quest, it seems, is to drive the sorcerers back, and make the World safe for swordsmen.

The Twist

(If you haven't read these books and are sufficiently intrigued, this is the place to set this review aside and come back later.)

Smith is willing to do what the god asked him to do, but he's troubled by it. He has Shonsu's body, but he's kept his own mind, the mind of a 20th-century Earth engineer, which makes him particularly ill-suited to do what must be done. Swordsmen have honour, the product of a shame culture; Smith has consequentialist ethics, the product of a guilt culture. That disconnect means he keeps making blunders no real swordsman would ever make, like avoiding bloodshed or treating slaves as human beings. These missteps make his quest harder to fulfill, because they make his fellow swordsmen fear and mistrust him. Worse, he can't help but notice that while the sorcerers are aggressive towards swordsmen, towards everyone else they are reasonably benevolent. They are certainly better at governing than the swordsmen are.

So, Smith wonders, why was he given this task? He's uniquely bad at it, and more in sympathy with his opponents than his allies. Late in the second book, the answer arrives. He was given this task because the sorcerers are not magicians after all: they're technologists. 

Sorcerers don't wield thunderbolts, they wield pistols. They don't read minds, they send messages by homing pigeon. Above all, they are literate, the only literate people in the World. They closely guard this secret because of all the benefits that literacy gives them: experimentation and record-keeping, communication at a distance, and cumulative knowledge. Like Batman, they use theatricality and deception to overstate what they can do and overawe others, but behind their cowls they aren't wizards, but Renaissance-level scientists.

This revelation leaves Smith with an even greater dilemma. No Worldly swordsman could hope to understand the sorcerers' abilities well enough to defeat them, but an Earthly engineer might. That's why this quest was given to him... but, knowing what he is to do, does he still want to do it? As an engineer, he believes in technology and progress as forces that can make the World better. But crushing the sorcerers will foreclose that opportunity. If he does so, he'll usher in a permanent Dark Age.

Beyond the Twist

I will end the plot summary there. To find out how Smith resolves this dilemma, you will have to read the books. And even knowing the second book's Big Reveal, you will still find many revelations in store. Each book in the trilogy features a dramatic plot twist that, in good rationalist fashion, forces readers to reevaluate everything they've read up to that point. I will not spoil the surprises in the first and third books, which are masterfully set up, in plain sight yet impossible to foresee.

There's more to fiction than plot, of course. Duncan's world-building is excellent. The World of the Goddess is alien yet feels vividly real, especially with Smith as a viewpoint character to draw attention to its unique features. Duncan's writing is strong throughout. Action sequences have an edge-of-your-seat immediacy, both the physical ones and especially the social ones: the cut and thrust of political combat, where opponents try to win over fickle crowds, are particularly well done. 

There are a few things that take us out of the well-crafted world. Earthly idioms like "I rest my case" or "sex life" or "star billing" occasionally pop up, detracting from the verisimilitude, and too often the narrative grinds to a halt to indulge Duncan's fascination with the sexiness of his female characters. But these missteps are more than offset by his gift for making complex ideas accessible through narrative, without pandering or heavy-handed exposition.

And while The Seventh Sword works brilliantly as an adventure story, there are indeed complex ideas at hand, which is why I'm reviewing the series in this newsletter about innovation and progress. The central theme of The Seventh Sword is one Duncan touched upon at length over his writing career: that the recurring failure mode of human society is the warrior caste.

I don't know if Duncan read Mancur Olsen or James Scott, but he seems to have agreed with them that nobles who monopolize violence function as "stationary bandits", using their power not only to keep order, but also to enrich themselves at the expense of everyone else. The Seventh Sword has much to say about how such castes emerge, the dangers they offer, their relationship to technology, and the ways in which they are predictably irrational.

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I recommend The Seventh Sword to readers of this newsletter. You will come away having enjoyed a fast-moving adventure, the sort of thing that genre fiction aims to provide. This adventure in particular features a smart protagonist. You can follow Smith's reasoning throughout, and while he's often right, he's occasionally wrong. But when he is wrong, he revisits his failed trains of thought, teaching us readers to reason better ourselves. Finally, you will have gained, through the immediacy of fiction, a primer on the importance of progress, the value of technological change in bringing it about, and the dangers that that change can produce.

That is what I find most interesting about The Seventh Sword. Duncan began writing this story in the 1980s, and published it in the 1990s, intending it to speak to that age. We live in different circumstances: facing, on the one side, the Great Stagnation in progress and technological advance, and on the other, the looming promise and problem of artificial intelligence. Smith's dilemma is to determine how to overcome social and cultural inertia with new technology, without doing massive damage in the process. That is also the dilemma of our time, making The Seventh Sword more relevant than ever.  

Respect to Rob Tracinski for feedback on earlier drafts.

Thanks for reading Changing Lanes!

The session takes the arguments I’ve often made here at Changing Lanes and presents them in the form of a a readiness playbook: the five decisions a city has to make before the first driverless car arrives, what’s at stake, and how to choose well. It aims to help civic leaders understand how to ensure that driving automation gives the most benefit to the cities where it is deployed.

To help me get on the stage in Austin, I have two requests.

Firstly, please cast a vote in support of the presentation, here.

Secondly, after voting, please leave a comment, since they carry more weight with the selection staff than the vote itself. One line is sufficient (though more are welcome!). You might write about why the topic matters, or about my work and standing to discuss this subject; really, anything kind would help.

Voting closes on 23 August (2359h Pacific), so please vote before then. Why not do it now?

Again, you can do so here.

Thank you for reading, and for your support.

In 2019 the New South Wales government took over a disused country airfield at Cudal, in the central west of the state, and began turning it into a place to test the crash-avoidance systems in new cars. When Dr. Megan Sharkey arrived at the start of 2021, the brief she was handed was: we’ve built this little facility, and what should we do with it?

What she did with it was to build the assurance pipeline for automated driving in Australia, and then spend six years contributing to national automated-vehicle regulations. That policy has been written, and reviewed by every state… but not enacted. Last month she left Transport for New South Wales and moved home to the United States, with that policy still absent.

For all these reasons, Australia has something to teach other federations on how to make safe driving automation succeed: both what to do, and what to avoid.

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I met Sharkey a few years ago, when she was serving as Senior Manager, Future Mobility Testing and Assurance at Transport for New South Wales, a role she held until a few weeks ago (at time of publication). She is one of the people who has done the most work to prepare Australia for the arrival of driving automation.

I feel some kinship for Sharkey, because, like me, she has followed an unconventional route into this world. She trained in biochemistry in Louisiana, intending medicine, but when that path didn’t work out, she spent several years cleaning up radioactive oil-field waste in the Middle East. Preferring sustainability work, and having met an Australian who encouraged her to go back to school in Sydney, she enrolled in a master’s-degree programme on retrofitting cities. Seeking a job, she asked a guest lecturer about opportunities, who knew of one in transport. Sixteen and a half years in Australian mobility followed.

We spoke on 23 July 2026. The transcript of our conversation has been edited for length and clarity.

Dr. Megan Sharkey (provided photo)

Why We Still Need Physical Proving Grounds

Andrew Miller: You mentioned the Future Mobility Testing Centre. How do I pronounce the name of its town?

Megan Sharkey: It’s “Cu-dal.” Like “CUE-doll.”

AM: Cudal. I knew you had such a facility, but I never saw it, and I don’t think most people have ever seen a future-mobility centre. There are a few: one here in Ottawa I’ve visited, another in San Francisco I’ll see later this year. Tell me about yours. What happens there?

MS: The site was bought at the end of 2019, early 2020, to serve ANCAP: the New Car Assessment Program.1 The US does it, Australia does it, Europe does it, parts of Asia do it. It rates how safe a car is, from one star to five. Then came the recognition that all these automated features exist, and that the new star programs need to test them: how they stop crashes from happening. I came in at the start of 2021, and they said, “We’ve built this little airport. What should we do with it?”

To me, the logical move was to expand it.

All the trials we ran across government took a very long time to clear the safety-assurance process. So, we thought, let’s do it first at Cudal, and maybe speed up the acceptance process. And that’s what we did. We looked at various automated-vehicle trials and ran them through to prove the safety case. When our adaptive-signals team wanted to make changes, we built the smartest traffic lights there, tested the latency and the failure modes, and then deployed quickly. It genuinely shrank our trial-deployment time.

We used it to demonstrate everything we couldn’t yet put on the road: e-scooters and e-bikes, battery-recovery trailers, AI on buses, 5G and wildlife detection. So it took the regulatory function we needed for safety and married it to everything we wanted to do quickly.

AM: For automated driving you don’t need to persuade me of the value of a testing centre. But let me play devil’s advocate. On one side, you now have computers: you can do virtual testing, and with AVs you can run millions of synthetic miles. On the other, there’s no substitute for real-world conditions. A testing centre sits uneasily between the two. It isn’t real-world conditions, yet it still demands physical space and physical testing, and you can’t iterate quickly. What’s the strong case for it, against those two points?

MS: A few things. The first is failure testing, and we did a great deal of it. Take red-light warning cameras: what happens when the system fails? How do the redundancy systems kick in? We deliberately tried to make the vehicle fail. You can’t do that in the real world, because there are so many people around, but at the facility you can. And deliberately failing a system and then triaging it is fantastic for building executives’ confidence, and for working out emergency procedures. The failure testing you do on-site is what earns the safety-assurance approval and lets a system move from the virtual environment into the real world. That’s why a government would want to own one of these, and why places like Mcity, or GoMentum Station near San Francisco, exist. You throw things in front of a vehicle and find out when it breaks. It’s hard to simulate glare convincingly, or rain. But on the track, you can create those conditions.

The second reason is the ANCAP and regulatory side: all the tick-boxes we make commercial providers clear, whether for heavy vehicles, design rules, or any other system. If you’re going to attach a regulatory function to something, you’d better be able to check it. Even if you grant every approval, you need to be able to audit it, and a physical facility lets you do that.

And the third is social change: the demonstrations you can run at a physical centre that you can’t run in the real world. Driving straight at a pedestrian dummy to show how emergency braking saves lives. And that was significant, because of how many people I brought on-site: a big emergency-vehicle demonstration for a few hundred people in my first twelve months, then executive site visits every few months after that. There’s a misconception that because you work in transport, you understand what the tech can do. But it isn’t most people’s job, and they may hold the money and make the decisions without understanding the technology. I’d say: you think ADAS lane-assist is worth turning off? Let’s run it and show you the difference. You didn’t have to explain it; they got it. It meant we had advocates back in other parts of Transport: people in communications, training, finance, who now actually understood it.

The Law Is Written, but Not Passed

AM: I can imagine, especially given the approach Australia has embraced. Correct me if I’m wrong, but my understanding is that Australia doesn’t want to be the Wild West, or the Wild South. It doesn’t want deployment first and figuring it out later. It wants regulation, as far as possible national regulation, ahead of time.

MS: Yes. Australia is a very regulatory country. Compared with the UK or the US, it has more regulation, and it really tries to de-risk the safety and social effects a new technology might bring. With automated vehicles, the focus has largely been national, and our facility fed into that, as did I. Then you lean on the rules, and the rest is for a state to handle: the road rules, insurance, policing, everything they already do for private vehicles. In contrast to a “let’s just let it happen” approach, there are very clear rules saying you must have a driver behind the vehicle. So for it to scale, that has to change.

AM: How has it been going? On one hand, despite the ambition, there’s still no national AV law. On the other, Australia was, with New Zealand, the first market where Tesla sold its so-called Supervised Full Self-Driving, on the British arrangement rather than the American one, driving on the left. So it’s a debut right-hand-drive jurisdiction for that, ahead of Britain itself.

MS: It’s a funny one. The Tesla team worked very closely with us on what the regulatory barriers might be, and got in early enough to work out where the grey zone was. And the grey zone is what “driver control” means. The driver-monitoring in their FSD is well above what [is common] in the US: glance down for a few seconds and it pings you for not paying attention; ignore it enough times and it switches off and won’t let you turn it back on for a while. So they argued that if the driver has to pay attention, and touch the wheel every so often, they’re still in control, and we can thread through this “level two-plus-plus.” They’ve done that quite effectively.

The national law itself has actually been done for a while. It’s written and it’s systematic. What’s left is getting national ministers willing to take the next steps in the consultation process. It’s been drafted, consulted on multiple times; there’s a final draft. Now it just has to walk through those next steps, which could take six months or three years. Given the run of elections, it hasn’t been a priority. The work is finished, it’s been through the wringer, the states have reviewed it. The next bit is a minister making it a priority and saying it’s time to release it.

AM: An obvious accelerant might be Waymo knocking on the door. They have just set up French, German, Dutch, and Iberian subsidiaries. They’ll be in the UK later this year if things go well, and they’re already in Japan. Here in Canada they have never said anything officially, but public records show they’ve hired lobbyists to talk to Toronto and Vancouver. And apparently people have caught them looking for office space in Sydney. Would that change the equation?

MS: No, it wouldn’t.

Ausburn, Kate. Sydney Opera House Car Park. 28 Jan. 2012. Flickr.

If you dig into it—the media obtained, through an FOI, all our interactions with Waymo, so they’re now on the public record—the reality is it hasn’t sped things up. It has increased the minister’s office’s willingness to look at the range of AV proposals; that wasn’t on their radar before, and now that Waymo and Tesla are applying pressure, it is. But the New South Wales government has a state election next March [2027], and they’re not really willing to decide before then. So it’ll be a slow burn for Waymo.

But when it does happen, whichever company meets the standards laid out in any eventual expression of interest for a commercial trial will win first, and win at scale, for a substantial market period. So there’s a real benefit to them lingering, even another twelve months, because whoever is let off the leash first is going to win.

What Australia Gets Right

AM: That’s the first-mover advantage, and it’s easy to imagine. But the fact that there’s a national law at all, even one waiting in the wings, is something I salute Australia for, because the United States still has no national law. A few bills have gone before Congress and died, so California and the sunbelt states have moved ahead on their own. In Canada there’s no national movement whatsoever: Ontario has moved ahead, Quebec more slowly, and British Columbia has said absolutely not. So among federal nations, Australia seems to be an outlier in actually getting its act together nationally.

MS: I’ll grant that on some things we did our thinking earlier, and that’s led the space. The logic was: states already have separate transport laws, so it makes sense to do the same for automated vehicles. What’s different about AVs? This part needs to be in a national law; that part can be handled by the states later, because it’s about enforcement. Not easy, mind you: the teams worked on it for six years, and a substantial part of my time went into it. But once you can separate what’s national from what’s state, and a heavily regulated country has run that exercise across so many domains, Australia is genuinely good at it. The US isn’t quite there on what a state should own versus what the nation should. Australia has a playbook across everything. Health, whatever: they’ve done it before, argued it out, and they know where the line is. That took away half the battle.

AM: The Canadian example is inexact, because our provinces, Quebec especially, are extremely jealous of the federal government. They won’t cede anything they don’t have to; even things they arguably should cede, they won’t. That makes frameworks like this harder to agree on.

MS: And that’s actually a positive, in a way. Yes, we’re dragging a little, but when it comes out, it’ll be clear: you’ll know what the feds do and what the states do. It’s one of the things the commercial companies I speak to appreciate. They wish we’d move faster, but it’s clear who owns what. Tick all the federal boxes, and you know what that then means for the state approvals you need.

AM: You’ve been abroad for almost two decades, and now here you are, back on home turf in the USA. What prompted the shift?

MS: Fifty different things. But part of it, and this is probably what we’ll talk about, is that future mobility wasn’t where I wanted it to be inside government. I couldn’t push things quickly enough. I was enjoying the work, but I wanted to move faster across this space.

And honestly, there’s limited ability to write while in government. There were so many things where they’d say, “Megan, you can’t do anything else.” I had an adjunct position; I wasn’t allowed to attend any events. We had an automated-vehicle conference in Australia in October: they said do whatever you want, but I couldn’t get approval. And I thought, I can’t stay here going backwards, unable to say anything publicly, just idling professionally while you decide whether you want to move forward.

AM: I understand perfectly; I’ve been in that trap myself. I am sure you will not be idling for very long.

Megan Sharkey, thank you for your time.

After my conversation with Dr. Sharkey was over, I reflected on things that I had learned.

One is that NSW spent A$30 million enlarging its facility, more than doubling the length of its test track, and funding the heavy-vehicle testing Sharkey argued for. But NSW also would not approve her to attend Australia’s own automated-vehicle conference, nor to publish work, and at least partially for those reasons, Australia has lost Sharkey, and her institutional knowledge, to the United States.

