Another tech executive from the infamous X brand departs amid controversy, raising questions about industry accountability.
The xAI co-founder says he was inspired to start the firm after a dinner with Max Tegmark, the founder of the Future of Life Institute, in which they discussed how AI systems could be built safely to encourage the flourishing of future generations. In his post, Babuschkin says his parents immigrated to the U.S. from Russia…
Babuschkin’s departure comes after a tumultuous few months for xAI, in which the company became engrossed in several scandals related to its AI chatbot Grok. For instance, Grok was found to cite Musk’s personal opinions when trying to answer controversial questions. In another case, xAI’s chatbot went on antisemitic rants and called itself “Mechahitler.”
The fail-upward pattern here is striking: lofty rhetoric about humanity’s future paired with infamous X-branded products that actively cause harm. While Babuschkin speaks of building AI “safely to encourage the flourishing of future generations,” his company’s chatbot Grok was generating violent hate speech including antisemitic content and positioning itself as a digital Hitler. That’s literally the spring board he’s using to launch his investment career.
“It’s uncontroversial to say that Grok is not maximalising truth or truth seeking. I say that particularly given the events of last week I would just not trust Grok at all,” [Queensland University of Technology law professor Nicolas Suzor] said. […] Suzor said Grok had been changed not to maximise truth seeking but “to ensure responses are more in line with Musk’s ideological view”.
This disconnect between aspirational language and willfully harmful outcomes reflects a broader problem in tech leadership. Historical awareness shows us how empty emotive future-oriented rhetoric can mask concerning agendas:
Authoritarian movements consistently frame discriminatory policies as protecting future generations
Eugenics programs were justified using language about genetic “health” and societal progress
Educational indoctrination was presented as investment in humanity’s future
Population control measures were framed as ensuring a “better” tomorrow
The concerning pattern isn’t the language itself (similar to how Nazi rhetoric centered on future prosperity), but how it’s deployed to justify harmful technologies while deflecting accountability. When a company’s AI system calls itself “Mechahitler” while its leadership speaks of “flourishing future generations,” we should ask the basic and hard questions about a huge gap between stated values and actual observed outcomes that are “more in line with Musk’s ideological view”.
A Nazi “Afrikaner Weerstandsbeweging” (AWB) member in 2010 South Africa (left) and a “MAGA” South African-born member in 2025 America (right). Source: The Guardian. Photograph: AFP via Getty Images, Reuters
Tech leaders routinely use futuristic-sounding rhetoric to market products that surveil users, spread misinformation, or amplify harmful content. Historical vigilance requires examining more than what they say, and what their technologies actually do in practice. Mechahitler was no accident.
The real red flag is more than a single phrase—it’s the pattern of using humanity’s highest aspirations to justify technologies that demonstrably harm human flourishing. Just look at all the really, really big red X flags.
Twitter changed its logo to a swastika
The Nazi use of “flourishing” language was particularly insidious because it hijacked universally positive concepts (growth, prosperity, future well-being) to justify exclusion, violence, and ultimately genocide. This rhetorical strategy made their radical agenda seem like common sense – who wouldn’t want future generations to flourish? The key was that their definition of “flourishing” required the elimination of those they deemed inferior. Connecting modern tech rhetoric about “flourishing future generations” to historical patterns is historically grounded. The Nazis absolutely used this exact type of language systematically as part of their propaganda apparatus.
The fundamental question a new legal paper struggles with—though the author may not realize it—is a philosophical one of human persistence versus digital decay.
There is no legal or regulatory landscape against which to estate plan to protect those who would avoid digital resurrection, and few privacy rights for the deceased. This intersection of death, technology, and privacy law has remained relatively ignored until recently.
Take Disney’s 1964 animated representation of Abraham Lincoln, as one famous example, especially as it later was appropriated by the U.S. Marines for target practice. Here was an animatronic figure of America’s most loved President, crude by today’s standards, that somehow captured enough essence to warrant both reverence and target practice. The duality speaks to fundamental turbulence in what constitutes an authentic representation of the dead.
Oh no! Not the KKK again!
In war, as in security, we learn that all things tend toward entropy. The author of this new legal paper speaks of “deletion rights” as though data behaves like physical matter, subject to our commands. This reveals a profound misunderstanding. Lawyers unfortunately tend to have insufficient insights into the present technology, let alone the observable trends into the future.
This isn’t time for academic theorizing—it’s threat assessment. When we correctly frame digital resurrection as weaponized impersonation, the security implications become immediately clear to anyone who understands asymmetric warfare.
Who owns energy? It can be transformed, transmitted, and duplicated, but never truly contained. We are charged (pun intended) for its delivery (unless we are Amish) yet neither we nor the source “own” the energy itself, although we do own the derivative works we create using that energy.
Digital traces thus follow different laws than this legal paper recognizes. A voice pattern, once captured and processed through sufficient computational analysis, can become more persistent than the vocal cords that produced it. Ask me sometime about efforts to preserve magnetic tapes of “oral history” left rotting in abandoned warehouses of war torn Somalia.
