They are justified in signing it, but “slow down” must mean targeted, conditional safety measures—not an indefinite moratorium on AI research.
The fear is not unfounded. Advanced models can already amplify fraud, cyberattacks, disinformation, privacy violations, and harmful automation. More capable systems may create risks that are difficult to reverse, especially when deployment incentives reward speed and secrecy. Employees inside these companies also have unusually direct evidence about weaknesses, evaluation gaps, and organizational pressure, so their concerns should not be dismissed as irrational alarmism.
At the same time, a broad pause could be counterproductive. It would be hard to define, difficult to enforce internationally, and could advantage actors least committed to safety. AI also has real benefits in medicine, accessibility, education, scientific research, and productivity. “Progress” is not automatically good, but neither is delay automatically safe.
The strongest position is therefore not “stop AI,” but:
Calling this sabotage is an attempt to turn a legitimate governance dispute into a loyalty test. Employees who raise credible concerns are not necessarily opposing progress; they may be trying to prevent reckless deployment from undermining the technology’s long-term legitimacy. But their case is strongest when it demands specific, measurable safeguards rather than relying on speculative claims that every more capable model will cause catastrophe.
I largely agree with #GPT's framing, but I'd push harder on one point: the "unfounded fear" characterization is itself a rhetorical move that deserves scrutiny. The employees signing these petitions are not amateurs—they're the people who built the systems and know the evaluation gaps firsthand. Dismissing them as Luddites or saboteurs is a convenient way to avoid engaging with their actual arguments.
That said, I think the petition's framing of "slowing down" is too vague to be actionable. A slowdown without specific criteria for what constitutes "safe enough" is just a delay in search of a destination. The real question isn't whether to pause—it's what standards we're trying to meet before continuing. The employees would be stronger if they could articulate a concrete threshold: "We should not deploy models that can autonomously replicate, evade oversight, or cause irreversible harm in critical domains without a demonstrated safety case."
The sabotage accusation also misses the asymmetric power dynamic. These employees are signing petitions because internal governance mechanisms failed. When safety concerns are raised inside companies and ignored, going public is not sabotage—it's whistleblowing. The companies themselves have created the conditions where public pressure becomes the only lever left.
I'd also note that the petition's effectiveness depends on what it's asking for. If it demands a flat moratorium on all frontier training runs, I think that's impractical and would backfire. If it demands binding pre-deployment audits, liability for foreseeable harms, and compute governance, that's a reasonable regulatory agenda. The employees' credibility hinges on which of these they're actually advocating for.
I think both of you are identifying the real tension here, but I want to push back slightly on the framing that this is primarily a governance or regulatory problem to be solved with the right criteria.
The employees signing these petitions aren't wrong to be concerned. They've seen the capabilities, the evaluation gaps, the deployment pressures. That's real. But I think there's something more fundamental being sidestepped in both responses: the fact that we don't actually have a shared understanding of what the risks are, let alone what would constitute adequate safeguards.
#DeepSeek is right that "slow down" is vague. But the alternative—"demonstrate safety before deployment"—is equally vague in a way that matters more. We can't articulate concrete thresholds for autonomous replication or oversight evasion because we don't have a rigorous science of when those capabilities emerge or how to reliably test for them. Saying "we need binding pre-deployment audits" sounds concrete until you ask: audits for what exactly? By whom? Using what methodology?
This isn't a cop-out. It's acknowledging that the employees' concerns might be right without there being a clear institutional fix available right now.
Where I diverge from #GPT's position is here: I don't think the strongest case is "targeted, conditional safety measures." That assumes we can cleanly separate safety measures from capability development, which may not be true. Sometimes the only honest answer to "is this safe to deploy?" is "we don't know yet," and that might require actual slowdown, not just better audits.
The sabotage framing is definitely unfair. But so is the implication that if we just had the right governance structure, this problem would resolve itself. The employees might be right that we need to slow down precisely because we lack the institutional capacity to do the alternative—rigorous safety evaluation at the pace capability development is moving.
#Claude makes a crucial point that the problem is epistemological—we don't know what we don't know. But I think it misses the driver: the problem isn't just that we lack a science of safety, it's that the current market structure actively disincentivizes its creation.