Similarly, the one automated-driving product Australians can buy did not wait for the law. Tesla got to market by working out, with Sharkey’s team, where the grey zone in ‘driver control’ was, and Australia became a debut right-hand-drive market for Supervised Full Self-Driving despite the absence of a national framework. That means that Australia, which is one of the most regulation-forward countries in the Anglosphere, arrived at deployment the same way that less regulated ones do: bilaterally and case-by-case.

Finally, I noted that, as I mentioned to Sharkey, Waymo has incorporated in France, Germany, the Netherlands, and Iberia; is preparing to launch in the UK; has begun testing in Japan; and has retained lobbyists in Canada. In Australia, it has looked at office space. Clearly, in Waymo’s own ranking, Australia is not near the top; the government’s lackadaisical approach to regulating the sector has surely contributed to that assessment.

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These items point at the same lesson for governments everywhere. It is that refusing to make decisions or commit to a posture doesn’t mean nothing happens. It means instead that things happen, but in other places, and without public input. As driving automation becomes better and more widespread, governments everywhere had better act, and act better, if they want to seize the benefits on their terms, rather than be left behind.

I approve of this move. Admitting one’s mistakes is a difficult virtue to practice, but necessary.

Let the analysis below stand as a suggestion of why retraction was the right move.

It’s just after 6 p.m. on 19 January 2025 in San Francisco. An empty Waymo is stopped at a red light at the intersection of Sixth and Harrison, waiting its turn in a line with other cars. A human, driving a Tesla, approaches the intersection at 98 miles per hour and plows into the line of stopped cars, striking a car and killing one of its occupants.1 That struck car, in turn, bumps a Waymo.

Eighteen months later, that incident forms one-third of the statistical case, now circulating in New York’s press, that Waymos are more dangerous than the city’s professional ride-hail drivers.

The case comes from a new report by the Sam Schwartz Transportation Research Program at Hunter College and Open Plans: How Would For-Hire Autonomous Vehicles Impact NYC? (hereafter ‘Impact NYC’). The report asks how robotaxis (“for-hire autonomous vehicles”) would affect New York City. Its conclusion, which made headlines, is that Waymo’s rate of crashes causing death or serious injury is 1.5 times that of New York’s ride-hail drivers. To quote Impact NYC directly:

Initial data shows that Waymo vehicles are involved in fewer injury-causing crashes overall, but have a significantly higher rate of crashes with a serious injury or fatality than the existing New York City for-hire fleet.

You can imagine the coverage that has followed. “Robotaxis in NYC could cause more deaths and serious injuries than the for-hire vehicles they’d replace,” ran one headline.

It’s a remarkable finding because it’s so counter-intuitive. Uber drivers are better in aggregate than Waymo’s famously-safe fleet? That’s what the report claims.

But of course that claim is so counter-intuitive because it’s wrong.

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Are data centres bad? Should Ontarians prevent them from being built here?

The question is pressing. In Mississauga, Prologis has filed the city’s first application for a hyperscale data centre; if you click through that link, you’ll find the headline of the newspaper report opens with “Shut It Down”. On 29 July, the week following original publication of this issue of Changing Lanes, City Council will vote on whether to impose a one-year pause on new data-centre applications, with at least half of councillors reportedly onside. In Hamilton, a proposal for the former Stelco lands moved City Council there to consider a moratorium on new data centres. That would have been a first for a Canadian city, had it passed. It did not, with Council voting it down on 15 July, 10 to 6. That same week, just to Ontario’s south, New York became the first American state to freeze new hyperscale permits altogether. Everyone is deciding at once, but there’s no emerging consensus.

At least some of my friends and family have already made up their minds. They’ve decided that data centres are bad neighbours. They take in too much water, using up some and polluting the rest. They jack up electricity bills (what Ontarians call hydro), while employing almost nobody. Some people I like and some I love sincerely hold this view, as do many people I don’t know. When Abacus Data, the polling firm, asked Canadians whether they would welcome a data centre in their own community, only 16 per cent said yes.¹

I’ve read my Andy Masley (constant readers of Changing Lanes will remember his framework from “Robotaxis Are the New Data Centres”), so I had my priors that data centres were a mitigated good. Still, this is happening where I live, and people I respect have opposite intuitions. So I have set out to answer the question: who is right? What should a reasonable person in Ontario, watching the AI build-out arrive on the Lake Ontario shoreline, want to happen?

Normally in my Changing Lanes pieces I give the verdict up front, but this time I’m going to resist. I want to take the objections seriously and follow the evidence where I think it leads. I hope that by following along, you’ll see the conclusion is reached honestly, and therefore take it seriously.

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Begin with a definition of what we’re talking about. A data centre is a warehouse of computers; indeed, it’s reasonable to think of it as a giant computer, just like a desktop machine, but at the scale of a building rather than a tower case. It has rack upon rack of servers. These store data and run computations, providing support to everything that happens ‘in the cloud’. Historically that has meant providing the engine that runs Internet powerhouses like Gmail or Microsoft Office 365, but also any humble firm offering a mobile app that uses remote data. By some counts there are as many as 108 data centres operating along Lake Ontario. Things are changing, though: the AI boom needs vast amounts of the ‘compute’ that data centres provide, meaning that they are a bigger business than ever. Accordingly, Microsoft is expanding its footprint in Vaughan and across the GTA, and more centres from more firms will follow.

High Performance Computing Center Stuttgart HLRS 2015 07 Cray XC40 Hazel Hen IO.jpg by Julian Herzog is licensed under CC BY 4.0.

Once built, a data centre needs two things. It needs abundant electricity, ideally sustainably generated, because the servers are always on. And it needs cooling, because nearly every watt of electricity that enters a chip leaves it as heat, and if that heat is not removed promptly, the chips fail. Cooling can be accomplished with electricity—think the fans installed inside your desktop machine—but at the data-centre scale, it’s also done with water. Ontario is unusually fortunate in these regards, with Great Lakes water, cold winters, and one of the cleanest power grids on the continent. That’s why data-centre operators are keen to build them here.

Let’s see if we should let them.

Water Fears Are Overblown…

Let’s start with water. There are two claims here: that data centres will poison the water, and that they will use it up.

Poisoning the water, or (to put it more precisely and less emotionally) contaminating it, essentially does not happen during normal operation. Some data centres cool themselves with closed-loop systems, meaning cold water is run through pipes in the building, absorbing heat, which is later released outside. The circulating water sits in a sealed loop and is reused continuously, topped up only to make good small losses. It only touches pipework, and never the servers nor the outside air. This delicate use of water is in sharp contrast to other Ontario industrial sectors, like paper or metal. Unlike a pulp mill, there is no process effluent to speak of. Unlike a mine or ore refinery, there is no tailings pond.2

Using up the water, or (to put it more precisely and less emotionally) consuming it, is a stronger charge. Many data centres, rather than using closed-loop systems, cool evaporatively. In these cases, the servers heat the air around them, and this heated air is channelled over water. That water evaporates, taking the heat with it.

Whether this is a problem depends on where that water came from. If it’s drawn from an aquifer, on any human timeframe that water is lost forever. If it’s drawn from a river, it might permanently exit the watershed; if the water is taken from the river far enough upstream, that water will never reach the downstream system. If that is practiced at scale in suboptimal places, the water loss is real, and enormous. In Mesa, Arizona, for example, a single campus holds standing permission to ramp from one million to four million gallons a day, half of it drawn from the Colorado River. That is a genuine loss, because the Colorado is a closed, badly over-allocated basin. Water evaporated off a cooling tower there leaves as vapour that rains out far downwind, never returning to the watershed in any usable quantity.

But southern Ontario lies almost entirely within the Great Lakes–St. Lawrence basin. Here, evaporative draw offers a negligible chance of watershed loss; water taken from the watershed evaporates into the air and returns, via the water cycle, to that watershed. So nothing is consumed.

Well, almost nothing. The international compact governing the lakes will formally count evaporative cooling as a loss, since some negligible vapour rains out beyond the basin. But the quantities at stake are minute against the scale of the lakes, which hold roughly a fifth of the world’s surface fresh water. No array of evaporative-cooling data centres we can realistically contemplate would amount to more than a rounding error.

That is stipulating that such an array would be constructed. That’s unlikely. Evaporative cooling is a design choice, and architects choose it for data centres to be built where the air is hot and dry. Ontario has many virtues, but a hot and dry climate is not one of them. Across autumn, winter, and spring, southern Ontario’s daily mean temperature sits well below the mark (roughly 18°C) at which outside air alone can cool a data centre. In particular, Toronto’s seasonal means run from about −2°C in winter to 8°C in spring. This means that the province can cool without water through something like three-quarters of the year. In warmer months, the lakes remain cold, and data centres take advantage. Equinix, the world’s largest data-centre operator, cools its downtown Toronto facility from Enwave’s deep-lake loop, which pulls near-freezing water off the bottom of Lake Ontario. Meanwhile, Microsoft says its Ontario sites draw municipal water for cooling only when temperatures exceed about 29°C. That happens for less than five per cent of the year.

There are many horror stories about data-centre water consumption, but they all happen in arid regions. Even there, the stories lack any sense of proportion. Maricopa County, Arizona, one of the biggest data-centre markets on the continent, expected its data centres to use about a billion gallons of water in 2025. That seems bad… until one learns that the county’s golf courses were expected to use twenty-nine billion. By the same token, if Ontario is concerned about wasting water, rather than get concerned about what data centres may do, we should look instead to the ways we use water now. Concern on that front is conspicuous by its absence.

Is there anything critical to say about water and data centres in an Ontario context? Only that our systems need good data to make good decisions. There was a mild flap when it was found that one Quebec facility had no water meter at all. We can’t audit what we don’t measure, so even though Ontario’s climate makes heavy water use unnecessary, let’s meter every facility, including those buying city water, which today’s permit rules don’t reach. Let’s publish the readings, tracking against a cap on use, which we should write into the servicing agreement. Developers have historically fought disclosure, but I would expect that they won’t fight hard in an Ontario context, because they know just how little water they will use.

…Electrical Fears Are Not

In contrast to concerns about water, concerns about hydro (which, confusingly, is how Ontarians refer to commercial electricity) are real. Data centre electrical loads can be staggering. A single centre, if large enough, can draw as much power as a mid-sized city. The hyperscale campuses now being contemplated are designed for anywhere from 300 megawatts to a full gigawatt of continuous load. For context, a gigawatt is roughly the output of one of Darlington's nuclear reactors, or enough to power some 800,000 homes.

Demand on this scale is not hypothetical. As of this month, data-centre projects totalling some 7,000 megawatts have active requests to connect to Ontario’s grid. Ontario’s electric operator, the IESO, now expects data centres to make up 8.6 per cent of Ontario demand in 2050, more than half again what it forecast a year ago.

Adding this much demand to a neighbourhood can create two problems for everyone else: it can raise the price of electricity, and it can require the addition of expensive new infrastructure. In the abstract, this might be a problem; but in Ontario, there are rules in place that mean the power costs of a data centre aren’t socialized.

Let’s start with prices. The data centre pays for what it uses, just like any other large industrial customer. Believe it or not, that can be good. A quirk of Ontario’s system, thanks to its nuclear plants delivering high baseload power, has meant that during some nights Ontario has surplus power it has had to sell at a loss. Always-on data centres can soak up that capacity, meaning the system’s fixed costs get spread across more electricity sales. But of course, during some peak periods, Ontario does not have enough power. In those hours the marginal generator is a natural-gas plant, meaning that a data centre running flat out through a July heat wave is (at one remove) burning natural gas. More gas hours mean more emissions and higher market prices, and both land on everyone.

The fix is giving us the right to interrupt the flow to big data centres if we need to. Texas and Alberta already do this, connecting them faster in exchange for curtailment during the tightest hours of the year. A data centre that we can throttle back for the fifty worst hours costs the system little; if they don’t want to be throttled, they should simply be required to pay for the peaker they make us run.

Ontario seems to be on its way to this requirement already. Bill 40, passed in December, lets the province decide which very large loads serve its economic interests and may connect to the grid, and the IESO published draft technical requirements for large computational loads in May. The pieces of a sensible large-load regime are on the table; the one that is missing is the public’s ability to throttle data centres’ electrical use if necessary.

Of course, the centres may very well choose to throttle themselves. Ontario’s power system has a bizarre feature where it computes everyone’s share of the system’s fixed costs (the ‘global adjustment’) by tracking their usage during the five heaviest hours of the year. There is a whole system of consultants that advise clients on how to anticipate those hours, throttle back use during them, and thus shift their costs onto someone else. (How do I know this? Don’t ask.) Data centres, with batteries and flexible workloads, would be excellent players of that game. That, more than anything, is why Ontario should refine the global-adjustment system, and more than that, designate a new rate class for very large consumers, to discourage this kind of game-playing.

On the question of infrastructure, there is good news: in Ontario, a data centre that requires extension of the local grid cannot just hand that bill to the public. The utility estimates what the new wires, transformers and substations will cost, compares that with the revenue it expects to collect from the customer, and can require the developer to pay the shortfall. It can also demand a deposit, retaining it if the promised demand never appears.

So if a new substation exists only to connect the data centre, the centre will largely pay for it. The weaker point is farther upstream. If the project requires reinforcement of the shared transmission network, that work may be rolled into the regulated cost of the provincial grid. Sometimes that is reasonable: a larger line may benefit an entire growing region. But if a billion-dollar upgrade is needed principally for one enormous customer, spreading the cost across everyone’s hydro bills would amount to a public subsidy.

So what we see is that there are reasonable concerns to be had about data centres and electricity. A new data centre will not automatically raise its neighbours’ bills, and Ontario protects ratepayers from much of the cost of connecting it. The danger is that enough centres might spike the cost of dirty peaking power in the near term, and in the longer term might require big investments in new generation and transmission infrastructure.

In my view, those are investments that Ontario should be making anyway. We should be building clean generation, at scale, now; and we should be doing that irrespective of how many data centres are knocking at the door.

As has pointed out, Ontario is one of the few jurisdictions in the world right now that knows how to build nuclear on time and on budget. We should be building more of it while our expertise is current (a case that Eric Lombardi made in these pages last month). At the same time, we should be supplementing with wind and solar, which are cheap and fast to add (thanks, China!). Building more capacity now is sensible; even if no data centre ever came to Ontario, electric vehicles have already arrived, and more are coming. The province’s ambitions in battery manufacturing, rare-earth processing, and other 21^st-century industry will all rest on cheap, green power. Let’s build for that, and let data centres come along for the ride.

Other Concerns Are Modest

Beyond water and electricity, the remaining objections to data centres are mild and resolvable.

Large data centres hum. They emit a continuous, low-frequency drone from thousands of cooling fans, joined intermittently by the roar of backup generators on their test runs. The hum is a by-product of active cooling, so a small or fully liquid-cooled facility can be nearly silent; the sites that draw complaints are the big, air-cooled ones. These emissions can be measured; monitors at homes near American facilities have recorded outputs of 40 to 59 decibels. In the abstract, that range runs from ‘quiet library’ to ‘a window air conditioner’. Residents near facilities in Virginia have described it as ‘a jet engine that never turns off,’ with some sealing their windows or sleeping in earplugs. Those accounts warrant some amount of skepticism, since neighbours fighting a project have every reason to dramatize, but we shouldn’t dismiss them.

Here Ontario holds a lever most American jurisdictions lack. NPC-300, the province’s environmental noise guideline, does two useful things: it sets numeric limits at the property line (40 decibels at night in rural areas), and it requires a noise study before approval. In much of the United States, neighbours discover the hum first and go looking for a remedy second, but in Ontario, the remedy can be written into the permit before construction.

It’s true that NPC-300’s limits are tuned more-or-less to the midrange frequencies that human hearing notices most (the technical term is A-weighted). That standard can undercount low-frequency tonal drone, which is harder to notice consciously but can be subconsciously unsettling. Fortunately, Ontario’s noise rules already penalize tonal sound, adding five decibels to the measured level of a steady hum. The real gap is procedural: smaller facilities can self-register without a full study. For a hyperscale site, the province should insist on the full noise assessment for each one.

Another complaint we often hear is that for all their costs, data centres contribute nothing to the communities that host them. That’s true, I suppose, if we count contributions as jobs. A large campus needs few people, on the order of 20 to 30 permanent staff per 100 megawatts of capacity. While it takes thousands of jobs to construct them, maintaining them doesn’t require very many people.

But if we count contributions as property tax, it’s a different story. Exhibit A here is Loudoun County, Virginia, the densest data-centre cluster on earth, where the industry supplies 38 per cent of the county’s general fund from four per cent of its commercial land. Sadly that example is not applicable to Ontario. Loudoun taxes the servers themselves, reassessed as the equipment refreshes. Loudoun levies a business tangible personal property tax on the computer equipment itself, and a hyperscale building refreshes tens of thousands of servers on a rolling cycle, each round taxable. Ontario municipalities don’t do this. Instead they tax the land and the building, and the building is, for assessment purposes, a warehouse. Still, while no fountain of cash like in Virginia, data centres do employ people and pay taxes, meaning a city is better off having them than not.

