#21: What the AI Industry Can Learn from the Automotive Industry

Last time, we discussed: What if Waymo worked like ChatGPT? We contrasted how self-driving vehicles are built (slow, safety-first) with how LLMs have grown (fast, scale-first). This time, let’s flip the question: What can the AI industry learn from the automotive industry?

The auto industry did move through a major transition: from wild experimentation and frequent harm to a mature system of safety norms, testing, regulation, and accountability. The AI world is still in its youth. We can speed our learning curve by seeing how cars tamed risk.

Why the metaphor holds and where it strains

First, let’s acknowledge the mismatch. Cars operate in the physical world: actions such as collisions, injuries, and deaths are observable and often immediately attributable. In contrast, the harms from AI tend to be hidden, delayed, or extremely subjective. A bad recommendation, a biased judgment, or emotional manipulation may never be traced back to the model or even recognized by the user.

In the automotive world, you can create statistics like deaths per vehicle-mile, or crashes per million miles driven. Those metrics anchor development, design, and draw public scrutiny. In AI, we lack similarly clean metrics. Harm can look like erosion of trust, misdiagnosis, or more. If users don’t know they were misled, how do we count it?

Still, the metaphor is powerful. The auto sector’s journey from chaotic risk to structured safety offers process lessons. Safety didn’t come from perfect technology; it came from institutions, rules, incentives, and enforcement. That architecture is what AI needs.

How cars got safe: key inflection points and mechanisms

To understand what AI can borrow, we must first walk through how the automotive industry evolved - not as a polished success story, but as a gritty, contested process.

Early danger, design by convenience

In the early decades of motoring, safety was barely a design priority. Speed, styling, comfort, power were the selling points. The idea that a vehicle should minimize harm in a crash was an afterthought.

Over time, engineers began introducing features like laminated safety glass (to prevent shattering) and crumple zones (to absorb impact). This is a timeline of automobile safety features released over time. Volvo famously built and shared the three-point belt design in 1959. But for decades, uptake was voluntary or uneven.

The Nader moment and public accountability

The turning point came politically and culturally. In 1965, Ralph Nader’s Unsafe at Any Speed exposed how automakers resisted safety improvements (e.g. seat belts) to preserve cost or aesthetics. The public reaction was fierce.

The Congress responded quickly. The Motor Vehicle Safety Act and Highway Safety Act passed in 1966 gave the federal government the authority to impose safety standards. Regulators could now require equipment, recall flawed parts, and hold companies to safety mandates.

Soon after, the NHTSA (National Highway Traffic Safety Administration) was established in 1970 and became the central safety regulator.

From mandates to rating systems and consumer pressure

Once minimum standards existed, the next evolution was competition above the floor. Enter NCAP (New Car Assessment Program). In 1979, NHTSA ran the first standardized crash test (35 mph frontal impact) and published the results. Over time, this matured into the star rating systems we know.

Independent crash-test agencies in Europe (Euro NCAP) pushed the bar further. Consumers began demanding high safety ratings, and automakers responded to that pressure by further improving safety.

Recall regimes and failure enforcement

Safety is not static, as new defects constantly emerge. Regulations evolved to include mandatory defect reporting and recall powers. In the U.S., the TREAD Act (2000) strengthened “early warning” obligations: manufacturers must report defects, incidents of injury or death, and recall campaigns worldwide.

When global scandals hit (ex. the Takata airbag crisis), the recall machinery kicked in, and companies faced fines, legal liability, and reputational damage. These enforcement levers forced real accountability.

Metrics, simulation, iteration

Crucially, automakers didn’t wait for tragedies to learn. They invested in millions of test miles, advanced simulation environments, and crash-test labs to discover edge cases before human lives were at stake. That iterative cycle of test → fail → fix → test again is the engine of safety.

Over decades these mechanisms combine: regulators, public pressure, ratings, recalls, and testing: not perfect, but cumulative. The result: safety features we now take for granted (seat belts, airbags, crash sensors, electronic stability control, pedestrian automatic braking) have become standard.

Safety: from externality to product attribute

We can think of the auto industry’s progress as a shift in how safety is treated:

• From nuisance / cost → a product attribute

• From afterthought → design constraint

• From voluntary to mandatory

• From opaque to transparent (ratings, recalls)

The AI industry is still in the phase of “safety as afterthought.” Many models ship, then are patched, then are silently updated. There is no universal standard for reporting harm, no public safety dashboards, no “AI recall” portal. The public mostly judges by dramatic failures.

To catch up, let’s imagine the lessons and analogues AI should adopt and then map the gaps and obstacles.

What AI can learn from automotive safety (and how to bootstrap it)

Here are actionable, concrete lessons that the AI industry could adopt - not just philosophy, but mapped to real analogues.

1. Create standardized harm metrics

In automotive, we have deaths per million miles, crash rate, injuries per 100,000 vehicles. AI needs something similar.

Some ideas:

• Harm incidence per million queries (with a taxonomy: medical error, defamation, self-harm encouragement, financial loss, privacy leak)

• Hallucination rate (false statements given as fact in factual queries)

• User complaints/reports per thousand sessions

These should be normalized, publicly reported, and auditable.