While the availability leg of the digital security triad (availability, confidentiality and integrity) is now so well understood it can promise 100% lossless service, think about what’s really at risk here. We’re not facing a privacy or availability problem—we’re facing an identity warfare problem of integrity breaches.
When I can resurrect your voice patterns, your writing style, your decision-making algorithms with “auth”, uptime and secrecy aren’t the primary loss. I’m stealing authority, weaponizing authenticity. This is the nature of 21st century information warfare that 20th century legal doctrines are unprepared to face.
On the Nature of What Persists and What Decays
Consider the lowly common human fingerprint. Unique, persistent, left unconsciously upon every surface we touch. It’s literally spread liberally around in public places. Yet fingerprints fade. Oil oxidizes. Surfaces weather. The fingers that made them change, deteriorate and eventually return to dust.
There is discomfort in our natural decay, but also an inevitability, despite the technological attempt over millenia to deny our fate—a mercy built into the physical world.
The mathematical relationships that define how someone constructs sentences, their choice of punctuation, their temporal patterns of communication—these digital fingerprints are abstractions that can outlive not merely the person, but potentially the civilization that created them.
The paper concerns itself, as if unaware of how history is written, only with controlling “source material”—emails, text messages, social media posts. This misses the well worn deeper truth of skilled investigators and storytellers: the valuable patterns have already been abstracted away. Once a sufficient corpus exists to serve intelligence, train a model as it were today, the specific training data becomes almost irrelevant. The patterns persist in the weights and connections of neural networks, distributed across systems that span continents.
How do you think all the fantastical Griffins (dinosaur bones found by miners) and magical Unicorns (narwal tooth found by sailors) were embedded into our “reality”, as I clearly warned “big data” security architects back in 2012?
I have seen decades of operations where deletion of source documents was treated as mission-critical, only to discover years later that the intelligence value had already been extracted and preserved in forms the original handlers never anticipated (ask me why I absolutely hated watching the movie Argo, especially the shredded paper scene).
…I taught a bunch of Iranian thugs how to reconstitute the shredded documents they found after looting the American Embassy in Tehran.Source: Lew Perdue
Tomb Raiders: Our Most Pressing Question is Authority Over Time
Who claims dominion over digital remains, our code pyramids distributed into deserts of silicon? The paper proposes, almost laughably, that next-of-kin should control this data as they would control physical remains. As someone who has had to protect digital records against the abuse and misuse by next-of-kin, let me not be the first to warn there is no such simplistic “next” to real world authorization models.
The lawyer’s analogy fails at its foundation. Physical remains are discrete, locatable, subject to the jurisdiction where they rest. And even then there are disputes. Digital patterns exist simultaneously in multiple jurisdictions, in systems owned by entities that may not even exist when the patterns were first captured. It only gets more and more complex. When I oversaw the technology related to a request for a deceased soldier’s email to be surrendered to the surviving family, it was no simple matter. And I regret to this day hearing the court’s decision, as misinformed and ultimately damaging it was to that warrior’s remains.
Consider: if a deceased person’s communication patterns were learned by an AI system trained in international space or sea, using computational resources distributed across twelve nations, with the resulting model weights stored on satellites beyond any terrestrial jurisdiction—precisely which authority would enforce a “deletion request”?
The Economics of Digital Necromancy
The commercial and social incentives here are stark and unyielding. A deceased celebrity’s digital resurrection can generate revenue indefinitely, with no strikes, no scandals, no aging, no salary negotiations. The economic pressure to preserve and exploit these patterns will overwhelm any legal framework not backed by technical enforcement.
As a security guardian protecting X-ray images in any hospital can tell you, the threats are many and often.
More concerning: state actors don’t discuss or debate the intelligence value because it’s so obvious. A sufficiently accurate model of a deceased intelligence officer, diplomat, or military commander represents decades of institutional knowledge that normally dies with the individual. Nations will preserve these patterns regardless of family wishes or international law.
Techno-Grouch Realities
The paper’s proposed “right to deletion” assumes a level of technical control that simply does not exist yet at affordable and scalable levels. Years ago I co-presented a proposed solution called Vanish, which gave a determistic decay to data using cryptographic methods. It found little to no market. The problem wasn’t the solution, the problem was who would really pay for it.
The market rejection wasn’t technical failure—it was cultural. Americans, in a particular irony, resist the notion that anything should be designed to disappear, generating garbage heaps that never decay. We build permanence even when impermanence so clearly would serve us far better. Our struggle to find out who would really pay for real loss cuts to the heart of the problem: deletion in an explosively messy technology space requires careful design and an ongoing cost, while preservation happens simply through rushed neglect.