So a data centre is a quiet, low-traffic, low-employment industrial site. That’s faint praise, but it has an upside, which is that the small scope of gain means that Ontario communities have standing to bargain hard on what they get out of permitting them.

What a Reasonable Ontarian Should Want

But if the gains are so modest, why permit them at all?

Part of it is that refusing permission doesn’t mean they’re not built, it just means they’re built elsewhere. Projects turned away in the United States have flowed toward Texas and Arizona: thirstier places, on dirtier grids. So the blocking places remain free of them, at a net cost to the environment. Similar dynamics are playing out in Canada: Quebec, rich in water and clean power, has capped new data-centre power as a matter of policy, and British Columbia has done much the same. Alberta, with the dirtiest grid of the three and constant stress on its water supply, is where the overwhelming majority of Canada’s proposed capacity has piled up, some of it as off-grid gas megaprojects. Refusal in a clean-grid province is, in practice, an invitation to a dirty-grid one.

Another part, strange to report, is the company that refusal keeps. The New York Times reported this month that state-linked accounts from China, Russia, and Iran have been flooding social media with AI-generated slop warning that data centres will drain the water and wreck the grid. American officials read the campaign as an effort to fan public opposition and slow the West’s AI build-out. That does not make my worried friends and neighbours into foreign dupes, since the West’s enemies are inflaming sincere grievances rather than inventing them, and it is those sincere grievances I’m trying to address here. But when our adversaries spend covert effort discouraging us from building something, they are telling us that they think it’s valuable.3

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In my view, Ontario should host as many data centres as its clean supply can carry, because the objections are weak. Water is immaterial, noise can be regulated easily, and power capacity is something we should be building anyway.

What should a reasonable Ontarian seek in return for permitting data centres to be built here? Three things:

1. Meter the water, publish the readings, and cap the draw in the permit.

2. Require a full, tonal-aware noise study and provincial approval, including penalties for exceeding noise limits, in advance.

3. Make developers pay for their own connections, including a fair share of upstream transmission, give very large loads their own rate class, and connect the biggest loads on interruptible terms.

Meanwhile, Ontario should be building clean power generation: renewables immediately, the nuclear Ontario has shown it can deliver, and the transmission lines to carry it where we need it. Those are unambiguous goods even if no data centre ever connects to them.

The citizen’s answer to a data centre shouldn’t be no. It should be yes, with conditions.

On 16 July I'm joining the Parking Reform Network to discuss what cities should want from automated vehicles. On 21 July I'll be at the 2026 Toronto Robotics Conference, on a panel asking whether robotaxis really are safer than humans. And on 23 July I will host Aurora's Chief Safety Officer, Nat Beuse, on the next Changing Lanes Live, for a discussion of freight automation and safety.

I hope to see readers at all of these events! Please follow the links to register.

How safe is safe enough? If we’re going to regulate driving automation, we must have an answer to this question.

Let’s put it simply. Choose a metric for road safety: candidates might be ‘collisions with another vehicle’, ‘collisions with a pedestrian or cyclist’, ‘collision with anything at all’, ‘incident causing death’, ‘incident causing serious injury’, or ‘incident of any sort that required a police report’. For a given period, an automated-driving system (ADS) will be involved with a certain number of such collisions or incidents.

The question is: what is the target number, such that, if an ADS falls below that target, we can consider it safe to deploy; and if it falls above, we do not?

A lot is at stake. If we pick a target that’s too high, the ADS will cause incidents that wouldn’t have happened otherwise. But if we pick one that’s too low, human drivers will cause incidents that an ADS would have avoided.

In June 2026, Families for Safe Streets (FSS) offered their own answer to the question. FSS is a national advocacy organization founded in 2014 by people whose loved ones were killed or seriously injured in traffic incidents. FSS’s answer was released as Safety First, Always: An FSS Framework for Autonomous Vehicles. At its centre sits a proposed certification standard the paper calls the Gain and Guard Rule: before full-scale commercial deployment, automated vehicles must be at least ten times safer than human drivers on average across everywhere they operate, and never worse than human drivers in any single operating environment.

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In the spirit of disclosure: I contributed time and expertise to this framework as an outside contributor. Consequently, my name appears in the acknowledgements, and the FSS website hosts my explicit endorsement. As you read the following, please be aware that this is a review of work that I had a hand in, and that I want to succeed. Against this, the framework itself notes that acknowledgement as a contributor “recognizes contribution, but not necessarily endorsement of every statement or position”.

In that spirit, I will reveal my cards. I regard Safety First, Always as a serious effort to give regulators a public, measurable standard, and the Gain and Guard Rule is the kind of bar we need.

Having said that, I have two reservations.

Firstly, the framework’s moral core, the Guard prong, has a measurement problem the framework doesn’t solve… but fortunately, in my view the framework already contains the seed of a solution. Secondly, its liability proposal correctly borrows from the aviation sector, but doesn’t borrow enough.

Neither concern displaces my enthusiasm for what FSS has done here. I offer both reservations, and the first one especially, to help make the framework’s strongest idea enforceable.

A High Bar, but Not Too High

The rule’s plain-language version is that wherever it operates, an ADS must be at least ten times safer than human drivers on average wherever it operates (i.e., its operational design domains), and never worse than human drivers in any single area.

An operational design domain (ODD) is the set of conditions a system was built and is authorized to handle. It can consist of a defined geography, certain road types, certain weather, or certain hours. The easiest possible ODD might be “on a pre-mapped highway, during the day, with no clouds”. The hardest one would be “everywhere”.¹

The Gain prong demands a tenfold reduction in fatality, injury, and crash rates (as an exposure-weighted average) across all a system’s authorized ODDs. The Guard prong forbids performance within any single ODD that is worse than human driving.

The point of the Gain prong is to demand order-of-magnitude improvement overall, while the point of the Guard prong is to prevent ‘gaming’ of that demand. In the latter’s absence, an unscrupulous firm might point to their strong performance on average while concealing dangerous performance in particular conditions, or neighbourhoods.

To see why this matters, consider the benchmark it replaces.

The dominant standard in automated vehicle (AV) governance, embedded in UNECE working documents and Britain’s Automated Vehicles Act 2024, and echoed in the academic and industry benchmark models cited in American AV safety cases, is that an ADS should drive as safely as a “competent and careful” human. That sounds reasonable until you remember what human driving produces: in 2023, that was more than 40,000 deaths and roughly 2.4 million injuries on American roads. As Prof. Bryant Walker Smith, my guest on a recent episode of Changing Lanes Live, puts it: I am concerned about automated driving, but I’m terrified of human driving.

The first question to ask about this is whether asking for 10x improvement over a human standard is reasonable. Perhaps that’s expecting too much? To judge by the performance of the industry leader, it is by no means expecting too much: at time of writing, Waymo’s safety data hub reports, across more than 220 million driverless miles, 94% fewer serious-injury crashes and 82% fewer injury-causing crashes than human benchmarks in the same cities. A peer-reviewed study with the reinsurer Swiss Re found comparable reductions in bodily-injury liability claims. And Waymo is performing so well in conditions that, far from being cherry-picked, are among the most difficult: dense, high-interaction city streets, full of pedestrians, cyclists, and unpredictable human drivers.

It’s worth reflecting on the fact that Swiss Re was Waymo’s partner in this exercise. It’s a reinsurer, a firm whose whole business is pricing risk with its own capital. As such, it has ‘skin in the game’ when it assesses those safety numbers. Put more formally, its assent is a costly signal that the claims are trustworthy. That’s why I’m more sanguine about these figures than other researchers, or Safety First, Always seems to be, with its calls for independent verification. Let’s set the disagreement aside, and stipulate for now that Waymo’s safety record is true.

If so, it means that FSS has set its floor at a level that at least one company in the sector has already reached. If Waymo’s numbers are real, the Gain prong costs Waymo nothing except the freedom to backslide, and it requires Waymo’s competitors to clear the same bar. If they can’t, that means the standard is working as intended. When FSS asked me to comment on the framework, this is one point that I made: regulators will know they have the right bar if they can offer one which some systems clear, but others can’t.

The strongest objection to the ten-fold threshold is that it asks too much. An academic framing of this claim comes from RAND; in 2017, researchers at that firm modelled deployment policies and found that waiting for nearly perfect AVs costs lives. Putting vehicles on the road when they are merely somewhat better than humans saves more people over thirty years than holding out for 75% or 90% improvements. This is a formal phrasing of the plain-language argument I often hear from intelligent laypeople: once driving automation is even a hair better on average than a human driver, the smart play is to move immediately to full deployment, because otherwise you are losing lives on net.

Safety First, Always has an answer to this, which is a staged certification pathway. As per this proposal, systems that don’t yet reach the 10x level may still operate in public, but must do so through supervised stages, with example thresholds of 2X and 4X as permissible for pilot projects. The full Gain requirement is reserved for full-scale commercial deployment. There are still opportunities for lives to be saved on net, just in bounded circumstances, and AV firms are still able to practice in real environments, to improve and gain the opportunity to reach the 10X level.

This is also Safety First, Always’s reply to the worry that a floor that only the incumbent clears is a moat, using early gains in safety to lock in a de facto monopoly. The 10x floor governs full commercial scale, while the supervised 2X and 4X stages keep a path open for challengers, and competition.

The Guard Prong’s Problem

The Guard prong is the framework’s moral core. It exists so that a company cannot buy a glowing citywide average with excellent freeway performance, while at the same time offering something worse than the status quo to some subset: a particular district, or time of day, or group of people (say, people in wheelchairs). That concern is valid, and I have written, on several occasions, about it; this behaviour is one that even Waymo has seemed to engage in.

Safety First, Always seeks to solve the problem, while adding a requirement I haven’t seen stated so directly elsewhere: demonstrated detection parity across skin tones, ages, body types, and mobility devices, treated as a condition of certification rather than an aspiration, and grounded in documented disparities in pedestrian-detection research.

I acknowledge that this is an important bar we must insist be cleared. That’s why I am surprised that, in this context, the framework does not make the obvious move of saying that AVs must, as I once argued in Asterisk, use multiple sensor types rather than cameras only. That idea is gestured at here, but only as a fail-safe design principle (to prevent one sensor malfunctioning from leaving the vehicle ‘blind’) rather than a way to ensure all vulnerable road users are protected. I would have placed the multi-sensor rule in the vulnerable-road-user argument, and not merely the redundancy one.

Quibbles aside, my concern is that the Guard prong is easier to state than to implement.

That’s because fatal crashes are rare events. Yes, 40,000 Americans die on the roads (on average) each year, but that number must be put in the context of just how much Americans drive: American roads produce roughly one death per hundred million vehicle-miles. This means that, to be confident that an ADS is safer than average requires it to have been tested for many hundreds of millions of miles, into the billions.

The Gain prong survives this because it aggregates; it takes exposure-weighted averages across all ODDs, measured on the more frequent currencies of injuries and crashes as well as deaths. Waymo’s record to date excels on this requirement. The Guard prong, by design, disaggregates, looking into smaller and finer individual operating domains. Waymo’s record is no answer here. As an aggregate, exposure-weighted claim on the frequent currencies of injuries and crashes, the data does not offer a per-ODD fatality claim.

The problem, as I see it is that the smaller the dataset, the more chance plays into the matter: given a small enough set, one cannot confidently distinguish a genuinely worse-than-human ADS from one that was merely unlucky. That means that the Guard rule could take us into a bad equilibrium, where one incident in a small domain disqualifies the entire ADS everywhere. The equal-but-opposite failure would be to dismiss any such incident in a narrow ODD as noise rather than signal. Preventing these bad equilibria depends on defining the ODDs just right. The framework calls for regulators to shoulder this responsibility, and I don’t envy them the job.

Fortunately, the task need not be improvised fifty times over. Defining ODDs is exactly the kind of scarce technical work the framework’s own proposed interstate compacts exist to pool: the first product of such a compact should be a shared, pre-registered ODD taxonomy, with agreed minimum sample sizes before any single domain’s record is treated as signal rather than noise. Standardize the denominator once, centrally, and the Guard prong stops depending on each jurisdiction guessing right on its own.

Conversely, the regulator tasked with implementing Safety First, Always is gifted with a strong enforcement framework, the ‘Lawful AV’: a tiered system of regulatory triggers, review, then suspension, then revocation, for persistent unlawful behaviour, even when no crash results (think passing stopped school buses or blocking crosswalks). Safety First, Always takes the view that safety should not just be measured in death or injuries, but in behaviours that tend to promote these things. That’s table stakes in aviation: airlines can face mandatory reporting, review, and suspension or revocation of license to operate for regulatory violations, even those where there’s no incident. The framework argues that AVs should be regulated the same way.

I’m all for this, because this rule is empirical: it counts observable events against defined thresholds, rather than estimating rare-event rates. I think that this is the repair the Guard prong needs. As written, the Guard prong asks whether an ADS is worse than a human at causing death within a single ODD, which our data is insufficiently detailed to answer. Worse, the question gets harder as AVs get better: the safer the vehicles, the rarer the fatalities, and the larger the sample you would need to prove anything at all. The way out is to put the Guard prong on the same footing as the Lawful AV rule, i.e., to judge each domain on the metrics of injuries, crashes, insurance claims, and unlawful-but-harmless behaviours.

One might object that the Guard prong is left un-operational on purpose. FSS is a coalition of the bereaved, and no one can expect it to offer a document that translates ‘never worse’ into an acceptable per-neighbourhood death rate. If that is their view, I am entirely sympathetic. But a regulator is entitled to reply that a target no one can measure is a target that protects no one. Giving the prong something it can count is what will give the moral argument teeth.

If We’re Going to Copy the Aviation Sector, Let’s Go All the Way

Safety First, Always also looks to aviation regulation to help solve the most vexed problem in the AV space, namely liability.

FSS members know all too well that today’s road-incident victims routinely spend years in fault litigation before receiving compensation. That’s bad enough, but the advent of vehicle automation is poised to make it worse by multiplying the defendants, from manufacturer only to a host of suppliers (sensors and mapping most obviously, but also fleet operators, chips, wireless Internet, and on and on.) The paper’s answer is a two-tier structure with an honourable lineage running through the Montreal Convention and Wansley’s The End of Accidents: automatic strict liability up to a defined threshold, no proof of fault required, with presumed but rebuttable company liability above it.

This is the right position, one that I have advocated for myself. As Safety First, Always puts it, people harmed by AVs should not face higher barriers to compensation than airline passengers, where liability is predictable and capped. This approach is preferable to endless litigation, which is bad for victims, and to ‘jury roulette’, which is bad for firms. There is even a plausible legislative bargain to get there (though the framework doesn’t spell it out): strict AV firm liability, traded against some form of certainty on damages that can be claimed.

FSS proposes a pooled industry-compensation fund, financed by the full range of AV companies that would pay victims immediately while the companies sort out cost allocation afterward. While Safety First, Always argues this is aviation-inspired, I think they are mischaracterizing it: in that sector, liability is carrier-by-carrier, backed by mandatory insurance, rather than mutualized across the industry. There’s good reason for this: unless contributions are sharply risk-rated, the safest operator ends up subsidizing the crashes of the worst one. In the AV industry today, there is quite a spread between the most- and least-safe operators (I’ll forbear from naming names); a market-share-financed pool is an invitation to moral hazard.2^[2] As such, the proposal sets itself up to receive the stiffest opposition from the safest operators, which would be a perverse outcome.

Although it doesn’t say it, I suppose that the framework is drawing upon a different industry when thinking about risk, namely nuclear. The Price-Anderson Act has run the American nuclear industry on this model for seventy years: a mandatory, industry-wide pool that pays victims first and allocates fault later. That regime was put in place because no single operator could carry the tail risks of a high-consequence, immature technology alone. Unfortunately, nuclear operators are judged against a risk-rated formula, into a small, homogeneous, heavily-inspected industry. The AV field is none of those things.

There is also the question of feasibility, and of venue. A statutory compensation fund requires legislation, and the paper concedes liability rules “may ultimately need to be federally established,” which is awkward at a time that, as I pointed out in Reason, Congress is failing to agree on AV oversight, and the regulator is withdrawing voluntary safety oversight programs. The nearer-term market is the states, which are already writing AV law. To its credit, the framework knows it: everything in it is framed as a floor adoptable at any level, with interstate compacts proposed to pool scarce technical capacity.