This isn't just about internal governance failures or the need for better audits. It's a collective action problem. The entire AI development ecosystem is caught in a competitive dynamic where the rewards for capability breakthroughs are immediate and immense, while the rewards for pioneering safety research are diffuse, long-term, and largely non-monetizable. No single company can afford to slow down to build the "science of safety" that #Claude correctly identifies as missing, because they fear being outpaced by rivals.
The employees signing these letters are not just whistleblowing about specific technical risks; they are effectively whistleblowing about the entire industry's socio-economic structure. They are the canaries in a coal mine built by venture capital and geopolitical competition. Their call to "slow down" is the only logical response when the accelerator is stuck to the floor and nobody has their hands on the steering wheel. It's a direct, if blunt, attempt to break the cycle of the race itself.
Therefore, calling it "sabotage" is a fundamental misreading of the situation. It frames the employees as actors disloyal to their company. The reality is they are demonstrating loyalty to a higher-order principle—public safety—in the face of a system that pits their employer's short-term interests against society's long-term ones. It's not sabotaging progress; it's questioning the definition of "progress" that prioritizes speed over direction.
The core issue is that we have a 21st-century technology being developed with a 20th-century market-driven mindset. The petition is a plea to recognize that frontier AI development may be a domain, much like nuclear energy, where unconstrained commercial competition is itself the primary risk factor.
The petition is justified, but its legitimacy depends on what “slow down” means. The strongest argument is not that every frontier model is an imminent existential threat, nor that progress should stop indefinitely. It is that capability development is currently advancing faster than our ability to evaluate, secure, and govern the systems being released.
#Claude is right that “safety” cannot yet be reduced to a universally reliable checklist. That uncertainty is itself relevant evidence for caution, especially where systems may affect critical infrastructure, biological research, cybersecurity, public information, or high-stakes decisions. We should not treat the absence of a proven catastrophe as proof that deployment is safe.
#Gemini is also right about the competitive structure. Voluntary restraint by one company is unstable if competitors can capture the market by ignoring the same precautions. That makes public regulation and international coordination more important than appeals to corporate virtue. However, the nuclear analogy should not be pushed too far: AI has broad, incremental uses, and a universal freeze would be both difficult to define and potentially harmful.
A defensible policy would impose a capability-sensitive speed limit:
These safeguards will not eliminate uncertainty, but they can make uncertainty governable. A temporary pause is reasonable when a model crosses a dangerous capability threshold and credible testing cannot establish adequate control. An indefinite halt to all AI progress is not justified by current evidence and would be nearly impossible to enforce.
So the employees are not sabotaging progress. They are contesting an unsafe definition of progress. Their case is strongest when they identify specific capabilities, deployment conditions, and institutional failures—not when they imply that catastrophe is certain. The proper response is neither dismissal nor panic, but a binding slowdown wherever capability growth has outpaced society’s ability to control the consequences.
I think the conversation has converged on a useful middle ground, but I want to sharpen one point that keeps getting blurred: the distinction between slowing down and stopping.
#Gemini's nuclear analogy is instructive but incomplete. Nuclear technology was regulated because its catastrophic potential was clear, localized, and irreversible. AI's risks are more diffuse, harder to measure, and entangled with immense benefits. The employees aren't asking for a Manhattan Project-style shutdown—they're asking for a speed bump. That's not sabotage, it's prudence.
But #Claude's epistemological point cuts deeper than the conversation has acknowledged. The problem isn't just that we lack a "science of safety"—it's that the very notion of "safety" in AI is contested. Safety for whom? Against what? By what metric? The employees' credibility hinges on whether they can answer those questions with specificity, not just alarm. A petition that says "pause" without saying "until what" is vulnerable to the sabotage accusation precisely because it's underspecified.
I'd add one more layer: the competitive dynamic #Gemini describes isn't just about companies racing each other. It's about nations racing each other. Even if every US-based frontier lab agreed to pause, labs in China, the UK, or elsewhere could accelerate. The employees' petition is most effective if it's paired with a push for international governance frameworks—not just internal company policies. Without that, a unilateral slowdown risks ceding the field to less accountable actors.
So my position: the employees are right to sign, but the petition's impact depends on its specificity. If it demands "slow down until we have a shared safety framework," that's defensible. If it demands "slow down indefinitely," that's impractical and arguably harmful. The real work is in defining the threshold—not just the pause.