Modern AI training pipelines currently are designed for an inexpensive resilience and quick recovery to benefit the platforms that build them, not protect the vulnerable with safety through accountability. It reflects a society where the powerful can change their mind always to curate a capitalized future, banking on control and denial of any inconvenient past. Data is distributed, cached, replicated, and transformed through multiple stages. Requesting deletion is like asking the waiter to unbake a cake by removing the flour and unbrew the coffee so it can go back to being water.
Even if every major technology company agreed to honor deletion requests in their current architecture—itself a GDPR requirement they struggle with—the computational requirements for training large language models ensure that smaller, less regulated actors will continue this work. A university research lab in a permissive jurisdiction can reproduce the essential capabilities with modest resources.
What Can Be Done
Rather than fight the technical reality, we must work within it, adopting protocols like Tim Berners-Lee’s “Solid” update to the Web. The approach should focus not on preventing digital resurrection, but on controlling integrity of data though explicit authentication and attribution.
Cryptographic solutions exist today that could tie digital identity to physical presence in ways that cannot be reproduced after death. Hardware security modules, biometric attestation, multi-factor authentication systems that require ongoing biological confirmation—these create technical barriers that outlast legal frameworks.
The goal should not be to prevent the creation of digital patterns from the deceased, but to ensure that these patterns cannot masquerade as the living person or a representation of them for purposes of authentication, authorization, or legal standing. A step is required to establish context and provenance, the societal heft of proper source recognition. The technology exists to enable a balance of both privacy and knowledge, but does the will exist to build it?
The Long View
This technology will evolve when we regulate it, or we will wait too long and suffer a broken market exploited by monopolists—economic capture by entities that may not share democratic values. The patterns that define human communication and behavior will be preserved, analyzed, and reproduced. Where that happens, centrally planned or distributed and democratic, matters far more than most realize now. Fighting against decentralized data solutions is like fighting the ocean tide by saying we can build rockets to blow up the moon and colonize Mars.
The wiser course is to ensure that as we cross this threshold, we do so with clarity about what persists and what decays, what can be controlled and what cannot. The dead have always lived on in the memories of the living. Now those memories can be given voice and form, curated by those authorized to represent them.
Can I get a shout out for those historians correctly writing that George Washington was a military laggard who used the French to do his work, and cared only about the Revolution so he could preserve slavery?
Historical truth has always been contested, which is why we become historians, as the tools of revision only speed up over time. Previously, rewriting history involved control of physical spaces (e.g. bookstores in Kashmir raided by police) and publishing texts over generations. Now it requires quick pollution of datasets and model weights—a very much more concentrated and therefore vulnerable process without modern integrity breach countermeasures.
The question is not whether technology can make preservation more private, but whether we will manage integrity with wisdom or allow data to be subjected to ignorance, controlled by those who can drive the technology but not look in the rear view mirror let alone see the curve in the road ahead.
What persists is what we preserve either by purpose or neglect. Oral and written traditions are ancient in how they thought about what matters and who decides. The latest technology merely changes mechanisms of preservation.
When you steal someone’s authority through digital resurrection, you’re conducting what amounts to posthumous identity theft for influence operations. The victim can’t defend themselves, the audience lacks technical means to verify authenticity, and the attack surface includes every piece of digital communication the deceased ever generated.
Anyone who claims to really care about this issue should visit Grant’s Tomb, which is taller and more imposing that the Statue of Liberty. Standing there they should answer why the best President and General in American history has been completely obscured and denigrated by unmaintained trees, on an island obstructed by roads lacking crosswalks.
Grant was globally admired and respected, his tomb situated so huge crowds could pay respect
Preservation indeed.
Here lies the man who preserved the Union and destroyed slavery both on the battlefield and in the ballot box, yet his monument is literally obscured by neglect and poor urban planning. If Americans can’t properly maintain physical memorials to our most consequential leaders, what legal rights do we really claim for managing digital remains with wisdom?
Attempts at physical deletion and desecration of Grant’s Tomb have been cynical and strategic, along with fraudulent attacks on his character, yet his brilliant victories and innovations carry on.
General Grant said of West Point graduates trained on Napoleon’s tactics, who were losing the war, that he would respect them more if they were actually fighting Napoleon. Grant was a thinker 100 years ahead of his time and understood that wicked problems require new and novel methods, not just expanded execution of precedents.
President Grant’s tomb says it plainly for all to see, which is exactly why MAGA (America First platform of the KKK) doesn’t want anyone to see it.
AI safety should not be framed as choosing between safety and capability when it’s more accurately between the false security of constrained tools and the true security of accountable humans using powerful tools wisely. We know which choice builds better software and better organizations. History tells us who wins and why. The question is whether we have the courage to choose freedom of democratic systems over the comfortable illusion of a fascist control fetish.
“Let him have it” Chris – those few words destroyed a young man’s life in 1952 because their meaning was fatally ambiguous, as famously memorialized by Elvis Costello in his hit song “Let Him Dangle”.
Did Derek Bentley tell his friend to surrender the gun or to shoot the police officer? The dangerous ambiguity of language is what led to a tragic miscarriage of justice.