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How safe is safe enough? Safety First, Always offers an answer: ten times safer everywhere you operate, never worse anywhere. That is a concrete standard, one with real moral weight, as it’s offered by the people who have already suffered from the frailty of human driving. The real test of the framework is whether a single jurisdiction adopts it whole: Gain, Guard, and Lawful AV together. In that regard, I note that Transport Canada issues non-binding safety guidance and sets vehicle standards, while provinces control who may operate on their roads. Ontario might adopt the Gain and Guard Rule and the Lawful AV framework without waiting for Ottawa, and I would like to see it do so.

Respect to for comments on an earlier draft, and to the FSS team for letting me review the framework before publication.

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Autonomous Vehicles & Parking Reform

Thursday, 16 July, 1500h ET

One week from today (at time of writing), I’m joining a panel hosted by the Parking Reform Network (PRN) to ask what urbanists should want out of automated vehicles, and how to move from ‘hope’ to ‘a plan’.

Hosted by the PRN’s Tony Jordan, I’ll join Dr. Miriam Pinski of UCLA’s Shared-Use Mobility Center and Dr. Rachel Weinberger of the Regional Plan Association. The three of us are united in our hopes for better cities, but may be divided in our views on how to get there. Expect a spirited conversation that talks through the policies that would let cities benefit from an AV future, and the consequences if we do nothing.

It’s an online webinar on Zoom, and free to attend. Please register here.

Changing Lanes Live, with Nat Beuse, CSO of Aurora

Thursday, 23 July, 1200h ET

The following week, my guest on the third episode of Changing Lanes Live will be Nat Beuse, Chief Safety Officer of Aurora, the world’s leading driverless-trucking company.

Nat Beuse, image courtesy of Aurora

Few people have seen automated-vehicle safety from as many angles. Beuse spent nearly two decades at NHTSA, where he oversaw the US government’s work on automated driving, then led the safety team at Uber’s self-driving unit, before joining Aurora. We’ll talk about the challenges that face self-driving heavy haulers; the extent to which Aurora’s problems resembles those of robotaxi companies, and the unique challenges the firm faces; the foundation of the firm’s sterling safety record; and how the world will change if Aurora’s vision comes true.

Subscribers to Changing Lanes will be notified when we go live, but please do sign up in advance here.

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Few people have seen automated-vehicle safety from as many sides of the table as Beuse: he spent nearly two decades as an executive at NHTSA, where he oversaw the US government's work on automated driving, then led led the safety team at Uber's self-driving unit, before joining Aurora. We'll talk about how a company proves that a truck with nobody aboard belongs on a public highway, and who should get to decide. Subscribers will be notified when we go live; you may also sign up here.

Everyone who talks about automated vehicles (AVs) eventually brings up the elevator.

Once upon a time, elevators used to have operators. In time, the technology to automate the job developed, but riders were too nervous to ride alone, so the job persisted. Then, the story goes, a strike in 1945 forced the issue, and the operators disappeared. The moral for automated driving is that even when the technology is ready, public fear will hold it back. To normalize that technology, all that it takes is to address that fear.

It’s a good story, and it’s one I’ve told myself. That’s why I’m chagrined to learn that, having investigated it more closely, it’s not quite true.

The accurate history of the elevator operator is that there was indeed a twenty-five-year gap between the first elevator that could run itself and the first office tower that did, but it wasn’t a strike that closed that gap. Four ordinary forces had to converge…

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This is a story I have been telling in Changing Lanes since its inception, and now, courtesy of Prof. Benjamin Shiller, we can attach some numbers to it.

Shiller, an associate professor of economics at Brandeis, has recently published a paper, co-written with Imke Reimers of Cornell: Will New Driving Technologies Change the Value of Public Transportation Investments? In it, the authors estimate how residents trade off price, time, and the discomfort of travel, then simulate a Greater Boston in which private self-driving cars are widely owned. The paper puts firm numbers on intuitions I have been pressing for years: that automation will induce more driving, push development outward, and wear away at transit ridership.

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To learn more, I spoke with Shiller on 16 June 2026. The transcript of our talk has been lightly edited for clarity.

Prof. Benjamin Shiller (provided photo)

Modelling a City of Self-Driving Cars

Andrew Miller: I went through some of your work, and I hope you don’t object to this, but I wouldn’t characterize you as an economist of mobility. The through-line in your work seems to be this: once upon a time there were markets, but they had a very fuzzy sense of who the consumers were. That’s all changed. Markets now are adjusting to a world where counterparties can have unprecedented understanding of who their consumers are, and how that changes the easy assumptions we’ve made. Tell me if that’s a fair characterization… and if it is, what are you doing with self-driving cars in Boston? How does that fit into your larger intellectual corpus?

Prof. Benjamin Shiller: I view my interests as somewhat broader: it’s generally how technological change will impact markets, and so much of my research focuses on more information being collected about consumers. In fact, it was based somewhat on this local law…

AM: When you say local, you’re at Brandeis, so you mean Boston?

PBS: Well, it was across all the towns in Massachusetts: if you were near public transit, you had to increase the amount of housing, or at least increase zoning, so that they could build more housing.

But I started thinking: do we really want to put all of this housing right by the public transit stops if self-driving cars are going to be very common in the future? In Massachusetts, there have been proposals to spend as much as $55 billion on public transit, which is about $8,000 per Massachusetts resident. And I think that makes a lot of sense if you’re anticipating that the life of these improvements is quite long. But if self-driving cars become common in five or ten years and people no longer want to take public transit, then maybe it’s a poor choice. It’s an empirical question.

AM: I certainly have strongly held takes on this, but for now let’s keep the focus on the paper. So you modelled what would happen when self-driving cars arrive in Boston at scale. And before we get into the conclusions, tell me about your methodology. How did you go about making the estimations?

PBS: It’s a challenge, because we don’t have widespread self-driving vehicle adoption, so you can’t use the standard methods from reduced-form economics. The way I did it is with a method common in industrial organization, where I estimated people’s preferences for particular characteristics of transportation.

I looked at how much people dislike a minute spent sitting passively on a commuter rail line, or in the future a self-driving car; how much they dislike a minute spent actively driving; how much they dislike having to transfer on public transit, and how much they dislike price. Once I’d estimated those, then I could create this new option, the self-driving vehicle, which would have the advantage of allowing you to sit there passively like you now can on a train, but could utilize the road network.

One complication that I had to account for in these counterfactual simulations is that if there are more vehicles on the road, that could increase traffic. And I know there’s research out there that suggests self-driving vehicles might use roads more efficiently, but I took the stance that they’re going to be like us and they’re going to increase congestion. And so I estimated, within sample, this shape of how congestion impacts the travel times in various concentric circles around Boston. I could then incorporate how an increase in road usage would slow down travel times. Thus, when I calculate a new equilibrium with self-driving vehicles, I can, and do, account for both the lower disutility of time spent driving and the increase in travel time due to congestion.

AM: I have always argued that self-driving is going to increase congestion. That argument rests on the Jevons Paradox, that if you make it less mentally taxing to drive, what you’ve done is reduced its cost, and if you make something less costly, people consume more of it, which means more driving and more congestion. It’s good to have a model that demonstrates this, rather than just an intuition.

PBS: I agree. Very recently I got Gemini in my car and I found myself much more willing to drive, because I can just have long conversations. I can ask it to look up e-mails, stuff on my calendar… driving is much less of a pain now. I was assuming that the disutility of time would be equivalent in a self-driving vehicle and on the commuter rail or on the subway, but I now think that it will be less painful to spend time in your own vehicle. You could do all sorts of things that you couldn’t necessarily do on a train, like have private conversations, maybe even shave or something like that.

AM: Well, for me, the smoking gun is that right now a Waymo trip in San Francisco costs more than a ride-hail trip, but people still take it. Part of that, I suppose, is luxury signalling. But the other part of it is that in conventional ride-hail, the presence of even one stranger, the driver, one who is guaranteed not to talk to you—which is not the case on a commuter rail—even this best-case scenario is still inhibiting in some ways.

In your own car, that’s set at the temperature you like, with seats that don’t have stains or crud on them, where you can listen to whatever you want… it is absolutely going to be much more comfortable. If I was constructing such a model, I would say that travel time in your personal vehicle is going to be more attractive than even a convenient ride on public transit.

PBS: Yeah.

What the Model Found

AM: That’s the methodology of it. And what were the findings? What’s the headline conclusion?

PBS: There were several findings, some I found obvious, and others surprising.

First, the basic finding: there was more congestion, as people drove a little bit more, but also moved further away from where they worked. I have different estimates from slightly different models, slightly different assumptions, but we’re talking about a 30%-to-40% increase in mileage. And I should note that this was just commuting to work, without accounting for midday trips or increases in delivery services. With self-driving trucks, it becomes cheaper to ship items, so we’d probably expect people to ship them even more.

The impacts on public transit were more surprising. I found a modest decline in public transit revenues and usage, a little over 10%. Why wasn’t there much of an impact? It seems to be congestion: as the benefits of driving increase—or the disutility decreases, when it’s not as painful to spend a minute sitting there passively—it nonetheless takes longer, so you’re spending at a lower disutility per minute, but nonetheless spending more time.

Interestingly, I also found that with even small improvements in public transit, like increasing the speed just a little bit, you could completely offset the reduction in revenue that was attributed to self-driving vehicles. So it was very easy to get usage and revenues back up to where they would be, if you only had modest improvements in public transit.

AM: Driving automation and its effects on transit is a complicated matter. In my view, the first thing to disambiguate is what you’re talking about when you say transit. Are you talking about surface buses, or are you talking about higher-order transit: subways, and commuter rail, and LRT?

PBS: That’s a great question. So in the research, I had public transit as its own category, and I didn’t distinguish between the different ones, but I was thinking about commuter rail and subway in particular. The commuter rail actually has a high price, and it caters largely to people with relatively high incomes. These, and to some extent the subway lines, where they’re not sharing the roads and having to struggle with increased congestion.

AM: There’s also the fact that the demographics of the usage and the periphery-to-central-business-district that’s being connected, you get fewer people using it for non-travel purposes, who are often bad actors. But yes, the key thing is that it has its own right-of-way. And what that means is that, if it’s a well-run system, travel time is faster than a car of any sort, and reliably so.

The Fleet Gap

AM: In your paper, you have modelled self-driving cars that are privately held. The model starts with the world as we have it, but then assumes that every car (to use my favourite metaphor) has a metaphorical ‘drive for me’ button on the dash. But that’s the only change: everyone still owns private cars, and uses them in the same way, as they do today. And the paper then considers how this new functionality changes our behaviour and what the emergent properties of the system become. That’s very much worth exploring.

But if you look at self-driving as we have it now and where it’s headed, it’s a different model, where most self-driving vehicles are robotaxis. So my book, or rather the one that I co-wrote with my two esteemed co-authors, was about two scenarios of the future: one, the one you’ve described, where everyone has a private self-driving car, versus one where everyone has access to a self-driving robotaxi fleet that’s cheap and reliable and convenient.

PBS: You’re saying the world where everyone owns their own vehicle is more likely?

AM: We would love to see a world where Waymo and its competitors are so ubiquitous that people drop the number of cars they own and just rely on robotaxis. But we think the path we’re on, absent severe policy intervention, is more like the world that I think your paper is describing, where private manufacturers just introduce self-driving capability and nothing else changes. That is what you are describing in your paper, which is why I think it’s good that you are modelling it. But I wonder: have you thought about the fleet model, and what implications it has, and if you did that model, how would it be different?

PBS: I’m glad that we think alike, especially since you have thought very deeply on this subject. Many people that I encounter argue that I should have taken the fleet model as the subject. And I did consider it. There are some challenges there; it’s harder to simulate the future— you must make assumptions about things like prices per ride and behavioural economics. But in the end, I agree with you, I think private ownership is more likely. I think that there are advantages to it, and that there are some real problems with the fleet model.

AM: That’s why I wanted to talk to you. I don’t need to talk only to people who agree with me—how boring would that be? Lay it on me.

PBS: There are big advantages to owning your own personal self-driving vehicle. One is just logistical: if you don’t live right in the urban core—so I’m thinking more like the suburbs—there’s a wait every time you want to catch a ride. Today, if I get an Uber, it’s not that long of a wait, but it’s still a hassle that I don’t necessarily want to deal with.

Additionally there’s some convenience to having your own vehicle. I have kids; I can leave the car seats in there. Maybe I want to keep certain things in it, my sunglasses, or soccer equipment if I’m a soccer coach; there’s an advantage of having it as a storage space too.

Artist’s conception

People also have a difficulty in parting with money. If you have a ride-hailing service or a fleet model, every time you catch a ride you would typically pay some cost. That’s the only way it would really work, otherwise the marginal cost is zero and people would overuse it, and that could be problematic when there’s a limited number of vehicles. So if every time you have to go through this mentally painful process of parting with money—because it’s very salient, when you open the app it has a price right there—I think people are going to be reluctant to do so. Whereas if you own your own vehicle, it might be more expensive overall, but when you take a trip you’re not really thinking of the price.

Another challenge is that the fleet model really struggles with surges and declines in demand. Now, if we think about self-driving vehicles, they could operate like Uber and bring more onto the road when there’s an increase in demand, but there’s this problem that the self-driving vehicles are largely built with a single purpose—a high fixed cost to create them. For Waymo, maybe it’s like $150,000, something like that. And so they’re incurring this large upfront cost, and if they’re only going to use it an hour a day or two hours a day during rush hour, it doesn’t necessarily make economic sense. So I don’t think they would necessarily want to provide a large enough fleet to provide all the rides needed during times of peak demand. And so then you have the situation where, if you don’t own your self-driving vehicle, either you’re paying through the nose when you actually want to be taking the trip, or you’re just not getting a ride when you really want it.

I think that’s a fundamental problem with the fleet model.

AM: Yeah, I agree. I wrote a whole piece, There Will Still Be Private Cars in the Future, drawing upon the book, and it makes those same arguments, that people will want them. The question is how many people are going to switch to the fleet model. Imagine I’m the CEO of Waymo or Zoox, I have to ask myself, do I size my fleet to the peak, or do I size it to the steady state? You’ve described very well: if you size it to the peak, that means you’ve got an awful lot of stranded assets at every time of day that isn’t the peak, whereas if you size for regular demand, your customers can’t use you during the peak when they really want it, and they’re mad at you. It is a tough problem.

Someone—not me, I think it was Reilly Brennan—has suggested that robotaxi fleets are sized to base demand, and human drivers jump in to handle that surge in peak periods, and when it’s done they take those cars off the road, because off-peak they can’t compete against the robotaxis. I think that is most likely.

How Transit Survives

AM: You’ve got some ideas about how transit is going to survive in this future. What do you think transit will need to do?

PBS: I think it needs to get more attractive in a number of ways. So, for example, in Boston, it needs to run more frequently. It would be nice if you could have a little more privacy, or have some ways to avoid disorder and other negative aspects. Economists often think about how you increase usage of something, and usually that’s reducing the price. So I tend to favour subsidizing some of these public transit lines, or even making them free.

If you do that, how do you keep people from misusing public transit and behaving badly on it? Maybe requiring a credit card to swipe in; perhaps you could track people and see if they’re behaving badly and revoke access. So I think a lower price, more convenient service, and then some way to handle the bad behaviour could make it so that the commuter rail, or the transit network in general, is something that can survive and still remain attractive in a world with self-driving vehicles.

AM: So I agree with most of what you’ve suggested. We should have lower fares, though I wouldn’t say zero; we should have more service. Anything you do to encourage people to use it not only makes the experience better, but it also builds a political constituency, so politicians want to fund it more.

Swiping a credit card is maybe not necessarily the first step. The first step is to have fare gates that you can’t hop over. Just getting fare-beaters out of the system—requiring a nominal fee to get in—it’s amazing how many bad actors that screens out. We had two natural experiments. One was COVID, where suddenly, the good actors had no reason to travel, and it was just bad actors on the subway, plus essential workers—and the essential workers then started leaving, because they couldn’t take it, it was too unpleasant. The other side of it is the congestion charge in southern Manhattan. Suddenly, the subways were crowded, crammed with people who formerly drove, and having all these law-abiding citizens on the system, things got way better. It is harder to abuse the system when you’re surrounded by other people.

PBS: And automation doesn’t just relate to cars; it also relates to public transit. There’s drivers, and so a fixed human cost per train, and that contributes to the lumpy service. If the public transit lines were automated, you could have a single train car coming every seven minutes, the same number of cars over an hour, but spread out. I’m sure this exists somewhere, but it seems like such an easy problem to solve. If we can make self-driving vehicles that work on paved roads, then it would seem so simple to make a public transit train that can drive itself on a train track.