Caveat: safety benchmarks in AI sometimes correlate with capability improvements (i.e. “safetywashing”). A recent study “Safetywashing: Do AI Safety Benchmarks Actually Measure Safety Progress?” warns that if your safety metrics move in lockstep with model capability, they may be overfitting proxies rather than real safety.

So our metrics must be clearly defined, unable to be gamed, and tied to observable harm (not just internal scores).

2. Independent testing / AI NCAP

Just as NCAP crash-tests push automakers beyond regulatory minimums, the AI space needs trusted third-party evaluation:

• A consortium of academics, civil society, and industry bodies that run adversarial prompt batteries, scenario-based stress tests, red-teaming, jailbreak tests, and fairness audits. Most models are indeed doing external red teaming today but this can be more formalized and overseen by a central body.

• Publish simple safety ratings (e.g. 1–10 stars) and full technical reports with failure cases.

This gives consumers and procurement officers a way to compare models on safety, not just performance.

3. Mandatory incident reporting + public “recall” infrastructure

In the auto world, if defects are discovered, they must be reported to regulators, and recalls issued. In AI:

• Vendors should be legally obligated to report incidents above thresholds (e.g. “X users were misled in a medical domain with dangerous consequences”) in a public registry. CSET proposes a hybrid framework for AI incident reporting: combining mandatory, voluntary, and citizen reporting.

• A public “AI Recall / Patch Portal” where users or regulators can check a model’s incident history, patch status, vulnerability disclosures, and last audit dates.

• Empowered regulators (or standards bodies) should have authority to mandate fixes or disable features that prove unsafe.

4. Staged rollout and sandboxing

Rather than full public launches, models should follow controlled deployment phases:

1. Internal testing / red team

2. Canary limited release (e.g. to a subset of trusted users)

3. Wider but monitored release

4. Public general release

At each stage, safety metrics are monitored, thresholds enforced, and rollback options should exist. This mirrors how automakers simulate millions of miles pre-launch and only expose features gradually.

5. Liability, enforcement, and incentives

Safety only matters if there are consequences:

• Clear legal frameworks about vendor liability when a model causes foreseeable harm (except in cases of deliberate misuse).

• Fines, penalties, or reputational consequences when known defects are not patched.

• Incentives for safer models: safety certifications should carry competitive advantage in procurement (e.g. in government contracts, healthcare, education).

• Insurance frameworks: models with poor safety records cost more to insure or partner with.

6. Transparency & public dashboards

Automakers let you check recalls by VIN. AI models need something equivalent:

• A safety dashboard per model: incident counts, audit dates, active vulnerabilities, red-team scores, last patch date.

• Clear safety labels or spec sheets, stating limitations, failure modes, intended domain, and “do not use” warnings.

7. Feedback loops, iteration, and “safety as a first-class revision target”

Cars don’t magically get safe. Companies iterate, learn from defects, patch, simulate, and deploy again. AI firms must treat safety regressions as bugs, not “edge cases.” After each incident, the feedback loop should include:

• root-cause analysis

• systematic patching

• regression testing across large adversarial sets

• public postmortem disclosure (for non-sensitive cases)

Challenges and objections

Of course, the analogy has limits and the path isn’t frictionless. Let me share some likely objections and how they might be addressed.

• Definitional complexity. Many harms are subjective, context-dependent, or delayed. Constructing a universally accepted taxonomy is hard. (But other domains have tackled similar issues, e.g. medicine, finance.)

• Experimental freedom. Heavy regulation might stifle innovation, especially for smaller labs. That’s why phased access (sandbox -> limited -> general) is key.

• International fragmentation. Automotive safety converged slowly across countries; AI will need global cooperation (standards bodies, treaties).

• Gaming & adversarial adaptation. Models may optimize for passing the tests rather than solving safety. That’s why tests must evolve, be secret or randomized, and red-teaming must be continual.

• Cost & incentives. Building safety infrastructure (incident tracking, audits, dashboards) is expensive. But the reputational, legal, and competitive risks of ignoring it may exceed that cost.

What success would look like

If the AI industry internalized these lessons, we might see:

• A mature system where models are paired with safety ratings and incident histories

• Companies competing on trusted safety rather than just benchmarks

• Regulated obligations to patch known issues and report incidents

• Staged rollouts that reduce broad exposure to novel failure modes

• Clear liability rules that force care in deployment

• A culture of safety-as-feature rather than safety-as-addendum

In other words: AI would be treated less like a wild frontier and more like a high-stakes system where trust is earned.

Why this matters now

We are in the transition era. AI capabilities are accelerating. Many deployments are in high-stakes domains: healthcare, finance, moderation, education, legal counsel. Mistakes can cascade.

The AI safety community is increasingly aware of this urgency. Tools like the AI Safety Index are beginning to evaluate companies on “current harms, risk assessment, governance, information sharing, etc.” Future of Life Institute New proposals like STREAM aim to standardize how models report danger-specific benchmarks (especially in chemical/biological domains).

But without the scaffolding of measurement, audit, recall, and enforcement, these remain voluntary patches on a fragile foundation.

The automotive industry’s transformation gives us a roadmap - messy and contested, but real. Its legacy is not perfection; it’s the systemization of safety.