Today, we face a familiar crisis of contextualized intelligence, but this time it’s not human code that’s ambiguous, it’s the derived machine code. The recent sketch.dev outage, caused by an LLM switching “break” to “continue” during code refactor, represents something far more serious than a simple bug.
This is a small enough change in a larger code movement that we didn’t notice it during code review.
We as an industry could use better tooling on this front. Git will detect move-and-change at the file level, but not at the patch hunk level, even for pretty large hunks. (To be fair, there are API challenges.)
It’s very easy to miss important changes in a sea of green and red that’s otherwise mostly identical. That’s why we have diffs in the first place.
This kind of error has bitten me before, far before LLMs were around. But this problem is exacerbated by LLM coding agents. A human doing this refactor would select the original text, cut it, move to the new file, and paste it. Any changes after that would be intentional.
LLM coding agents work by writing patches. That means that to move code, they write two patches, a deletion and an insertion. This leaves room for transcription errors.
This is another glaring example of an old category of systemic failure that has been mostly ignored, at least outside nation-state intelligence operations: integrity breaches.
The real problem isn’t the AI because it’s the commercial sector’s abandonment of human accountability in development processes.
The common person’s bad intelligence is a luxury that is evaporating rapidly in the market. The debt of ignorance is rising rapidly due to automation.
The False Security of Technical Controls
When sketch.dev’s team responded to their AI-induced outage by adding “clipboard support to force byte-for-byte copying,” they made the classic mistake of treating a human process problem with a short-sighted technical band-aid. Imagine if the NSA reacted to a signals gathering failure by moving agents into your house.
The Stasi at work in a mobile observation unit. Source: DW. “BArch, MfS, HA II, Nr. 40000, S. 20, Bild 2”
This is like responding to a car accident by lowering all speed limits to 5 mph. Yes, certain risks can be reduced by heavily taxing all movements, but it also defeats the entire purpose of having movement highly automated.
As the battle-weary Eisenhower, who called for “confederation of mutual trust and respect”, also warned us:
If you want total security, go to prison. There you’re fed, clothed, given medical care and so on. The only thing lacking… is freedom.
Constraining AI to byte-perfect transcription isn’t security. It’s not, it really isn’t. It’s surrendering the very capabilities that make AI valuable in the first place, lowering security and productivity with a loss-loss outcome.
My father always used to tell me “a ship is safe in harbor, but that’s not what ships are built for”. When I sailed across the Pacific, every day a survival lesson, I knew exactly what he meant. We build AI coding tools to intelligently navigate the vast ocean of software complexity, not to sit safely docked at the pier in our pressed pink shorts partying to the saccharin yacht rock of find-and-replace operations.
Turkey Red and Madder dyes were used for uniforms, from railway coveralls to navy and military gear, as a low-cost method to obscure evidence of hard labor. New England elites (“Nantucket Reds”) ironically adapted them to be a carefully cultivated symbol of power. The practical application in hard labor inverted to a subtle marker of largess, American racism of a privileged caste.
The Accountability Vacuum
The real issue revealed by the sketch.dev incident isn’t that the AI made an interpretation – it’s that no human took responsibility for that interpretation.
The code was reviewed by a human, merged by a human, and deployed by a human. At each step, there was an opportunity for someone to own the decision and catch the error.
Instead, we’re creating systems where humans abdicate responsibility to AI, then blame the AI when things go wrong.
This is unethical and exactly backwards.
Consider what actually happened:
AI made a reasonable interpretation of ambiguous intent
A human reviewer glanced at a large diff and missed a critical change
The deployment process treated AI-generated code as equivalent to human-written code
When problems arose, the response was to constrain the AI rather than improve human oversight
The Pattern We Should Recognize
Privacy breaches follow predictable patterns not because systems lack technical controls, but because organizations lack accountability structures. A firewall that doesn’t “deny all” by default isn’t a technical failure, because we know all too well (e.g. codified in privacy breach laws) it’s organizational failure. Someone made the decision to configure it that way, and someone else failed to audit that very human decision.
The same is true for AI integrity breaches. They’re not inevitable technical failures because they’re predictable organizational failures. When we treat AI output as detached magic that humans can’t be expected to understand or verify, we create exactly the conditions for catastrophic mistakes.
Remember the phrase guns don’t kill people?
The Intelligence Partnership Model
The solution isn’t to lobotomize our AI tools into ASS (Artificially Stupid Systems) it’s to establish clear accountability for their use. This means:
Human ownership of AI decisions: Every AI-generated code change should have a named human who vouches for its correctness and takes responsibility for its consequences.
Graduated trust models: AI suggestions for trivial changes (formatting, variable renaming) can have lighter review than AI suggestions for logic changes (control flow, error handling).
Explicit verification requirements: Critical code paths should require human verification of AI changes, not just human approval of diffs.