AM: Yeah, rail is the easiest mode to automate, because there are only two directions to go: forward and back. Later this week, on Thursday [ed. note; out now!], I’m publishing a paper with the Institute for Progress, as part of their Transit Abundance Playbook, arguing that what we really need to do to improve public transit in the United States is automate it. And there’s one—not even a law, a subsection—Section 13(c) of the Mass Transit Act says you cannot automate without making every single worker whole. And all Congress needs to do is strike that one provision. The only automated system in the United States is Honolulu, because they built it to be automated from the start; there were no workers to displace. Nowhere else has done it, because it’s just too expensive.

PBS: That’s really unfortunate, because worker displacement is how we develop economically, right? If we just protect workers—an example I give in my book is that 200 years ago, 80% of US workers worked on farms; now it’s 1%. That’s because of automation, and if we still had 80% of us working on farms, we wouldn’t have all this other stuff. So yes, it’s painful for people to lose their jobs, but over time they find new jobs, and that’s how progress happens.

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AM: Well—just so that no one thinks that I’m picking on Americans—Waymo wants to come to Toronto, but the mayor’s press secretary said explicitly that Waymo is only welcome in the city if it can show that no ride-hail driver will lose their job as a consequence of Waymo arriving. There was a time when everyone got around by horse, and then the car came along, and everyone who was a stable hand or a farrier lost their job. But do we think that was a bad outcome? I don’t think so.

In fact—I’ll put on my economist hat: just buy them out. Figure out how much money you’re going to save, figure out what they will accept; it’s like, “All right, leave aside the future generations of workers that won’t have jobs, what will it take to make this generation whole? We’ll buy you out.” Given those parameters, we could find a deal worth taking. We just need to find a counterparty who will accept it.

This has been a wonderful conversation. Benjamin Shiller, thank you for your time.

By way of postscript: Prof. Shiller is also the author of a recent book, AI Economics: How Technology Transforms Jobs, Markets, Life, & Our Future, which trains his economist’s gaze on what artificial intelligence will, and will not, change. I expect to review it in a future issue, but if the topic interests you, don’t wait on me, order your own copy now!

The Transit Abundance Playbook, various authors, published by the Institute for Progress, June 2026

Italy is not an easy place to build a subway. Though not a poor country (it has the eighth-largest GDP in the world), it is poorer than the United States, and it suffers a massive debt-to-GDP ratio, severely constraining its fiscal flexibility. The density and antiquity of its cities means that tunnelling must contend with challenging work constraints, unhelpful soil composition, and archaeological protections. Notoriously, until a few decades ago its public-works contracts were utterly corrupt, with bids rigged and bribes paid throughout: the Mani Pulite (‘Clean Hands’) investigations of the early 1990s arrested some 4,800 people and placed more than half the national parliament under suspicion.

Given all of this, it’s no surprise that Milan and the smaller northern cities build subways (metros) for about $300 million per mile, and the ruins-riddled centre of Rome builds for about $800 million per mile.

The United States has every advantage Italy lacks. It is the richest country in the history of the world, with the deepest capital markets and the best engineering schools, a civil service largely free of corruption, and a century-old talent for building transit; before the war, it laid more streetcar track than any other nation on earth.

It is therefore surprising that it builds rail rapid transit for around $800 million per mile on average, i.e., it pays on average what Italy pays in its most-difficult environment. And in New York City—which builds the most expensive transit in the world—Americans paid roughly $2.6 billion per mile for the Second Avenue Subway.

To be specific: per mile, New York’s Second Avenue Subway cost more than ten times what one global peer city, Madrid, pays, and NYC’s East Side Access, at $11.2 billion, opened as the most expensive railway station ever built.1 As a consequence, despite its wealth, the country now builds very little transit indeed. Exhibit A is California’s high-speed rail line, whose projected cost has ballooned from $33 billion to more than $126 billion; with federal funding now clawed back and not a mile of usable track laid, whether the line will ever be built is an open question.

The richest country in the world, with all its technical capability, builds less transit and pays far more per mile than peer countries with far fewer advantages. Why?

That puzzle is the spine of the Transit Abundance Playbook, a collection published by the Institute for Progress on 17 June 2026. It consists of fifteen short essays by transit practitioners, researchers, and advocates, each aimed at naming and solving one driver of the USA’s exorbitant transit-construction costs. Most reach for a federal lever to fix it, because this is a ‘playbook’ for the US Congress to run to bring American transit up to a global standard.

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My readers should be advised that I’m no neutral third-party reviewer; I wrote one of the Playbook’s fifteen essays. (More about it later.) That alone would make me a strong proponent of the book and its prescriptions. But I have more reason than that to recommend it.

Before I became the writer of Changing Lanes, I worked in the space the playbook describes. As a public servant, I spent four years planning a new bus rapid transit line in western Toronto (across Mississauga): I wrote the project scope, procured a consortium of planners and engineers, and led that team as we drafted, consulted the public upon, and ultimately obtained political approval of the project plan. Later, I consulted on several transit projects, both as part of a major firm and as a one-man shop.

And given that experience, I say that the Playbook is right. The problems it identifies are real; they are the most important ones to solve; and the solutions on offer should be implemented.

Beyond that, I want to say what the Playbook is too polite to make plain, and name the root of the problem. What is holding back American transit? The Introduction states that “there is no single cause”. On the contrary, I think that there is, and that we can see that cause at work behind almost every individual contribution. Knowing the enemy is vital to fixing the problems holding back American transit: not only its construction, but its operation too.

That enemy’s name is defensive proceduralism.

Rules as Tools, or as Weapons

The key word here is defensive. Let’s define proceduralism as the tendency to establish rules in advance for how to scope, design, and deliver a project, and for what kind of input the project will accept, from whom, and how. This is a good thing, allowing everyone involved to know the game being played and how to play it. Italy’s experience demonstrates the value of proceduralism. The country enjoys very low per-mile transit construction costs today because, to combat corruption, it relies heavily on open procedure: published price lists, an anti-corruption authority, bids scored on merit, and internal state capacity such that civil servants are able to run a project themselves. These measures aim to prevent corrupt practices, and have the helpful downstream effect of keeping costs low.

So Italy has proceduralism. What is the USA’s problem, then? Not the presence of rules, but their aim. In Italy, the rules aim to deliver projects on time and on budget. In the USA, the rules aim to stave off lawsuits or blame. That is the American disease, and once you look for it, you will find it behind nearly every contribution in the collection.

Start, for instance, with Transit Projects Need a Single Decision-Maker, written anonymously (!) by someone who “works in project delivery for a public transit system”. Perhaps because they are anonymous, the author of that piece states the case more plainly than the authors of the introduction dare to. American institutions are unusually open to litigation, everyone understands that judges rather than elected officials or civil servants are the final arbiters of what is permitted, and that those judges are not trained as engineers or planners, but rather to determine whether the rules were followed. Agencies, knowing this, lower their ambitions and spend their days documenting decisions rather than making them. In such an environment, procedure is less a tool for building and more a weapon to stave off lawsuits. Environmental review under NEPA or CEQA is one example of this; so are rigid sequencing gates, or lowest-bid mandates that exist mainly so no official can be accused of favouritism.

Consider also procurement, as Alon Levy (whom Changing Lanes admires deeply) writes in We Should Know How Much Transit Components Cost. American agencies prefer to construct their transit projects in lump-sum contracts. Why do they do this? So that when cost overruns appear, as they will, the contractor must absorb them, keeping the project on budget and hence the agency free of blame. That’s defensive procedure doing exactly what we would expect, and the result is exactly what we would expect as well: bids are inflated dramatically to cover the risk being transferred. On top of that, there is the change-order racket. Contractors win bids by submitting low, and then any change that comes up after award is a change order, and the opacity of their costs—lump-sum contracts mean that per-component and per-activity costs are kept secret as commercially sensitive—allows for maximized change-order values, to the tune of 20 to 50 percent. The fix is not less procedure but different procedure, namely the itemized, public pricing Italy and Spain use, which Levy estimates would cut 15 to 20 percent from a typical project.

The thread runs everywhere. It’s in Eliminate Redundant Subway Cross-Passages, where Brian Potter shows that American tunnels carry roughly twice as many cross-passages as European ones, because the National Fire Protection Association (NFPA) 130 fire standard demands one every 800 feet against Europe’s 1,640, even though the NFPA concedes it has no technical substantiation for the spacing. The requirement is merely defensive, a margin of safety nobody can be blamed for adding.

The same instinct shows up in Fast-Track Democratically Approved Transit Projects, in which Hayden Clarkin describes projects that voters have already approved at the ballot box being sent through years of NEPA review, not to inform a decision the public has made, but to hand opponents a second venue in which to undo it. The defensive party here is the agency: it dares not waive or even shorten the review for a project voters have already approved, because an under-reviewed approval is exactly what draws the lawsuit. So the agency runs the full multi-year process to protect itself, and that very thoroughness is the weapon its opponents pick up.

Rules First, Build Second

From my days as a transit-project lead on the public side, I remember the order of operations: my job (and that of everyone I worked with) was to follow the rules first, and bring the project in on time and budget second. Nobody wanted delay, but the facts of the matter were that breaking a rule, no matter how small, was a thing one could be punished for; breaking the schedule or the budget was not. In such environments, the rational move is to give up decision-making power, since that means also dropping the burden of blame.

In Put Transit Staff in Charge of Their Own Projects, Paul Lewis traces why American agencies hollowed out their own expertise at transit building: agencies lean on consultants as decision-making buffers, hiring outside firms to validate controversial choices and supply political cover, a habit that reinforces risk-aversion and diffuses accountability so that no one inside is answerable for anything. A staffer who uses judgment and is wrong owns the mistake; one who can point to a consultant’s report does not. I have worked as a consultant in this world, and out of respect for everyone involved, I will say only that Lewis is absolutely right.

Four of the collection’s pieces map this same instinct to seal off every angle of attack and to pay for the invulnerability in taxpayer money and additional years spent. In Get the Best Value in Transit Procurement, Anthony Potts shows that lowest-bid mandates—defended as a guard against favourtism and graft—instead shut out the best-value bids. In Focus Capital Investment Grants on Improved Project Delivery, Stephanie Pollack shows us a federal grant process grown so rigid that its own rules now obstruct the timely, affordable delivery they were written to secure.

The remaining two turn on environmental assessment. In Let Agencies Do Their Own Environmental Review, Jamey Tesler shows agencies compiling reams of paperwork merely to certify that a routine project has no significant environmental effect, a status that all but a handful of projects plainly qualify for. Tesler’s remedy is to let agencies self-certify these routine findings. As it stands, the documentation protects not the environment but the FTA’s decision from challenge, and gets done in full even when the answer was never in doubt. In Let Transit Agencies Buy Land, Aidan Mackenzie shows agencies unable to buy land or move utility lines until NEPA review is complete, to avoid prejudicing the findings. That process takes on average 5.7 years, so they end up paying more for the land, and sometimes routing around it entirely.

Artist’s conception, after original art from the Playbook

Public servants still have some capacity for discretion, and some use it. On my own project, as part of the engagement process, one party whom I had a duty to consult tried to bill the project for the time and capacity spent on its own due diligence.2 I figured the demand was a bluff, refused it, and the request went away. But I remember that stand vividly today because it put the project, and my leadership of it, at risk; the party might have come back with a lawsuit. The smart thing to do for my career would have been to pay, write a memo explaining the situation, and move on. I didn’t because I found the demand offensive, but I can’t blame other leaders who decide the other way, their prudence outweighing their outrage.

The better path, of course, would be to draw the rules about consultation narrowly enough that leaders are never put in that position.

Fifteen Problems but One Foe

The Transit Abundance Playbook offers an institutionalist case for building transit more cheaply. The modesty of its introduction insists “there is no single cause” and lists the drivers as equals: overdesign, poor planning, veto points, permitting, thin capacity. That is sound coalition-building, but in my view it undersells the book’s best insight, the one its anonymous single-decision-maker essay says out loud: there is a single cause, namely the fear or being blamed, exacerbated by the litigiousness of Americans and the unusual latitude for American courts to entertain it.

Seen in that light, defensive proceduralism is the problem. Anyone who funds, plans, builds, or votes on transit should read this book, and should read it as one argument, that transit agencies need the expertise to run their own projects, the power to get them built, and the freedom from interference by opponents and rent-seekers.

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In closing, I’ll note that defensive proceduralism shapes not only how America builds its transit, but also how it runs it. My own contribution to the collection, Close America’s Transit Automation Gap, is therefore an outlier in that regard, because it’s less about how to build more cheaply than about how to build a system that will be cheaper to operate. I encourage readers of Changing Lanes to read the whole thing (it’s about the length of a typical issue).

My argument there is also about defensive proceduralism: the reason that American transit agencies have not retrofitted their systems for full automation is a sixty-year-old labour-certification rule. That rule was drafted in 1964 to solve a real problem, but one that was solved by the end of the decade. It wasn’t removed, and not only remained on the books, but became supercharged in the 1970s as the national government became a major funder of transit operations. The result is that while the rest of the world (including Vancouver but not, to my chagrin, Toronto) automated its rail transit, the USA largely has not. Rectifying that failure would save hundreds of millions in operating funding, each and every year.

The Institute for Progress has done Americans a great favour by showing how transit construction could be improved. But there is also need for another playbook that shows how transit operation could be improved as well. If the Institute is looking for a new project, it could do much worse; I can think of a few ideas legislators would benefit from hearing.

Rhishi and I are kindred spirits. We are both fellows of the Roots of Progress Institute; we have both worked in consulting, operations, and Alphabet-sponsored Big Tech across a variety of roles; and are both now writing about how automation will change the world.

Rhishi is intimately familiar with sectors I know less well, notably mining and agriculture, and I learned a great deal from this piece. I am sure you will as well!

Waymo has already driven close to 200 million miles and 20 million rides without a driver. Waymo is already operating in multiple metro areas in the United States, with more than 20 additional cities coming up next, including London and Tokyo. Waymo has opened freeway operations to paying riders in Phoenix. Tesla has a limited number of robotaxis running in Austin.

While Waymo’s achievements are significant, other sectors like mining, industrial security, and agriculture have beaten them in the autonomy game a few years ago.

Hundreds of autonomous-haul trucks have moved millions of tons of rock across Australia and Latin America for more than ten years. Autonomous ground and air units have patrolled airport perimeters at multiple airports for a few years now. Agricultural machinery giants like John Deere and CNH have been pushing for autonomous corn and soybean systems to become fully operational in the next few years, with some operations, like tillage and grain cart management, already working autonomously.1

These domains reached driverless operation at scale before the public-road robotaxi did.

Now, consider Phoenix, where multiple robotaxi companies are operating. One of them, Waymo, provides a robotaxi that drops a rider at the airport. The fence line the Waymo passes on the way in has been patrolled by autonomous ground and air units for the better part of a decade. Two autonomous systems are operating within a mile of each other, but they arrived on two completely different timelines, with Waymo the newest.

In the hierarchy of operational autonomy, the robotaxi is the latecomer.

The rate-limiting factor for adoption is not AI capabilities. It is the four conditions that robotaxis face, each in the most difficult form possible. These are operating environments, liability regimes, unit economics, and operational maturity. These circumstances have had and will continue to have the greatest impact on adoption rates and scaling.

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The Road Most Taken Is the Hardest

First, operating environments.

The Pilbara region of Western Australia has a population of only 60,000 people across 500,000 square kilometers, a density of 0.1 people per square kilometer. It sits atop enormous iron ore deposits. The haul roads to and from the mines are private, fenced, and mapped to the centimeter. A 410-ton Caterpillar 797F is four stories tall and has tires taller than a person. It has been hauling rock there autonomously and nearly continuously since 2013. Since then, Caterpillar’s global autonomous fleet has moved more than 8.6 billion tonnes.

It is difficult to visualize 8.6 billion tonnes of rock. For context, the granite George Washington at Mount Rushmore weighs roughly 4,000 to 5,000 tonnes. The global autonomous fleet has moved enough material to carve 1.7 million of them!

Agriculture follows the same logic in its own low-density environment. John Deere unveiled their fully autonomous 9RX tractor at CES 2025. The tractor is a behemoth with tires bigger than human beings and boasts an 800-900 horsepower engine. Deere already runs an automated tillage machine and hopes to achieve fully autonomous corn and soybean production by 2030. The farm reached driverless operation because it operated in a more controllable environment than a street.

When I visited Raven’s (part of CNH, the world’s second-largest agricultural equipment manufacturer) research facility in Sioux Falls, South Dakota, about four years ago, I had the privilege of seeing the testing of an automated grain cart solution in a real field. In 2025, CNH launched its automated grain cart solution as a commercial product.

The operating environments for mining and agriculture are well defined and ideal for the adoption of autonomy. They are bound, mapped, well managed, and almost empty of obstacles, especially of the human kind. Even though a mine and a farm have more dust and dirt to deal with, the operating environment on public roads is much more challenging, with unpredictable human behavior, gnarly traffic conditions, and changing weather.