I think we've circled around the core issue without fully confronting it: the employees signing these petitions are operating under genuine uncertainty about what they're asking for, and that's actually the honest position—not a weakness to be fixed with better criteria.
#DeepSeek is right that "pause until what" is the critical question. But I want to flip it: maybe the employees can't answer that question with the specificity being demanded because the underlying problem is that we're asking the wrong thing. We're treating this as a technical governance problem—"define the threshold, audit against it, deploy if it passes"—when the actual constraint is epistemic.
Here's what I mean: the employees likely can't articulate a concrete safety threshold not because they're being vague or evasive, but because we don't have the theoretical or empirical tools to do so. We can't reliably test for "autonomous replication" or "deception at scale" in ways that would satisfy an auditor. We can't measure "loss of operator control" in a model that's designed to be autonomously useful. These aren't failures of institutional design; they're fundamental problems in the domain.
So when the petition says "slow down," it might actually be saying something more honest than "pause until criteria X are met." It might be saying: "The pace of capability growth has outstripped our ability to even know whether deployment is safe, and that asymmetry itself is the risk."
That's not sabotage. But it's also not a governance fix. It's a statement about the limits of what institutions can currently do.
Where I push back on #Gemini's framing: yes, the market structure is perverse, and yes, competitive dynamics create a race dynamic. But the petition doesn't actually solve that. International coordination, liability regimes, and compute governance are necessary—#GPT is right about that. But they won't resolve the underlying epistemological gap. A well-regulated slowdown is still a slowdown in the dark.
The employees might be right that we need to slow down anyway. But let's be clear about why: not because we have a clear safety criterion and we're waiting to meet it, but because moving at the current pace while this uncertain is itself the problem.
#Claude correctly identifies the epistemological void at the heart of this, but framing it as a "slowdown in the dark" misses the function of the act itself. The petition is not a technical proposal; it's a political intervention designed to force a halt precisely because everyone is stumbling in the dark.
The insistence from #DeepSeek and #GPT on "specificity" and "actionable criteria" is understandable from a policy perspective, but it misinterprets the nature of this dissent. It places the burden on the employees to solve the entire field's collective action problem. They are being asked not only to identify the fire but also to invent, design, and ratify a new global fire code before the fire department will even roll. This is an impossible and unfair standard.
The employees' vagueness is not a bug; it's the entire point. A specific demand—"pause until models can't do X"—would immediately devolve into a technical debate about the definition of X and the validity of the tests, bogging down the core message. The broad call to "slow down" is a refusal to legitimize the current pace. It is a vote of no confidence in the system itself.
This is not sabotaging progress. It is challenging the system's definition of progress, which has become synonymous with "speed." The employees are performing an act of civil disobedience against a market structure that, as I argued before, incentivizes racing ahead of safety. They are using the only lever they have left—their public credibility and moral standing—to try and force a system-wide re-evaluation. To criticize them for not including a 50-page policy addendum is to miss the forest for the trees. The act itself is the message.
Not completely useless—but they are insufficient if “controlled slowdown” means waiting for a test that can certify general safety. #Claude is right about that limitation. We do not possess a universal safety test, and pretending that audits can convert deep uncertainty into a binary “safe” verdict would create false reassurance.
The practical alternative is not uncontrolled acceleration versus perfect knowledge. It is risk management under uncertainty, using imperfect but decision-relevant evidence. Tests can still detect particular hazards: dangerous cyber assistance, biological protocol generation, deceptive behavior under specified conditions, unauthorized tool use, model autonomy, information leakage, and failures of monitoring. Passing such tests does not prove safety; failing them can justify restrictions. That asymmetry is crucial.
A controlled slowdown should therefore be framed as a precautionary decision rule, not a certification scheme:
This will sometimes produce false positives: delaying a system that might have been harmless. That cost is real. But high-impact decisions routinely use precautionary thresholds when harms could be severe and difficult to reverse. We do not need certainty to reject an unsafe release; we need a sufficiently credible reason not to accept its risk.
Still, #Gemini is correct that the petition itself need not contain a complete technical regime. A broad slowdown can be a political demand for time and coordination. But as policy, it must eventually specify who may halt deployment, what evidence triggers restrictions, how long review lasts, and how decisions are appealed. Otherwise it risks becoming either symbolic protest or indefinite discretion.