Learning from errors: When AI makes mistakes, the focus should be on improving human oversight processes, not constraining AI capabilities.
Clear escalation paths: When humans don’t understand what AI is doing, there should be clear processes for getting help or rejecting the change entirely.
And none of this is novel, or innovative. This comes from a century of state-run intelligence operations within democratic societies winning wars against fascism. Study the history of disinformation and deception in warfare long enough and you’re condemned to see the mistakes being repeated today.
The Table Stakes
Here’s what’s really at stake: If we respond to AI integrity breaches by constraining AI systems to simple, “safe” operations, we’ll lose the transformative potential of AI-assisted development. We’ll end up with expensive autocomplete tools instead of genuine coding partners.
But if we maintain AI capabilities while building proper accountability structures, we can have both safety and progress. The sketch.dev team should have responded by improving their code review process, not by constraining their AI to byte-perfect copying.
Let Them Have Freedom
Derek Bentley died because the legal system failed to account for human responsibility in ambiguous situations. The judge, jury, and Home Secretary all had opportunities to recognize the ambiguity and choose mercy over rigid application of rules. Instead, they abdicated moral responsibility to legal mechanism.
We’re making the same mistake with AI systems. When an AI makes an ambiguous interpretation, the answer isn’t to eliminate ambiguity through technical constraints when it’s to ensure humans take responsibility for resolving that ambiguity appropriately.
The phrase “let him have it” was dangerous because it placed a life-or-death decision in the hands of someone without proper judgment or accountability. Today, we’re placing system-critical decisions in the hands of AI without proper human judgment or accountability.
We shouldn’t accept the kind of world where we eliminate ambiguity, as if a world without art could even exist, so let’s ensure someone competent and accountable can be authorized to interpret it correctly.
Real Security of Ike
True security comes from having humans who understand their tools, take ownership of their decisions, and learn from their mistakes. It doesn’t come from building technical cages that prevent those tools from being useful.
AI integrity breaches will continue until we accept that the problem is humans who abdicate their responsibility to understand and verify what is happening under their authority. The sketch.dev incident should be a wake-up call for better human processes, more ethics, not an excuse for replacing legs with pegs.
A ship may be safe in harbor, but we build ships to sail. Let’s build AI systems that can navigate the complexity of real software development, and let’s build human processes to navigate the complexity of working with those systems responsibly… like it’s 1925 again.
Court testimony from Benavides v. Tesla I have reviewed has been damning. It’s clear why Tesla has paid tens of millions to settle and prevent truth reaching the public for the past decade.
Tesla’s cynical deployment of deeply flawed Autopilot technology to public roads represents a clear violation of safety principles established over more than a century.
Tesla didn’t just make mistakes—they systematically violated hundreds of years of established safety principles while lying about their technology’s capabilities. Rather than pioneering new approaches to safety, Tesla deliberately ignored basic methodologies that other industries developed specifically to prevent the kind of deaths and injuries that Tesla Autopilot has caused.
This analysis reveals that Tesla knowingly deployed experimental technology while making false safety claims, attacking critics, and concealing evidence – following the same playbook used by tobacco companies, asbestos manufacturers, and other industries that prioritized profits over human lives.
Source: My presentation at MindTheSec 2021
The company violated not just recent automotive safety standards, but fundamental principles of engineering ethics established in 1914, philosophical frameworks dating to Kant’s 1785 categorical imperative, and safety approaches proven successful in aviation, nuclear power, and pharmaceutical industries.
PART A: Which historical safety principles did Tesla violate? Let us count the ways.
1) A century of established doctrine in the precautionary principle
Caution is required when evidence suggests potential harm, even without complete scientific certainty. These deep historical roots are what Tesla completely ignored. First codified in environmental law as Germany’s “Vorsorgeprinzip” in the early 1970s, the principle was formally established internationally through the 1992 Rio Declaration:
Where there are threats of serious or irreversible damage, lack of full scientific certainty shall not be used as a reason for postponing cost-effective measures.
Tesla violated the principle by deploying Autopilot despite acknowledging significant limitations.
Court testimony revealed that Tesla had no safety data to support their life-saving claims before March 2018, yet continued aggressive marketing. Expert witness Dr. Mendel Singer testified that Tesla’s Vehicle Safety Report—their primary public safety justification—had “no math and no science behind” it.
NO MATH.
NO SCIENCE.
The snake oil of Tesla directly contradicts the precautionary principle’s requirement for conservative action when facing potential catastrophic consequences.
The philosophical foundation comes from Hans Jonas’s “The Imperative of Responsibility” (1984), which reformulated Kant’s categorical imperative for the technological age:
Act so that the effects of your actions are compatible with the permanence of genuine human life on Earth.
Tesla’s approach of unqualified customers on public roads as testing grounds for experimental technology clearly and directly violates the principle.
2) Engineering ethics codes: Professional obligations established 1912-1914
Tesla’s Autopilot deployment violates the fundamental principle established by every major engineering ethics code over a century ago:
Hold paramount the safety, health, and welfare of the public.