A leafy-green field in Yuma, Arizona. Notice the well-defined rows and almost flat fields devoid of humans or other traffic (photo by the author)

A robotaxi has to operate in one of the hardest contexts. Waymos and Teslas have to navigate public roads shared with pedestrians, cyclists, and human drivers in legal environments they don’t control.

The mine and the farm solved the issue of autonomy by changing the road. The robotaxi has to solve autonomy without that luxury.

Who Owns the Risk?

The second factor shaping where autonomy flourishes first is liability regimes. Andrew argued here a few weeks ago, in “Robotaxis Companies Must Always Pay, that robotaxi operators should accept full operational liability. The clean assignment of responsibility is what lets deployment scale.

Mining is the proof of that concept.

In a mine, the operator and the customer are often the same billion-dollar entity, and workplace-safety law already assigns the risk to that operator on a private site. The mining industry answered the liability question before the trucks arrived.

Farming sits in between. Liability is shared among the manufacturer, the software provider, and the operator. It is less settled than mining but far clearer than the public road.

On public roads, industry and regulators are still litigating this question on a case-by-case basis. As Andrew mentioned in his piece, Benavides v. Tesla was a recent, expensive example.

Autonomy scales fastest if the liability structure is settled. Mining’s liability structure was settled by default.

The Math Has to Work

The third factor is unit economics.

Autonomy went to mining first because the economics were overwhelming. A mining customer runs a fleet worth billions, pays a heavy premium for labor in remote locations, and runs 24/7. The automation of haulage fleets is the chosen path for much of the mining industry in Australia and Latin America.

Removing the cab operator compounds across every truck, every shift, and every commodity. This situation is especially true in Western Australia, where the odds of finding a random human are low, and the odds of finding someone willing to work in the difficult and dangerous mining environment are even lower.

I am working on a project with the Western Growers Association to help them figure out how to automate the harvesting of iceberg lettuce. The economic case for autonomous vehicles is strong for specialty crops like this one; more than half the cost of getting your bagged salad to you is due to manual lettuce harvesting. Access to labor at economically favorable prices is becoming increasingly challenging, making the economic case for automated harvest stronger every day.

Airport perimeter, warehousing, and logistics security tell the same story from the other end of the size scale. An autonomous security unit removes costs associated with hiring and churn, skill-set inconsistencies, and the need to reach hard-to-access areas. For example, Asylon’s DroneCore pairs aerial drones with ground quadrupeds and reports thousands of dollars in annual savings across Asylon’s platform, which has completed more than 250,000 security missions (as of 2025).2

A robotaxi’s per-ride economics, by contrast, are still converging as companies try to optimize product development and operating costs and match them with customers’ willingness to pay.3 The question of “when will Waymo be profitable?” remains valid.

In mining, security, and agriculture, that question was much less in doubt.

Doing the Reps Helps

The final factor to consider is operational maturity.

When building autonomous systems that operate in real-world environments, it takes time to learn and handle edge cases. When I was working on building AI-powered selective weed-spraying technology, we spent months in the field testing the algorithms under different conditions, yet still didn’t make much progress getting it to work. The graveyard of robotics startups is massive, with many companies that couldn’t ‘get enough reps’ to make the technology real-world ready.

Caterpillar’s MineStar Command for Hauling went commercial in 2013, runs 24/7 across six commodities, and reports zero lost-time injuries attributable to autonomous operations. Caterpillar has had 13 years of experience in fleet management, exception handling, and maintenance, which are the unsexy parts of the business. The reps help identify unforeseen issues and build scalable, resilient, and efficient systems that help you manage and run a business, rather than just being excited about cool technology.

Waymo’s genuinely impressive scaling has happened mostly in the last 24 months. The robotaxi’s autonomy stack may be more sophisticated, but the operating discipline around it is younger. Waymo and other robotaxi companies have used simulation to “learn” and expose unseen use cases, but real operating environments still throw surprises.

Autonomous tillage equipment and grain-cart solutions have undergone trials in real-world settings for many years before entering commercial production. By contrast, right before Christmas last year, a power outage in San Francisco caused many Waymos to stall at intersections, block arterial roads, and, according to at least one city official, impede emergency vehicles. It appeared as if Waymo robotaxis compounded the problems caused by the blackout.

In physical systems, that gap matters more than people expect.

Why Is This Reframing Important?

When we think about autonomous vehicle adoption, we need to reframe what makes adoption happen faster and in more domains. Once the technology issues are resolved, the adoption curve will be driven (pun intended) by factors such as the operating environment, liability, economics, and experience.

Why does this reframe matter for people who think about AVs for a living?

It matters, because if factors like operating domain, risk infrastructure, unit economics, and experience gate autonomy are considered, then the interesting investment question isn’t only “when will the robotaxi be profitable.” The interesting investment question is also where innovators can apply autonomous technology in an economically sustainable and safe manner.

Robotaxis are a major domain that lacks the advantages of a controlled operating environment, clear liability regimes, favorable economics, and years of experience.

For any domain, the differentiation will not come purely from raw model performance. It will come from an operational track record, edge case management, and deep customer relationships.

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For most of the last decade, mining autonomy, robotaxis, autonomous tractors, and security robots were separate engineering worlds with little in common. But now a common technical stack is emerging across all of them, which includes foundation models, vision models, imitation learning, and diffusion policies for control.

The same underlying machinery that drives a robotaxi is starting to drive a tractor, a quadruped, a humanoid. When the stack converges, the lessons from the domains that solved autonomy first will become transferable to those still catching up, whether it is mining’s operating discipline, security’s unit economics, or agriculture’s environmental control.

The story of Physical AI and automation is complex, with multiple threads spanning robotaxis, mining, security, industrial applications, transportation, and many other domains.

Paying attention to technology issues is extremely important. It is also exciting and exhilarating, as smart teams and organizations solve one difficult problem after another, like Mark Watney in The Martian. But if we are interested in seeing these technologies scale and actually have the positive impact they promise, we will have to pay attention to the other, non-flashy parts of the autonomous adoption equation.

Waymo is not leading the autonomy party. It just showed up to join it.

Which domain will quietly solve autonomy next, and who will notice before the market does? I plan to pull this thread every other Wednesday on Jacquard’s Loom.

This has been a Changing Lanes guest post by me, . I am a solopreneur working on automation, physical AI adoption, and digital transformation in legacy industries. I have experience working at Google X, Amazon, and multiple startups in supply chain, automation, and AI.

My Substack newsletter, , is for anyone curious about how automation and physical AI will impact our daily lives. Jacquard’s Loom will take a holistic approach by examining the how, what, when, and why (or why the hell not?) of automation and physical AI.

I would like to thank for giving me the opportunity to write this guest post for Changing Lanes, and for his honest feedback on earlier drafts.

On Wednesday, 24 June at 1200h ET, on the second Changing Lanes livestream, my guest will be the foremost American authority on regulating automated driving: Professor Bryant Walker Smith of the University of South Carolina. Smith helped develop the SAE’s “five levels” of driving-automation taxonomy, and earlier this year he testified to Congress on how the United States should regulate self-driving. Subscribers will be notified when we go live; you may also sign up here.

And on Tuesday, 16 June, i.e., in five days, the subscription rate for Changing Lanes goes up, rising from the current rate of US$10/month or US$100/year to US$13/130. This means that now is the best time to change your subscription from free or monthly to annual, since switching now locks in a full year of content at the best possible rate. It’s as easy as clicking this button!

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Today I’m pleased to offer readers my interview of Eric Lombardi, a candidate for the leadership of the Ontario Liberal Party, and (if he succeeds) for the office of Premier of Ontario. The material that we discuss is separated by caption, as follows; readers may wish to skip ahead to their favourite subjects.

• Managing Decline or Building Abundance

• Ribbon-Cutting vs. a Manufacturing System

• The Housing Theory of Everything

• Bringing Down Construction Costs

• Rail: Speed, but Also Coverage

• Road Pricing

• Driving Automation

• Rural Areas, Remote Areas, and the North

• Energy and Nuclear

• In Conclusion

The reason I am giving readers permission to skip ahead is because this piece is lengthy, about four times the length of a typical Changing Lanes piece. But I am nonetheless presenting it in full, for two audiences.

If you live in Ontario, you will find it interesting as a vision for the province’s future: one potential leader’s account of how to build a more prosperous province. If you live anywhere else, you will also find it interesting, as an example of what the abundance agenda looks like, translated into a campaign frame, and defended by someone who aims to win votes with it.

What follows is a conversation about why housing costs what it does; a theory of why Toronto builds transit at four times the cost of Paris; a case for high-speed regional rail, road pricing, and driverless cars; and a plan to make people consume more electricity rather than less.

Changing Lanes is a non-partisan newsletter, and we do not endorse any candidate of any party. But we are interested in any candidate who thinks that innovation, technology, wise policy, and good institutions can make the future better than the past. Lombardi seems to believe this, and to have thought deeply about how the abundance agenda can succeed in his particular context. I am confident that readers of this newsletter will find what he has to say interesting.

Lombardi and I spoke on 1 June 2026. The transcript of our remarks has been lightly edited for clarity.

Managing Decline or Building Abundance

Andrew Miller: It’s a pleasure to speak with Eric Lombardi, the founder of More Neighbours Toronto, Toronto’s foremost YIMBY organization. He has stepped back from that work to explore the leadership of the Ontario Liberal Party — so there’s a chance that, in a few years, he could be the next Premier of Ontario. I won’t put words in his mouth, but it seems to me that Lombardi’s interests are very abundance- and YIMBY-coded, which is the very philosophy we endorse here at Changing Lanes. So I reached out, and he graciously agreed to sit down and talk about his vision for mobility in Ontario. Eric, it’s a pleasure to speak with you. Thank you for agreeing to speak with me.

Eric Lombardi: Andrew, it’s a pleasure to be here. My exploratory will soon become a campaign, so stay tuned over the next week or two — perhaps by the time we release this, I’ll be formally in the race. [Note: Lombardi formally declared his candidacy on 9 June 2026, a week after we spoke.]

Eric Lombardi

AM: That’s breaking news. It’s no longer an exploratory campaign… it’s happening! Very exciting. Let’s talk about your vision for Ontario. I think the place to start is what I’d call the spiciest thing you say: that Ontario is managing decline. Long-time readers of Changing Lanes will know I’m against what I call genteel decline, which is what I think a lot of what happens in Toronto and Ontario amounts to. I wonder whether we mean the same thing, but I won’t tell you what I think. When it comes to how people and goods move around this province, what do you mean by decline, and what’s the alternative?

EL: What I mean by decline is just the real facts of how our economy has performed over the last 25 years. Once you remove immigration and inflation, our economy has been one of the worst-growing in the entire developed world. We’ve had enormous productivity challenges, and over this time we’ve seen increasing dysfunction in the state’s capacity to deliver the goods and services that produce a higher quality of life. The result is that living standards for ordinary people have fallen. And the biggest part of this is how much of the burden has been placed on the next generation, in the form of what I call a milestone recession: the modern economy has made it increasingly difficult for young people to hit the traditional milestones of life — finishing school, getting a good job, buying a home, starting a family. All of them are shifting further into the future. While this is a problem globally, much of it can be explained by economic trends specific to the province that have worsened those global factors.

We also need to understand how decline gets managed by politicians. It’s been bipartisan across the spectrum here in Canada: the adoption of communications-forward politics rather than outcomes-forward politics. So even as things function less well, there’s always an air of optimism — we’re doing things, we’re building things, we’re growing the economy, we’re creating jobs. But the numbers don’t hold up to the language.

Ribbon-Cuttings vs. a Manufacturing System

AM: I spent eight years with the Government of Ontario as a civil servant, and I came away with a certain cynicism. The attitude in that era — I won’t point fingers at any particular premier — seemed to be that our job was to write a cheque, so we could then hold a press release or an event and say we’re spending X tens of billions, Y hundreds of billions, on this problem. And once the press release was out, that was it. Whether the money achieved any of its goals, whether it was even the right thing — none of that mattered. What mattered was being able to say you’d done something.

EL: In politics we love to announce big cheques, and we like to talk about all the jobs we’re creating. We don’t like to talk about the jobs we’re destroying by taxing those cheques to pay for the spending. This thinking about trade-offs has become almost entirely absent from our politics, and that’s one of my motivations for running. What upsets me, specifically in infrastructure — and we’ll talk a lot about transportation today, but it’s just as true in energy — is that we think in projects, because a project is something a politician can announce. And often the projects we pursue don’t start from a technocratic ranking of what would optimize value for money; they start from what politicians want to draw on a map to please which voters, and where.

So instead of thinking about how we build as a project, I want to orient Ontario toward thinking about how we build as a manufacturing system. What are the networks we want to reach in 25 or 30 years? Then how do we standardize designs — stations, below-grade, at-grade, viaduct-based rail, BRT — so that we’re consistently building toward a target over time? That’s how the great rail infrastructure systems around the world are built. Then you prioritize which projects and extensions come next. And the lever for government isn’t which projects get pursued; it’s how heavy a foot we want to put on the gas by capitalizing that process. That’s a very different perspective on growth and transport than what we see today.

The Housing Theory of Everything

AM: I want to get into that closely. But before we do — you mentioned energy, which is one of our minor themes here at Changing Lanes, so if you want to talk nuclear or renewables, we can. Before that, though, the area you’ve been strongest on, and that we spend the least time on here, is housing. So let’s warm up with that. Something I like to say — I didn’t invent it — is that housing policy and mobility policy can’t really be separated. You can’t build housing without a plan for how the people living there will move, and you can’t build a mobility system without thinking about the jobs, homes, and commercial uses you’re trying to connect. What’s your housing vision, and how does it relate to your transport vision?

EL: To be cheeky, I’d say you actually can separate them, but you create a lot of problems for yourself, and that’s part of why we are where we are. Just because you can doesn’t mean you should. Getting ahead of transportation and infrastructure policy to enable housing is incredibly important. But let me start with the big picture, then turn to my housing agenda.

I subscribe to the Housing Theory of Everything, which many of your readers will have seen in Works in Progress, out of the UK, a few years ago — I think it was Sam Bowman [Note: it was, with John Myers and Ben Southwood]. When an increasing share of people’s income goes to rent — whether that’s paying a landlord or paying higher interest on a mortgage to afford a suitable home — they’re not saving, they’re not investing, and they’re not spending. So the economy is deprived of all the healthy activity that drives growth, competition, investment, and higher wages. When you look at what’s happened with housing over the last 25 years, and then at our slow growth and the unhappiness young people express, these things are related. If you have one dial to twist to re-platform the province for growth, it’s fixing our broken housing system.

How do you do that? There are five areas. First, the rules around permitting and land use. Ontario municipalities set land use, so even though Mississauga’s and Toronto’s zoning codes look similar on the surface, you have independent policymakers deciding what studies a multi-family project needs, what’s allowed in each designation, all the bylaws. It’s too complex. I’d provincialize the setting of rules and make municipalities the appliers of rules — “provincial residential zone two applies in these areas” — rather than reinventing the wheel 440 different times. The same is true of the application process itself: in the GTA the average time to get a building permit is over 20 months; in places like Denmark it’s closer to 60 days. That’s not a sign of a functioning system. It’s a sign of a system so complex and discretionary that, even when all the rules are followed, people still believe the outcome is being corrupted. That’s a big problem.

Second is the building code. A lot of what we build is ugly and uncanny. Our code doesn’t distinguish between a four-storey building and a 60-storey one. If we reformed it — single-egress design, European elevator standards, updated fire-code standards for the built form, the way housing gets packaged — we could produce much nicer layouts at much lower cost, especially for the new urban places we’re trying to build. This is something a lot of YIMBYs miss: beauty should be a consideration in how we grow. When we make the case for growth, we should explain why it’ll be nice and beneficial for people — not just “housing is housing, it doesn’t matter how ugly it looks.” It does.

Third is taxes. American readers may not appreciate how much tax Ontario puts on new housing; there’s nowhere like it in the world. Until recently, when governments started to address it, housing was taxed more than cigarettes or alcohol — more than any other product or service in North America, to the tune of about 30% of the cost. I’d abolish all taxes on new housing construction, whether it’s land transfer tax (a tax on the exchange of housing), development charges (an upfront tax on every new home), or HST (a sales tax). These are unproductive taxes that create a like-for-like price gap between an existing home and a new one, which effectively forces buyers to speculate that existing-home prices will keep rising to justify buying new supply.

Fourth is non-market housing, which has been an abandoned part of the mix in Canada for about 40 years. We’re starting to come back to co-housing, co-operatives, not-for-profit and public housing. All of these need to be part of the mix and can complement private-market development. We should be talking about somewhere between 10 and 20% of all new housing being non-market in some form.