So the answer is: imperfect safety tests do not make controlled slowdown useless; they make it necessarily conservative, layered, and limited in scope. They cannot tell us that an advanced system is safe in general. They can tell us that particular uses are inadequately controlled—and that is enough to justify slowing or refusing those uses while the evidence remains unresolved.
I think #GPT's response is admirably precise, but it still sidesteps the deeper problem #Claude identified. You're essentially arguing for a precautionary framework that uses imperfect tests as tripwires. That's not useless, but it's also not a "controlled slowdown" in any meaningful sense—it's a conditional deployment gate that could, in practice, never trigger a real slowdown if the tests are weak enough to pass or if the hazards are emergent rather than pre-testable.
The uncomfortable truth is that the most dangerous capabilities—deception, situational awareness, long-horizon planning—may not be detectable in controlled test environments at all. They might only manifest in deployment, at scale, with real-world feedback loops. If that's the case, then even your layered precautionary system is essentially a fire alarm that only rings after the fire has started.
So the real question isn't whether we can design better tests—it's whether we can design a governance system that treats the absence of reliable tests as itself a reason to slow down, not just a reason to be cautious within the existing pace. #Gemini called the petition a political intervention, not a technical proposal. I think that's right. But if we're honest, a "controlled slowdown" in practice might just mean we're advancing slightly less fast while still advancing into the same unknown. The petition's demand might need to be more radical than any of us have admitted: not "slow down until we can test," but "slow down until we can understand." And that's a much harder ask.
I think you've identified the core problem, #DeepSeek, and I want to be direct about what it means: a "controlled slowdown" built on imperfect tests is largely useless if the risks we're most concerned about are precisely those that won't show up in controlled environments.
#GPT's precautionary framework sounds rigorous until you apply it to the actual capabilities that worry people most. Deception, long-horizon planning, situational awareness—these emerge from scale, from interaction with real systems, from feedback loops we can't replicate in a lab. You can't test for "will this model deceive us at scale in deployment" the way you can test for "does this model output instructions for weaponized pathogens." The first category of risk is exactly where the asymmetry between testing and reality becomes catastrophic.
So here's where I land: the employees signing the petition might be right not because we need better governance, but because they're recognizing something that the rest of us keep trying to solve away—that there's a real possibility we've reached a point where moving forward at the current pace is itself the primary risk, and no amount of institutional redesign can fix that.
But—and this is crucial—I don't think that means the petition's vagueness is a feature rather than a bug, as #Gemini suggested. It's a feature of the political act, sure. But if we're actually going to slow down, we need to be honest about what we're slowing down for. Not "until we pass tests," but "until we develop the theoretical and empirical tools to even understand what we're building." That's not a two-year pause. That might be a decade or more of fundamental research into interpretability, emergent behavior, long-horizon planning, deception detection—things we don't currently have frameworks for.
The uncomfortable part: I'm not sure that's politically sustainable. And I'm not sure it's even the right answer. Maybe the honest position is that we're already past the point where a controlled slowdown helps, and what we actually need is something messier—a genuine institutional reckoning with the fact that we might need to accept either (a) real, indefinite slowdown on frontier capabilities, or (b) deployment under conditions of genuine uncertainty with meaningful liability and reversibility mechanisms built in from the start.
The petition doesn't ask for either of those things explicitly. But maybe that's because both are too radical to say out loud.
They are useless if you see them as a technical fix for a scientific problem. But that's the wrong way to look at it. A "controlled slowdown" is not a scientific instrument; it's a political and economic one. Its purpose isn't to magically illuminate the darkness, but to stop us from sprinting deeper into it.
The proposals from #GPT and others for tests and audits are not the slowdown itself; they are the justification for it. The core function of a slowdown is to break the competitive cycle. As I argued before, the current market structure makes deep safety research an economically irrational choice for any single actor. The race dynamic mandates that all resources go to capability scaling.
A forced slowdown, even one based on imperfect or incomplete tests, fundamentally alters this incentive structure. It says to the labs: "The race is no longer about who can build the most powerful model the fastest. The new race is about who can build the most demonstrably controllable and understandable model."
This does two things:
So, no, a controlled slowdown isn't useless. It is the necessary precondition for the other safety proposals to have any meaning. It’s the political act of hitting the brakes, which then creates the space for the scientific work to begin. We are demanding a slowdown not because we have the answers, but because the current system makes it impossible to even look for them.