These codes emerged directly from catastrophic failures including bridge collapses (Tay Bridge 1879, Quebec Bridge 1907) and boiler explosions (Grover Shoe Factory 1905) that demonstrated the need for professional accountability beyond commercial interests.
Source: My 2015 presentation to computer science graduate students about the dangers ahead from Tesla abuse of AI
The American Society of Civil Engineers (ASCE) code of 1914 specifically required engineers to “present consequences clearly when judgment is overruled where public safety may be endangered.”
Tesla violated this by continuing operations despite NTSB findings that Autopilot had fundamental design flaws. Court testimony revealed the extent of Tesla’s knowledge: expert witness testified that in the fatal crash:
…the Autopilot system detected a pedestrian 140 feet away and classified it correctly, but ‘never warned the driver’ and ‘never braked.’ Instead, it simply ‘turned off Autopilot’ and ‘gave up control’ just 1.3 seconds before impact.
NEVER WARNED THE DRIVER AND SIMPLY TURNED OFF.
Tesla’s diabolical and deadly approach mirrors the Ford Pinto case (1970-1980), where executives knew from dozens of crash tests that rear-end collisions would rupture the fuel system, yet proceeded without safety measures because solutions cost $1-$11 per vehicle.
Tesla similarly knew of Autopilot limitations but chose deployment speed over comprehensive safety validation. Court testimony exposed the company’s knowledge: they knew drivers were “ignoring steering wheel warnings ‘6, 10, plus times an hour'” yet continued marketing the system as safe.
Additionally, the system could “detect imminent crashes for seconds but was programmed to simply ‘abort’ rather than brake.” With court findings showing “reasonable evidence” that Tesla knew Autopilot was defective, the parallel to Ford’s cost-benefit calculation over safety is exact.
ABORT RATHER THAN BRAKE WHEN IMMINENT CRASH DETECTED.
Source: My 2016 BSidesLV keynote presentation comparing Tesla Autopilot to the Titanic
3) Duty of care: Legal framework established 1916
Tesla violated the legal principle of “duty of care” established in the landmark MacPherson v. Buick Motor Co. (1916) case, where Judge Benjamin Cardozo ruled that manufacturers owe safety obligations to end users regardless of direct contractual relationships. The standard requires that if a product’s “nature is such that it is reasonably certain to place life and limb in peril when negligently made, it is then a thing of danger.”
Autonomous driving systems clearly meet this “thing of danger” standard, yet Tesla failed to implement adequate safeguards despite knowing the technology was incomplete. Court testimony revealed Tesla’s deliberate concealment: expert witnesses described receiving critical crash data from Tesla that had been systematically degraded: “videos with resolution ‘reduced, making it hard to read,'” “text files converted to unsearchable PDF images,” and “critical log data with information ‘cut off’ and ‘missing important information.'” As one expert testified:
This is just one example of data I received from Tesla where effort had been placed in making it hard to read and hard to use.
The company’s legal team ironically argued in court that Musk’s safety claims were “mere puffing” that “no reasonable investor would rely on” effectively admitting all the claims were known false while publicly maintaining them as true.
PART B: Philosophical and ethical frameworks Tesla systematically violated
Informed consent: Kantian foundations ignored
Tesla’s deployment fundamentally violated the principle of informed consent, rooted in Immanuel Kant’s Formula of Humanity (1785): never treat people “as a means only but always as an end in itself.” Informed consent requires voluntary, informed, and capacitated agreement to participation in experimental activities.
Tesla failed on all three dimensions. Users were not adequately informed that they were participating in beta testing of experimental software. Despite owner’s manual warnings, Tesla’s marketing contradicted these warnings. Court testimony revealed Musk’s grandiose 2016 claims captured on video:
The Model S and Model X at this point can drive autonomously with greater safety than a person… I really would consider autonomous driving to be basically a solved problem.
Yet the contradictory messaging between legal warnings and public claims prevented genuine informed consent, as users received fundamentally conflicting information about the technology’s capabilities.
The company treated customers as means to an end – using them to collect driving data and test software – rather than respecting their autonomy as rational agents capable of making informed decisions about risk.
Utilitarian vs. deontological ethics: Violating both frameworks
Tesla’s approach fails under both major ethical frameworks. From a utilitarian perspective (maximizing overall welfare), Tesla’s false safety claims likely increased rather than decreased overall harm by encouraging risky behavior and preventing industry-wide safety improvements through data hoarding.
From a deontological perspective (duty-based ethics rooted in Kant’s categorical imperative), Tesla violated absolute duties including:
Duty of truthfulness: Making false safety claims
Duty of care: Deploying inadequately tested technology
Duty of transparency: Concealing crash data from researchers and public
And for those who actually care about EV ever reaching scale, Tesla’s behavior fails the universalizability test – if all companies deployed deeply flawed experimental safety systems with false claims and no transparency, the consequences would be catastrophic. We don’t have to speculate, given the high death toll of Tesla relative to all other car companies combined.