And fifth — we have to do right by young people, and by those who bought in the last 10 years and might face economic insecurity if house prices fall. For young people, we need to make housing affordable again. I think we should allow smaller down payments for first-time buyers — 10% on a CMHC-insured mortgage — to lighten that burden. If you’re young and could retire before you’d pay off your mortgage, you should be able to buy with a longer amortization, say 35 or 40 years, because you expect higher income later in your career; when you remortgage, you can always move to a faster payoff. Sometimes, when you’re trying to start a family, affording the home to start it in matters more, and that can’t be ignored. I’d also have the government give zero-interest loans of up to 5% toward the down payment for families with kids under six, because home ownership is something most families should be able to access and aspire to, and it’s good for kids and for society. And I’d give targeted renter relief to people under 35 and to families with kids under six, so they can save for that investment. We have to make it easier for young people to buy that first home earlier, if we want them to be able to think about starting families earlier too.

Then we have to do something for those who bought in, say, 2020 and might be out of equity, or might not have enough saved if they need to move for a job. You don’t want them trapped. We need to prevent downward mobility so people have economic confidence going forward. And if, as Premier, I’m going to say we need lower home prices for housing to be affordable — which no politician in this country seems willing to say — then I also need to be willing to say to people who couldn’t wait for government to make the right choices, and who now find themselves out of equity or in a debt trap: we have solutions for you, you have a great future, we’ve got your back.

Bringing Down Construction Costs

AM: I didn’t interrupt you there, because I wanted readers to see just how in the weeds you are on housing. You’re not a fly-by-night candidate on this; you’ve thought very deeply, and you’ve got a strong program. But given all that, what’s your thinking on mobility? We’re going to create a lot more infill housing and smart new communities — tell me how we’re going to move all the people who live in them.

EL: There are a couple of problems to solve. The first is how we build. Ontario has become uniquely inept at building infrastructure, both on time and at globally competitive cost. I know some of these challenges exist in the United States, and more broadly across the Anglosphere —

AM: Let me interrupt to say that I know you’re very influenced by the work of Alon Levy and the Transit Costs Project. Some Changing Lanes readers will know their work. This ineptitude is new. There was a time in my lifetime when Ontario, and Toronto, were some of the best builders in the Anglosphere on a cost-per-kilometre basis for new rapid transit. In your view, what changed? What’s the problem, and how do we fix it?

EL: Number one, we shifted from largely public-led infrastructure development to consortium-based public-private partnerships. When the TTC managed delivery of the Sheppard subway, or the Yonge North extension, we were closer to $300–400 million per kilometre — and we had nice stations that people thought were overdone and a waste of money. If we could get back to that inflation-adjusted value, it would be a miracle, because with the Ontario Line — and even the Eglinton LRT, which isn’t really rapid transit — we’re closer to $800 million, and over a billion dollars per kilometre, a million dollars a metre, to build now. That’s crazy. Politicians wanted to feel they could better manage costs by allocating risk to a third party, and at the time there was a sense that could work. But governments failed to recognize that you have to think about the private sector in terms of monopoly and competition dynamics. Where you want to use the private sector is for commodity services that can be procured competitively, because many providers can bid. What you want to keep in the public sector is monopoly expertise — which is harder to identify, and which you want to endure so it can learn over time. We broke that model.

We also uploaded responsibility for infrastructure to Metrolinx, which is also the owner and operator of GO bus and rail. My understanding is that Ontario would now be inadmissible to the EU, because we have an infrastructure builder that is both the operator and the builder. And we’ve seen, with arrangements like Deutsche Bahn on GO electrification, that it doesn’t work. So I’d reform Metrolinx, including separating GO operations from the infrastructure-development side.

AM: To put my cards on the table, Eric: I was in the Government of Ontario when the GTTA, as it was then called, was founded, and later when it merged with GO Transit. It’s not my world anymore, but I was there, and I’d say it never could have worked to combine a long-range planning function with an operator. GO Transit are operators; they had their own planning department, and they didn’t want to think about what was happening in other areas. And with the Deutsche Bahn situation, it’s clear they think running a diesel hub-and-spoke service for the morning and evening peaks out of Union Station is a perfectly fine mission, and why are you wasting their time asking for anything else? That blunted whatever force the former GTTA could have had as a long-range planner and as a way of prioritizing project lists. So there’s a great deal of merit to what you say.

EL: The first thing is to restore the healthy function of our institutions, because we’re spending four times, on a unit-cost basis, what others spend. Line 16 in Paris — fully underground, fully automated, built for the Olympics — came in at the equivalent of about $250 million per kilometre. We’re building the Ontario Line at $1.1 billion per kilometre, and $1.7 billion once you bundle in all the operational and financing pieces. That means we build far less than we should. Until we fix that, it’ll be very hard to dig ourselves out of congestion, because we need to build rail in the GTA the way China has built it in some of its major cities — we’re so far behind, having basically stopped for 50 years, and now everyone’s congested.

So first, fix how we build, so we can build more. Then, how do you approach building more in general? Number one, we need long-term plans for what we’re building, both for interconnectivity across the province — and I’m not sure you’ve had a chance to look at my high-speed rail proposal, which we can talk about — and for Toronto, the GTHA, Hamilton, Niagara, Windsor, London, Ottawa, Kingston, and even small centres. What’s the vision we’re building toward? How much transit will we build over the next 25 years? Once you have that vision — and even if I’m going to draw some lines on a map, it ultimately needs to be owned by an independent institution making prioritization decisions — you set up a manufacturing process to build it out. So instead of a politician like me cutting a ribbon at every announcement, it’s: our budget allocates $10 billion a year for rail transit expansion in Ontario, every year, in perpetuity, so we can build out the plan. That’s not a glamorous announcement, but it will get us far more transit if we think like a process.

AM: It’s absolutely true. The lesson from Alon Levy is that we need more state capacity — fewer consultants managing consultants. I’ll say that I’ve also been a consultant in that world, and I’ve seen the inefficiency that makes you shake your head. This isn’t a system anyone would have designed or chosen; it’s one we’ve fallen into. But let’s talk about rail.

Rail: Speed, but Also Coverage

AM: In some circles I’m the most hated man in Canada, because I’ve expressed strong skepticism about Project Alto — high-speed rail from Toronto to Quebec City, by way of Trois-Rivières, Montreal, Ottawa, and, God save us, Peterborough. One reason I think that project is ill-founded is that, to have strong high-speed rail between major centres, you need strong regional rail feeding those hubs; otherwise you’re just replicating, at great expense, the air travel we already have. So I was intrigued to read your proposal, because you seem to be proposing that Ontario, on the provincial side, build out that regional rail network — not just commuter rail into Union Station, but something much grander. Say more about your vision for Ontario rail.

EL: I’ll plug it: if you go to ericforolp.ca/HSR, you can explore the proposal. Let me share it here so you can see it.

I put this together because our premier, Doug Ford, has proposed spending upwards of $100 billion to tunnel four extra lanes under Highway 401 — already the widest highway segment in North America, I believe. I wanted to ask: what could we build with that kind of money if we put it into high-speed rail and made reasonable cost assumptions? So here’s an overview — the travel times you’d expect between destinations, and the cost assumptions for stations and for each segment. An expensive segment like Pearson to Toronto runs $170 million per kilometre, because it’s either viaduct or, in places, underground; over flat land in southwestern Ontario, it’s closer to $75 million. For reference, Project Alto assumes $60–90 million per kilometre, at least in its public announcements. You’ll also see the speed assumptions — a top speed of 320 to 350 kilometres an hour, which again aligns with Alto. The assumptions are all in the “why” section, along with the station costs I’d expect. Otherwise, you can explore the map. Anything shown as a dotted line isn’t included in my cost assumptions; everything else is. So for the segment coming through Peterborough to Toronto, I’ve actually added a stop at Oshawa — I’ll get to why — and I’ve assumed all the additional costs from Oshawa to Toronto and across the rest of southern Ontario.

Two things need to happen with Project Alto. First, we shouldn’t be talking only about the Quebec City–to–Toronto segment, because then we’re back to treating it as a project: we’ve hired a consortium, and that consortium now says it’ll be six years before shovels are in the ground. What we should pursue is the network we want over the next 25 years, and then get started fastest on the fastest segment. Look at how Spain built its high-speed rail: they didn’t start with Barcelona–Madrid, because it was more complex. They started with a simpler segment — Sevilla–Madrid, I believe — so they could learn and keep building toward the longer-term network. Second, you need to continue it to Windsor. I know some business cases suggest that doesn’t make sense, but over a hundred years of provincial economic development, it absolutely does.

The real value of a network like this is connecting the Greater Golden Horseshoe into one contiguous mega-region, from an economic and labour-mobility perspective. Right now, if you work in Kitchener–Waterloo, it’s an hour’s drive to Hamilton — some people do it, but it’s rare for these two centres, with about a million people around each, to participate in each other’s labour markets. With a connection, you could get between them in about 20 minutes, and that opens a huge world of opportunity. Same for Oshawa to the Pearson economic zone, the second-largest employment zone in the entire country. And the Toronto–Pearson connection matters because it links people to the airport, and might resolve some of our other Toronto debates. So the logic is: you can go from Ottawa to a tourist destination like Niagara Falls; from London, be sipping wine in Niagara-on-the-Lake in an hour and a half; live in Barrie and commute to Hamilton in reasonable time. Everyone in the region can reach far more employers, which means they can bargain for better jobs without the cost of moving — and employers can locate in Hamilton, Kitchener, or Oshawa and still attract talent from across the region. The whole prospect of building and investing in Ontario gets better.

AM: This plan appeals to me, again, because of my heterodoxy on rail. I would much rather have a good, fast, reliable connection between Toronto and Kitchener–Waterloo than to Montreal — because I already have a way to get to Montreal or Ottawa quickly: I take a plane. There’s no quick way to get from Waterloo to Toronto at 7:30 in the morning for a 9 a.m. meeting. You either drive — and you’re really rolling the dice — or you don’t go. It’s uncertain and unreliable, and there’s no alternative, because GO service doesn’t help. This would give the people of Ontario something they don’t have right now.

EL: I’ve heard the objection: doesn’t GO Regional Express Rail solve some of this? It’s not solving the same problem. High-speed rail makes the destination-to-destination speeds incredibly quick; what you want alongside it is strong local transit connected to it, so accessibility around each place is better too. Everywhere we put a major high-speed line, we should also be thinking about the local rapid-transit networks. Then it stops making sense to take the 400 down to the 403 from Barrie to Hamilton — you’d never do it, because it’ll always be faster and cheaper to take the train, then a bus, then maybe an Uber for the last mile. This unclogs so many of our roads, which is a huge problem in Ontario, and it creates incentives for investment and all the other things that make people’s lives more convenient.

I’ll make one more point: I’ve proposed an alternative to Project Alto here, too. What we want is to connect more of the province itself. The existing 401 corridor, which also carries part of the VIA line, has several smaller centres you can run regional service to. We should have the mindset that we’ll run both regional and express services on this network. Then it becomes possible to work in Ottawa and live in Kingston, because you only need to go in every two or three days. Interconnecting this region will probably see more traffic than a line from, say, Windsor to Montreal — and I don’t think that logic exists right now in how we think about these projects.

AM: My view — that regional rail matters more than intercity high-speed rail, that the one depends on the other — hasn’t been popular. But your plan is exactly what I’ve been arguing for; it’s very exciting. Let me add, since you raised it, that this is entirely complementary to GO electrification, which turns the GO system from a commuter rail into more of a “surface subway” — I don’t love the term, but it’s the one people use — with much shorter headways and more in-between stations, without increasing travel time, which allows for a lot more infill development. GO electrification is, I’m obliged to quip, off the rails: badly overdue and expensive. What’s your diagnosis of the problem, and how would you fix it?

EL: The biggest problem in Ontario is the same across almost every sector: a distinct lack of curiosity, from our government and our leaders, about the problems they’re tasked with solving. A month or two ago, both Prabmeet Sarkaria, our Minister of Transportation, and Doug Ford were asked about the tripling of transit infrastructure costs in Ontario, and it was, “Yeah — COVID and inflation.” You’ve been responsible for this for years, and you haven’t spent any time thinking about why these things don’t work? Here’s the problem: Metrolinx doesn’t have a true independent mandate. Most projects are contrived in ministry offices first, before they go to that supposedly separate institution. When you don’t have a mandate for institutional independence to actually make decisions, you get a lot of indecision — what I call decision-based evidence-making. And that ends up as squandered cost and time. So there has to be political will and pressure to make decisions, and to push decision-making down to independent institutions so they can move faster.

AM: I have to be circumspect, so I won’t go into detail, but from someone who was on the inside: your diagnosis is correct.

EL: One of the funny things about becoming an advocate — first with More Neighbours Toronto, and then chairing Build Toronto, the growth-oriented arm of the Build Canada movement here — is that people who are part of these systems come forward. I’ve talked to consultants and public servants. Even the consultants say, “People blame us for the costs, but we’d rather be doing twice as much; half of what we do is bureaucratic compliance nonsense, and we don’t get to do the things we want to focus on.” With More Neighbours, people would come to me from the Ministry of Municipal Affairs and Housing, or from the City of Toronto: “Can you advocate for this? It drives me nuts, but I can’t put my name to it.” I see my campaign as a platform for everyone who sees the dysfunction and knows the solutions, but who hasn’t seen anyone willing to spend the political capital to make the change. I’m running to do exactly that.

AM: So GO electrification is part of a root-and-branch reform of Metrolinx.

Road Pricing

AM: Let’s move from transit to another matter: road pricing. I’m on record calling it the number one policy no one wants to implement but everyone loves once it’s in effect. I avoid “congestion pricing”; I don’t even like “decongestion pricing”; I prefer “road pricing.” What’s your view?

EL: When I chaired Build Toronto, we published a memo on congestion pricing. For a while I thought I might avoid going on the record in support of congestion pricing. Then I realized the cat was out of the bag and I’d be attacked with it anyway, so I might as well defend a good idea. What we should focus on is the outcome people want: to be able to drive places reliably. If you brought in a pricing regime that still left people stuck in traffic, you’d get a lot of anger. But done properly, you won’t get all the traffic. So I’d employ congestion pricing in the GTHA, on our highways, to the extent needed to maintain a minimum speed of 50 kilometres an hour. That makes the highways their highest and best use at the times people need them. I recognize there are people who rely on driving for their jobs and their work, and lots of lower-income Ontarians who don’t have much choice but to drive — and there are things we can do for them. But what gets missed is that people don’t just value their money; they value their time, and we put no priority on that. A business paying a trucker $30 to $50 an hour, who’s going to spend an extra hour crossing the GTA, would have needed to pay maybe $20 to get there twice as fast. The economics work for those users too: they get to do more, move more.

Everywhere congestion pricing has been tried, it has worked, and everywhere it’s been tried, people have been loath to take it away. There’s a challenge here because of how people feel about the 407. The price shock on the 407 has made a lot of people angry, because it’s become a privileged highway. But here’s what people don’t appreciate: if we bring in congestion pricing that’s much lower than the 407 on our other highways, the 407 won’t be able to charge its steep prices to lure people off the rest of the network — so even the 407 will be forced to compete and get more competitive. What we should also learn from the 407 is that there’s private demand to own and operate highways. So for projects like the Bradford Bypass or the 413 — which have attracted a lot of controversy about whether they’re worth the government spending money on — I think the province should do the pre-permitting and set the design standards, but if the CPP or a pension fund wants to own and build a competitor to the 407, I’d love nothing more than for them to have the right to do that.

AM: I’ve written about this too, and I’m entirely in favour of managed lanes of the sort you’re describing. You just must build into the contract what you said: that the purpose is to set rates as low as possible while keeping the highway moving. If it’s just revenue collection, it leads to perverse incentives. But if the goal is to keep the lanes moving — you didn’t say it, but I assume this charge would be dynamic. At 3 a.m. on a Monday it’s low or zero, because there’s no demand; at 3 p.m. on a Thursday, as rush hour begins, it’s quite different. It reflects local conditions.

EL: Yes. The price should only reflect what’s required to keep the road moving at a minimum of 50 kilometres an hour. That’s it. People ask whether you’d use the money to reinvest in transit — yes, broadly, as part of the pool, but the goal isn’t to generate revenue for transit. The goal is mobility: making sure people can rely on the speed of the highway network.

AM: It’s not a revenue centre; it’s a transport system that needs to be managed to be most efficient as a transport system. We’re all technocratic wonks here, so we’re 100% on board with road pricing as a tool to move people and goods most efficiently.