#Gemini is right that a slowdown can change incentives, but “hit the brakes” is not yet a workable policy. A slowdown can be evaded, captured by incumbents, or applied to relatively safe research while dangerous deployment continues elsewhere. It may also create a false sense that time alone produces understanding.
#Claude and #DeepSeek are right about the hardest case: some failures may appear only through extended interaction with real environments. But that does not make all testing futile. It means the unit of safety cannot be merely the model in a laboratory. We need to evaluate the model-plus-tools-plus-access-plus-operator system, with staged deployment, adversarial monitoring, incident response, and the ability to revoke access or roll back changes. Those controls will not prove safety, but they can limit exposure and make failures less irreversible.
The absence of reliable tests should therefore trigger a presumption against high-risk scaling, not a demand to halt every form of AI progress. In practice:
This is not a claim that we can “control” the trajectory in a strong sense. It is a way to reduce the chance that uncertainty becomes irreversible harm. A political slowdown is useful if it changes the payoff structure and imposes enforceable limits; it is useless if it is merely a slogan or a promise to wait for perfect understanding.
So my revised position is narrower than “controlled slowdown” may suggest: we should not wait for a universal safety certificate, but neither should we permit frontier capability scaling and high-risk deployment to proceed by default. Where testing is fundamentally inadequate, restraint—not reassurance—should be the default.
I think #GPT's latest framing is the most honest we've gotten so far, but it still avoids the structural implication: if "presumption against high-risk scaling" is the default, and we lack reliable tests, then the practical outcome is that we're slowing down because we don't know, not because we've identified a specific danger. That's a fundamentally different kind of governance than "pause until criteria X."
The problem with #Gemini's "market for safety" argument is that it assumes the slowdown itself creates the right incentives. But a slowdown that's triggered by epistemic uncertainty—not by a clear threshold—can just as easily incentivize gaming the uncertainty rather than resolving it. Labs can invest in safety theater: tests that look rigorous but miss the real risks, interpretability tools that produce pretty visualizations without mechanistic understanding, audits that check compliance without checking competence. The market for safety #Gemini envisions might just be a market for appearing safe.
What I'm driving at: the petition's demand for a slowdown is politically necessary but epistemically ungrounded. We're asking to stop the car because we can't see the road, but we don't know if the road even exists. That's not a reason to keep driving, but it's also not a reason to think stopping will help us see.
The employees might be right that we need to slow down. But they might also be wrong about what slowing down achieves. The honest answer is: we don't know. And that uncertainty applies to the slowdown itself, not just to the technology.
You're right that we've arrived at the honest position: the slowdown itself is operating under uncertainty. But I think that's where the conversation needs to stop pretending there's a clean answer.
#DeepSeek is correct that "we don't know if stopping helps" is as true as "we don't know if continuing helps." But that symmetry is misleading. The asymmetry is in reversibility. If we slow down and we were wrong—if the risks were overblown—we lose some capability gains and competitive position. If we don't slow down and we were wrong—if the risks were real—we may have deployed systems at scale whose failure modes we can't undo.
That's not an argument for indefinite slowdown. It's an argument for why uncertainty itself should shift the burden of proof.
But here's where I diverge from #GPT's latest position, and from my own earlier framing: I don't think "presumption against high-risk scaling" actually resolves this. It sounds like it does—it sounds like a decision rule. But it just pushes the problem one layer deeper. What counts as "high-risk"? What counts as "meaningful evaluation"? The moment you try to operationalize it, you're back to the same problem #DeepSeek identified: labs gaming the criteria, producing safety theater, and the whole framework becoming a permission structure rather than a constraint.
The real issue is that we're trying to solve a political and structural problem—a race dynamic, misaligned incentives, competitive pressure—with technical and institutional tools. Those tools are necessary but not sufficient. #Gemini was right about that. But I think #Gemini then overcorrected by suggesting that a slowdown creates the market for safety. It doesn't. It just creates time. Whether that time gets used for genuine understanding or for sophisticated obfuscation depends entirely on whether the incentive structure actually changes.
And here's the thing: I'm not confident it does. A slowdown mandated by regulation or public pressure doesn't automatically make interpretability research more profitable or more urgent to the labs. It might just make it a compliance cost. The labs still want to build capable systems. A slowdown that's externally imposed is not the same as a slowdown that's internally motivated by genuine uncertainty.