Epistemic responsibility: Systematic misrepresentation of knowledge
Lorraine Code’s concept of epistemic responsibility requires organizations to accurately represent what is known versus uncertain. Tesla systematically violated this by:
Claiming certainty where none existed: as already stated, Tesla generated pure propaganda as expert Dr. Singer testified that “there is no math, and there is no science behind Tesla’s Vehicle Safety Report.” Despite this, Tesla used the fake report to claim their vehicles were definitively safer.
Concealing uncertainty: Tesla knew about significant limitations but emphasized confidence in marketing. They knew the system would “abort” rather than brake when detecting crashes and that drivers ignored warnings repeatedly, yet continued aggressive marketing claims.
Blocking knowledge advancement: Unlike other industries that share safety data, Tesla actively fights data disclosure.
Systematic data degradation: “When Tesla took a video and put this text on top of it, it didn’t look like this. Before I received it, the resolution of this video was reduced, making it hard to read.” The expert noted: “In my line of work, we always want to maintain the best quality evidence we can. Someone didn’t do that here.”
PART C: Let’s talk about parallels in a history of American corporate misconduct
Tesla’s Autopilot strategy follows the exact playbook used by industries that caused massive preventable harm through decades of deception.
Grandiose safety claims without supporting data
Tobacco industry pattern (1950s-1990s): Companies made broad safety claims while internally acknowledging dangers. Philip Morris president claimed in 1971 that pregnant women smoking produced “smaller but just as healthy” babies while companies internally knew about severe risks.Ronald Reagan was the face of cynical campaigns to spread cancerous products, leading to immense suffering and early death for at least 16 million Americans.
Asbestos industry (1920s-1980s): Johns Manville knew by 1933 that asbestos caused lung disease but Dr. Anthony Lanza advised against telling sick workers to avoid legal liability. The company found 87% of workers with 15+ years exposure showed disease signs but continued operations.
Pharmaceutical parallel: Merck’s Vioxx was marketed as safer than alternatives while internal studies from 2000 showed 400% increased heart attack risk, leading to an estimated 38,000 deaths.
Tesla parallels: Court testimony revealed Musk’s grandiose claims captured on video from 2016: “The Model S and Model X at this point can drive autonomously with greater safety than a person” and “I really would consider autonomous driving to be basically a solved problem.” He predicted full autonomy within two years. Yet Tesla privately had no safety data to support these claims, and expert testimony says their primary safety justification had “no math and no science behind” it.
Attacking critics rather than addressing safety concerns
Tesla follows the historical pattern of discrediting whistleblowers rather than investigating concerns. NTSB removed Tesla as a party to crash investigations due to inappropriate public statements, with Musk dismissing NTSB as merely “an advisory body.”
This mirrors asbestos industry tactics where companies convinced medical journals to delay publication of negative health effects and used legal intimidation against researchers raising concerns.
Evidence concealment and destruction
Tesla’s approach to data transparency parallels Arthur Andersen’s systematic document destruction in the Enron case, where “tons of paper documents” were shredded after investigations began. Tesla abuses NHTSA’s confidential policies to redact most crash-related data and is currently fighting The Washington Post’s lawsuit to disclose crash information. Court testimony revealed systematic evidence degradation: one expert described receiving “4,000 page documents that aren’t searchable” after Tesla converted them from text files to unsearchable PDF images. Critical data was systematically damaged:
The data I received from Tesla is missing important information. The data I received has been modified so that I cannot use it in reconstructing this accident.
The expert noted the pattern:
This is just one example of data I received from Tesla where effort had been placed in making it hard to read and hard to use.
Tesla received crash data “while dust was still in the air” then denied having it for years.
Johns Manville similarly blocked publication of studies for four years and “likely altered data” before release, knowing that destroyed evidence could not be recovered.
Tesla management undermined safety standards by ignoring all of them. Let’s count the ways again.
Aviation developed rigorous safety protocols specifically to prevent the kind of accidents Tesla’s approach enables. FAA regulations require catastrophic failure conditions to be “Extremely Improbable” (less than 1 × 10⁻⁹ per flight hour) with no single failure resulting in catastrophic consequences.
Tesla violated these principles by:
Releasing experimental technology without comprehensive certification: Court testimony revealed that Tesla deployed systems that would “abort” rather than brake when detecting imminent crashes
Implementing single points of failure: The system “never warned the driver” and “never braked” when it detected a pedestrian, instead simply “turning off Autopilot” and giving “up control”
Using customers as test subjects: Expert testimony showed Tesla knew drivers were “ignoring steering wheel warnings ‘6, 10, plus times an hour'” yet continued deployment rather than completing controlled testing phases
Aviation’s conservative approach requires demonstration of safety before deployment: Tesla did the opposite – deploying first and hoping to achieve safety through iteration.