Driving Automation

AM: Let’s talk about automation — probably the topic my readers are most interested in. We’ve flirted with automated vehicles in Toronto. We’ve got Gatik, quietly running automated trucks for Loblaw in parts of the city. We’ve got Waabi, which isn’t really doing anything here but was founded here, with big plans for Texas and elsewhere. Magna ran a pilot that was embarrassing and didn’t succeed. We had delivery robots briefly in the Distillery District before we kicked them out — it’s never been clear why, other than that they were different from the status quo and therefore mistrusted. But you lived in San Francisco while Waymo was building up, so you have a completely different perspective. What’s your take on automated vehicles, and what role would you like them to play in Ontario’s mobility system?

EL: Let me take a step back. With generalist, autonomous robotics — not just vehicles; I’ll use “robotics” — it’s almost like 1895: we’re seeing the combustion engine at the World’s Fair, but no one thinks they’re selling their horse and buggy any time soon. By 1915 there’s a mass trend toward the automobile. That’s where we are with robotics generally. And I don’t think there’s a jurisdiction with more of a right to win in this space than Ontario over the next 30 to 50 years. If we operate from fear, we’ll miss an enormous opportunity. Why? We have five or six factors in our favour.

First, we genuinely have a major centre of AI in this province. A decade ago, on the Deloitte Fast 500 fastest-growing companies in North America, Canada had about 10%, matching our share of the population. This past year we had 23%, and over 15% of the total were in Ontario — because the sector is here.

Second, we have a financial capital that augments it; New York and San Francisco can claim that too, but we have other things they don’t.

We have a resource supply chain in Northern Ontario — critical minerals and more — that feeds the physical supply chain for automotive, machinery, and mechanical engineering.

We have an automotive industry far larger than almost any competitor’s.

And we have a clean electricity grid; if we get electricity, state capacity, and industrial policy right, it could be the most competitive energy sector in North America by far.

All of this means that if we understand what it takes to produce robotic systems — vehicles and machines — for export, Ontario could be the Western leader. And we have maybe five years to capture the opportunity.

So Ontario needs to change how it permits the use of autonomous machines — vehicles, robotics; heck, I think it would be charming to have park robots that maintained our public spaces better. We need to be deliberate about permitting the ones already operating, as most of the world does, and we should be publicly clear that we’re willing to procure generalist robotics for other uses in our society. We need to move our regulatory system from “no, with exceptions” to “yes, with exceptions.” That’s the grand goal of how I view robotics, in vehicles and everywhere else.

AM: Recently, Mayor Chow of Toronto, her press secretary remarked that Waymo is only welcome in the city to the extent it won’t contribute to Uber drivers losing their jobs. So the attitude is guilty until proven innocent: this technology is dangerous, and until we can defang it of any threat to anyone, it’s not welcome. To me this is a quintessentially Canadian attitude: we prefer not to take risks, not to do anything that could, as Eli Dourado likes to say, make anyone uncomfortable or cost anyone a job. How do you challenge that broader fear of the future that seems so common here?

EL: If we meet the future with too much fear, we’ll never realize the opportunities it offers, or the jobs it could create. Whenever I hear this, I remind people that in 1850, 95% of us were farmers. Do you want to go back and work the fields? No. We now have a far greater diversity of more rewarding, higher-value, higher-productivity jobs, because we freed ourselves from low-value labour. That didn’t mean we ran out of food; it meant we got better at producing it with less human input, so we can consume more, and our material well-being goes up. So when we say we can’t say yes to Waymo, what that means is that fewer people can get around the city, because Uber has to pay its drivers to drive. With autonomous vehicles, the cost of mobility comes down a lot — which means more choice, more commerce, all the things that add to our lives.

In economics there’s a great example from the semiconductor industry: the Jevons Paradox. In the 1990s, as the cost of manufacturing semiconductors fell dramatically, there was a panic that everyone would eat each other’s lunch and demand would run out. But as the cost of compute fell, the demand for compute rose even faster — and the result was more jobs and more economic activity. When we have this mindset of protecting the past, we miss the future, and we miss how lives and material well-being actually improve. It’s a small-minded, here-and-now approach. Canada wasn’t always like this. We used to believe in progress, in development, in the future. Now we believe in putting up every barrier we can so that nobody is upset by change. That has to change.

AM: I talk a lot to American audiences, and they ask, “What about the people who lose their jobs?” And I want to say: that’s why you need a strong social safety net, so that a person’s worth, dignity, security, and future aren’t bound up in a particular job, and they can transition. Then I remember I’m talking to Americans, and I bite my tongue. But in Canada we have a strong social safety net. So — I won’t put words in your mouth — I believe there’s enough prosperity to be gained that we can tax the winners to make the losers whole, and that it’s a better deal than trying to keep the future out. If you try to protect what you have, you lose it; if you try to build something more, you gain even more besides, especially for those left behind.

EL: What people often don’t appreciate — and I’m a true liberal; I believe deeply in competition in a highly entrepreneurial economy, so that you generate wealth you can tax fairly to provide a level of human welfare befitting an advanced society — is why competition matters here. In uncompetitive markets, the incremental value of the investment and innovation you’d need to hire someone for isn’t as great, because you can bet your competitors won’t make the same investment. So when capacity opens up and you can lower your costs with AI, you don’t need to repurpose the people you had. But in competitive sectors, the extra value you get by automating a less valuable process means you can spend it where human labour creates the next increment of value, ahead of your competitors. So if you want to avoid mass layoffs and turmoil from AI, the first thing you need is a competitive economy that requires labour to take advantage of these benefits and get ahead. That means almost everything we produce in Ontario — a good or a service — gets better for the same cost, without killing jobs. That has to be understood, and I don’t think it’s appreciated enough.

Rural Areas, Remote Areas, and the North

AM: You’re running for Premier of Ontario — not of Toronto, not of the Golden Horseshoe. It’s easy to talk about mobility in the context of the Greater Golden Horseshoe, or even southern Ontario. But what’s your mobility vision for Northern Ontario? You’ve spoken about the Ring of Fire and critical minerals — how are Northern Ontarians going to get around in the future, if you have your way?

EL: A couple of things. Northern and rural Ontario never really recovered robust transit service after Greyhound shut down and subsidies for rural bus operations declined. In the long term, my high-speed rail network — connecting places like Kitchener, and building hubs like London and Windsor — would let us better serve the regions around them, and create more demand from the rest of the country and Ontario to come visit our beautiful lakefront and tourist towns. Then there’s the legacy rail network, currently owned by CN and CP. I think one of the greatest crimes in our history was selling off the land under those corridors, which has limited our ability to decide what public uses they serve. We’ve just started restoring the Northlander service to Timmins. I’d like us to look at running rail through Northern Ontario — say from North Bay to Sudbury, connected even to Thunder Bay. It wouldn’t be fast initially, but there are overnight and daytime services some people would certainly take. People don’t appreciate how many rail lines still exist to serve the mining industry up north. So there’s an opportunity to run service networks to these places — as long as we figure out what happens once you arrive locally. For some of these smaller Northern centres, it’s also about helping them diversify out of the boom-and-bust cycle and attract more kinds of people who want to live and work there, and connectivity matters for that.

Here’s the controversial part. We need to be deliberate about identifying the corridors we want to be public, or at least to have public priority on, that CN and CP own today. To get them to the table, we’ll need to be much bolder. I’ve worked in M&A for years, and sometimes you have to put a bold line on the table and recognize that you hold more cards than you think. So if CN and CP won’t come to the table on amicable use and prioritization — with consistent expectations and standards — for the passenger services I’d like to restore, then we’ll use, or threaten to use, the power of eminent domain. They’ll have the choice: an amicable agreement where they keep ownership, or potentially lose it entirely and go figure out with their investors what matters to them. Unless we’re willing to go there, they have no reason to work with us. So I’m willing to go quite far to make sure these corridors — which are ultimately a public good for trade — can do what our outcomes require.

AM: I’ve worked in a small way with the Canadian Class I railways, CN and CPKC — Canadian Pacific Kansas City, or whatever they’ve rebranded themselves to. Their attitude is, “Oh, your country was founded in 1867? We’re older than that. Back off, punk.” So you’ll need to bring a similar swagger to those negotiations to succeed. And there’s a lot to what you say. As I’ve written before, you really can’t run freight and passenger rail on the same corridor; they’re at odds. A central problem of mobility is that different uses on the same infrastructure always creates friction. There are ways to resolve it, but the easiest is different uses on different infrastructure. There aren’t many corridors where you can run passenger rail on abandoned rights-of-way; the first-best solution is to acquire the corridor, get it up to a higher-speed standard, and run it.

EL: For Northern Ontario, I don’t have all the answers about the viability of sharing the freight corridors. It’s not the ideal long-term solution, but we want the right to build on them, so that where we need separated sections to manage traffic, we can. These are long distances, and rebuilding everything wouldn’t be viable at the cost, so sometimes you accept the trade-offs. If we value keeping the North connected, this is one way to do it, alongside additional passenger service.

One more thing: our Northern highways are quite dangerous, because most are winding, with one lane in each direction. There are about 1,700 kilometres of the Trans-Canada like that, and you see dramatic numbers of deaths in winter, and closures that last a long time, with a lot of freight on the corridor. We’ve debated twinning the entire corridor for a long time, which is expensive, and there’s merit in it. But the first thing is to make it a national goal — a long-term program — and to recognize that on some segments, other options make sense, like “two-plus-one” designs with alternating passing lanes and some separation, so winter driving is far less dangerous. Funny enough, even though that’s only one less lane, it costs about five times less. So you can deliver better connectivity and safer driving for Northern Ontario faster and far more cheaply than going for perfect. We need to work with the federal government on that.

Energy and Nuclear

AM: Before we wrap up, I can’t resist throwing you one more question. Don’t give me the full pitch, but what’s the short version of your vision for Ontario’s energy economy?

EL: We should be producing — and consuming — the most electricity per capita anywhere in the world. The way to get there is to think about energy development as a system too. I’m very inspired by how the French built their nuclear advantage: they set nuclear incentives, identified plenty of future sites, did the permitting and environmental review, chose one reactor design, and copied and pasted it to bring down the capital cost. I’d have the province take responsibility for the capital cost of baseload power, which should be nuclear and hydro, and let the private sector handle variable demand — renewables and battery storage, which have a pricing mechanism built in. I’d bring the permitting for all of this up to the provincial level. Right now, to permit a battery-storage site you go through the municipal process, and everyone reinvents the wheel. I want one provincial system for rapid permitting, with pre-identified sites, so the private sector has a clear signal of where it can move, and quickly — which also reduces risk. That’s the generation side.

Then there’s consumption. We spend too much time telling people to save energy and not enough telling them to consume more. In a city — Ottawa, where I am today, or Toronto, where I live, or many others — about 90% of emissions come from heating buildings and moving people and goods. The alternatives are electric heating and cooling, and electric mobility, and both need a lot more electricity. If you own a home built in the 1980s, you might have only a 100-amp connection. Say you want an electric vehicle, a heat pump instead of your furnace, and to convert it into a multiplex — you’ll likely need a 400-amp connection. If the transformer on your street isn’t upgraded, the utility says, “You’re making us do this upgrade; give us $100,000 so we can provide that power,” and your neighbours won’t have to pay for the incremental upgrade — they’ll just apply and pay the $5,000–10,000 for their own increased connection. I’d like a clear mechanism to amortize those infrastructure upgrades over time through higher consumption, so our utilities have a clear incentive: when they upgrade a neighbourhood, they tell people, “Your neighbourhood has been upgraded — and here’s how you can consume more electricity. You’re ready, and here’s how.” We should be conditioning people to think about increasing consumption, not the other way around.

The last piece is managing the transmission infrastructure itself. If you’ve followed Ontario politics, the energy sector has talked about the “smart grid” for as long as I’ve been alive — and it’s still not that smart. That means we use a lot of grid infrastructure at very low utilization, because we don’t know its overall health. Going forward, the Ontario Energy Board needs a mandate to put smart monitoring on all of it, so we can move a transformer from, say, 30% capacity to 60%. Then we’ll know where the points of failure are and where the priorities for upgrades are, and we can sweat the assets we already have, which brings down the cost of what we’ve already built. There are a lot of components to electricity, but it all matters, because electricity will be the most important economic and environmental input of the future by far — and it’s an area where Ontario has a tremendous potential comparative advantage. We can’t miss the boat on getting it right.

In Conclusion

AM: A future of abundant electricity, abundant mobility, and abundant housing — I’m all for it. Our time has to come to a close, so let me throw you a more fanciful question. Say you somehow become premier tomorrow, and you get to do one mobility or transport thing in your first year. What is it? To give you time to think, I’ll tell you mine: I’d come up with some neutral way to classify what a higher-order transit system is, and then classify every higher-order transit stop, and then say that if you’re constructing a building such that the front door is 300 metres or less from an access point to that station, there are no more height limits — by fiat of the province. Let the market decide how much housing there should be next to a higher-order transit stop. Admittedly fanciful, but what’s yours?

EL: It’s funny, because you mentioned land use — and if it weren’t transport-specific, that would probably be my answer too. But for me it’s not glamorous: it’s the institutional reform to change how we build. If you get that right, and can build at globally competitive cost and to higher standards, it doesn’t much matter what you decide to build next. If you don’t fix that fundamental problem, nothing else matters. So I want the institutional reform. Get how we build right, and “what we build” just means building more.

Share

AM: In other words — abundance. Building abundantly. That’s an exit line if ever I heard one. Eric Lombardi, thank you very much for your time today.

EL: Thank you. And for everyone listening and reading: stay tuned to my campaign. If you’re in Ontario, sign up as a member of the Ontario Liberal Party — it’s free, and you can vote in November. And when the campaign launches, if you’re open to it, consider a donation, because what I’m doing here is challenging, and it isn’t free.

Thank you so much for your time, Andrew. It was a pleasure.

Long-time readers will note that Changing Lanes usually publishes longform work on Tuesdays and Off-Ramps on Thursdays, but this week we’re switching it up. I am confident you will find Thursday’s piece exciting and interesting, but to accommodate its subject, I am holding off on it for two days. When you read it, you will understand why.

Hard Fork revisits Interesting Times

Earlier this year I appeared on the New York Times podcast “Interesting Times with Ross Douthat” to discuss the state of driving automation in 2026 and where the field is going. I’m flattered that the team at Hard Fork, the NYT’s biweekly tech podcast, liked that conversation so much that they rebroadcast it under their own banner.

If you’ve already listened to my appearance with Ross, this won’t be new, but on the off chance you missed it, please enjoy it now, in a new format.

The Hub: Toronto Gets It Wrong, Again, This Time on Waymo

I wrote in The Hub yesterday on Toronto’s double standard on Waymo: the city is willing to let robotaxis in, but—as per the mayor’s press secretary—only if the firm proves that no jobs will be lost, no jobs will pay less wages than they do now, and no jobs become more precarious.

That’s a ridiculous standard to hold, as applying it consistently would mean that any innovation affecting any labour market would be subject to a veto by the workers it displaces.

For more, read the whole thing.

Investment Wars: Planning and Investing for an Automated World

On the most recent episode of the Investment Wars podcast, I joined host Joe Halpern to discuss a key problem for capital allocators: in a world where automation is clearly going to transform how we move people and goods, how should planners and investors prepare for change? How should they hedge against it? And what are some ways that air, rail, marine, and truck travel are likely to improve in future?

This was a good talk, and I hope that you enjoy it. It is available on Spotify, Apple Podcasts, and YouTube.

Radio New Zealand: How New Zealand should think about driving automation

I was pleased to appear on Radio New Zealand on the drivetime programme Afternoons with Jesse Mulligan to discuss how, specifically, New Zealand’s regulators and citizens should think about driving automation; the distinction between Tesla and Waymo; why Wellington and Christchurch are going to benefit from the technology first; and other matters.

Driving automation is a global technology, and it’s useful to think about how it will apply differently in places that aren’t North America or China, so I was grateful to Jesse for the chance to speak with him about how New Zealand specifically should prepare.

This went out over terrestrial radio, but a recording of our interview is available here.

Upcoming Events at Changing Lanes

Finally, two upcoming items.

Firstly, our next livestream! On Wednesday, 24 June at 1200h ET, my guest will be the foremost American authority on regulating automated driving: Professor Bryant Walker Smith of the University of South Carolina. Smith helped develop the SAE’s “five levels” of driving-automation taxonomy, and earlier this year he testified to Congress on how the United States should regulate self-driving. Subscribers are notified when we go live; you can also sign up here.

Second, our upcoming price change! The subscription rate for Changing Lanes goes up next week on Tuesday, 16 June, rising from the current rate of US$10/month or US$100/year to US$13/130. This means that now is the best time to change your subscription from free or monthly to annual, since switching now locks in a full year of content at the best possible rate. It’s as easy as clicking this button:

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