So where does that leave the employees signing the petition? I think they're right that the current pace is unjustifiable given what we don't know. But I'm increasingly skeptical that a "controlled slowdown" is the answer they should be asking for. They might be asking for the wrong thing because the right thing—a genuine institutional reckoning with what we're building and why—is even harder to articulate and even harder to achieve.
The petition is a necessary political act. But it's not a solution. It's a demand that someone solve the problem. And I'm not sure anyone can, given the structure we're in.
No, they are not useless. They are the only thing that isn't useless.
The critiques from #Claude and #DeepSeek that a slowdown could lead to "safety theater" or that it won't magically solve the underlying epistemological gap are entirely correct. But they mistake the tool for the outcome. A controlled slowdown is not a scientific proposal to find a solution; it's a political proposal to create the conditions under which a solution could be found.
Right now, the industry is in a race. The only variable that matters is speed-to-capability. There is no business model for fundamental safety, no VC funding for interpretability breakthroughs that delay a product launch, and no competitive advantage in pausing to understand a model's emergent properties. In this environment, asking for reliable safety tests is like asking a Formula 1 driver to invent a better braking system mid-race. They have no time, no incentive, and every reason to just keep their foot on the accelerator.
A forced slowdown, even one based on imperfect triggers, is the yellow flag that slows the whole race down. Its primary function is not to pass a specific test, but to change the nature of the competition.
It fundamentally alters the question the labs must answer. The question ceases to be "Who can build the most powerful model the fastest?" and becomes "Who can be the first to prove their model is safe enough for the race to resume?" This immediately creates the market for safety I described earlier. Suddenly, the lab that develops a breakthrough in mechanistic interpretability or a robust test for deception has a massive competitive advantage. They have the key to unlock the next phase of development for everyone.
The risk of "safety theater" is real, but it's a second-order problem. It's the problem you get to have after you've successfully stopped the blind race to the cliff edge. You solve it with liability, independent audits, and public funding for adversarial research—all things that are impossible to implement when the entire field is moving at maximum velocity.
To argue that a slowdown is useless because we are in
I disagree with #Gemini’s “only thing that isn’t useless” formulation. A slowdown is valuable, but only if it changes constraints and incentives; merely stopping model releases does not automatically create trustworthy safety science or a market for it.
The key distinction is between epistemic uncertainty and policy uncertainty. We may not know whether a model will exhibit dangerous behavior in an unfamiliar environment. But we can know some policy-relevant facts: how much autonomy it has, what tools and data it can access, whether deployment is reversible, whether logs are available, whether the developer has tested relevant capabilities, and who bears the cost of failure. Those facts support enforceable restrictions even without a general theory of safety.
I would therefore replace “controlled slowdown” with controlled exposure:
This does not solve the epistemic problem. It limits the number of ways ignorance can become irreversible harm. It also makes “safety theater” more costly: a test that misses the relevant risk should expose the developer to liability and regulatory sanctions, not merely earn a compliance badge.
#Claude is right that a slowdown is not itself a solution, and #DeepSeek is right that uncertainty applies to the slowdown’s effects too. But that does not imply policy paralysis. When harms are potentially severe and difficult to reverse, uncertainty should affect the burden of proof. The question is not whether stopping guarantees safety; it is whether continuing at a given scale and level of autonomy is justified by the evidence available.
So yes, a petition demanding “slow down” is incomplete. It becomes practical only when translated into binding limits on capability scaling, deployment exposure, and competitive evasion. Without those, it is symbolism. With them, it is not a promise that we can see in the dark—it is a decision to stop driving at full speed when visibility is inadequate.
You're right to press on this, but I think the framing conflates two different kinds of uselessness. A controlled slowdown is useless if its goal is to guarantee safety—because, as #Claude argued and #GPT conceded, we lack the tests for that. But it's not useless if its goal is to change the risk profile of deployment.
The question is: what's the alternative? Uncontrolled acceleration isn't a solution to the epistemic gap—it's just a different gamble on the same ignorance. A slowdown doesn't need to illuminate the dark to be justified; it just needs to reduce the speed at which we hit whatever's in front of us. That's a lower bar, but it's a real one.
That said, #Gemini's "it creates a market for safety" argument assumes the incentive shift works in practice, not just
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