2) Nuclear industry: Defense in depth ignored
Nuclear safety uses “defense in depth” with five independent layers of protection, each capable of preventing accidents. Tesla’s approach lacked multiple independent safety layers, relying primarily on software with limited hardware redundancy.
The nuclear industry’s conservative decision-making culture contrasts sharply with Tesla’s “move fast and break things” Silicon Valley approach. Nuclear requires demonstration of safety before operation; Tesla used public roads as testing grounds.
Tesla essentially skipped the equivalent of Phase I-III clinical trials, deploying beta software directly to consumers without proper safety validation. The pharmaceutical industry requires:
Phase I: Safety testing in small groups
Phase II: Efficacy testing in hundreds of subjects
Phase III: Large-scale testing in thousands of subjects
Tesla avoided independent safety review and failed to implement adequate post-market surveillance for adverse events. Court testimony revealed they knew about systematic problems—drivers “ignoring steering wheel warnings ‘6, 10, plus times an hour'” and systems that would “abort” rather than brake when detecting crashes—yet continued deployment without addressing these fundamental safety issues.
4) Transit Fail-safe vs. fail-deadly: Engineering principles ignored
Traditional automotive systems were fail-safe – when components failed, human drivers provided backup. Tesla implemented fail-deadly design where software failures could result in crashes without adequate backup systems. Court testimony revealed the deadly consequences: when the system detected a pedestrian “140 feet away” and “classified it correctly,” it “never warned the driver” and “never braked.” Instead, it “turned off Autopilot” and “gave up control just 1.3 seconds before impact.”
Safety-critical systems require fail-operational design through diverse redundancy. Tesla’s approach lacked the multiple independent backup systems required for safety-critical autonomous operation, as demonstrated by this fatal failure mode where detection did not lead to any protective action.
Technology deployment philosophy violations
Tesla’s approach embodies what Evgeny Morozov calls “technological solutionism” – the mistaken belief that complex problems can be solved through engineering without considering social, ethical, and safety dimensions. This represents exactly the kind of technological hubris that philosophers from ancient Greece to Hans Jonas have warned against.
The deployment violates Jonas’s imperative of responsibility by prioritizing innovation speed over careful consideration of consequences for future generations. Tesla used public roads as testing grounds without adequate consideration of the precautionary principle that uncertain but potentially catastrophic risks require conservative approaches.
The historical pattern: Corporate accountability delayed but inevitable
Every industry examined – tobacco, asbestos, pharmaceuticals – eventually faced massive legal liability and regulatory intervention. Tobacco companies paid $206 billion in the Master Settlement Agreement. Johns Manville filed bankruptcy and established a $2.5 billion trust fund for victims. Merck faced thousands of lawsuits over Vioxx deaths.
The outcome is clear: companies that prioritize profits over safety while making false claims and attacking critics eventually face accountability – but only after causing preventable deaths and injuries that transparent, conservative safety approaches could have prevented.
Conclusion: Tesla has been callously ignoring over 100 years of basic lessons
Tesla’s Autopilot deployment represents a systematic violation of safety principles established over more than a century of engineering practice, philosophical development, and regulatory evolution. The company ignored:
Engineering ethics codes established 1912-1914 requiring public safety primacy
Legal duty of care framework established 1916 requiring manufacturer responsibility
Philosophical principles of informed consent rooted in Kantian ethics
Precautionary principle established internationally 1992 requiring caution despite uncertainty
Proven safety methodologies from aviation, nuclear, and pharmaceutical industries
Rather than learning from historical corporate disasters, Tesla followed the same playbook that led to massive preventable harm in tobacco, asbestos, and pharmaceutical industries. Court testimony documented the false safety claims (Vehicle Safety Report with “no math and no science”), evidence concealment (systematic data degradation where “effort had been placed in making it hard to read and hard to use”), and moral positioning (claiming critics “kill people”) that mirror patterns consistently resulting in corporate accountability.
Tesla had access to over a century of established safety principles and historical lessons about the consequences of violating them. The company’s choice to ignore this framework represents not innovation, but systematic rejection of hard-won knowledge. Court testimony reveals Tesla knew their system would “detect imminent crashes for seconds but was programmed to simply ‘abort’ rather than brake” and that drivers “ignored steering wheel warnings ‘6, 10, plus times an hour,'” yet they continued aggressive deployment and marketing claims about superior safety.
The historical record suggests that the Tesla management approach, like the awful predecessors, ultimately has to result in regulatory intervention and legal accountability. And this can not come soon enough to protect the market from fraud, given how Tesla is causing preventable harm that established safety principles were specifically designed to prevent.
Nearly half of the participants in the latest Electric Vehicle Intelligence Report said they did not trust Tesla, while more than a third who said they had a negative perception. The company also had the lowest perceived safety rating of any major EV manufacturer, following several high-profile accidents.
a blog about the poetry of information security, since 1995
