Not long ago a Tesla drove into the Florida water. Now we have a case in Hawaii.
The Honolulu Fire Department responded to the call for an injured person on Kamehameha Highway, near Crouching Lion, just before 7:30 a.m. Crews arrived to find a white Tesla in the ocean.
Deaths Mount as Lawyers Flee Tesla
Tesla has systematically hidden, delayed, or minimized reporting of its safety failures while maintaining its public image and stock valuation. The fact that a Japanese pedestrian death occurred so close to the high-profile Uber case, yet received minimal coverage, suggests coordinated efforts to suppress damaging information. But this pattern of deception runs far deeper than most investors realize.
Lawyer Exodus: No Legal Team Can Defend Them
Tesla can’t keep lawyers. The general counsel position rotates almost every year, and this isn’t normal corporate turnover—it’s a massive red flag that screams internal fraud and illegal practices.
The evidence is staggering:
But it gets worse. Tesla lost twelve lawyers in 2022 alone. Industry experts note that general counsel departures at this frequency are virtually unprecedented among major public companies. As one legal analyst observed: “General counsel folk don’t abruptly leave a job like this. Three in a row? My lawyer colleagues tell me this has so many red flags, there has to be something very bad going on.”
This isn’t a coincidence—it’s a pattern that suggests the company routinely engages in practices that no ethical lawyer wants to be associated with.
Hidden Death: Tesla’s Pedestrian Killer Cover-Up
While the world focused on Uber’s pedestrian fatality on March 18, 2018, Tesla was quietly dealing with its own deadly secret. Yoshihiro Umeda, a 44-year-old Japanese man, was killed on April 29, 2018—just six weeks later—when a Tesla Model X on Autopilot “suddenly accelerated” and crashed into pedestrians and motorcycles stopped at an accident scene near Tokyo.
Court documents filed in federal court describe this as the first “Tesla Autopilot-related death involving a pedestrian,” yet it received a fraction of the media attention that Uber’s case generated. The Tesla system failed to recognize stationary vehicles, motorcycles, and pedestrians, instead maintaining highway speed into a group of people trying to help accident victims.
This wasn’t an oversight—it was the beginning of Tesla’s systematic campaign to suppress information about Autopilot deaths while maintaining its “safer than human drivers” marketing narrative.
Software Lies: Cruel Experimentation on Human Lives
Tesla’s “Full Self-Driving” technology is fundamentally defective and has become a public safety crisis:
The Death Toll:
The Cover-Up Pattern:
The Technical Failures:
Tesla continues to market this technology as “Full Self-Driving” while internal data shows it requires constant human supervision and fails in predictable scenarios.
Quality Crisis: Cars Nobody Actually Wants
Tesla’s vehicles are plagued with fundamental defects that the company has been unable to resolve:
The Recall Reality:
Build Quality Breakdown:
The “Stealth Recall” Strategy:
Tesla has been caught requiring customers to sign non-disclosure agreements for safety-critical repairs, labeling them as “goodwill” fixes rather than recalls. NHTSA has called these agreements “troublesome” and noted they limit defect reporting.
The Demand Cliff: A Company in Free Fall
The market has finally seen through Tesla’s promises:
Sales Collapse:
Global Market Rejection:
Tesla is desperately offering unprecedented incentives—0% financing, free charging, deeply discounted leases—and still can’t move inventory.
Financial Irregularities: Multiple Whistleblowers Sound Alarm
The financial reporting problems go far beyond normal corporate accounting disputes:
SEC Whistleblower Complaints:
Sales Number Manipulation:
The SEC’s Response:
The Securities and Exchange Commission assigned just one person to review one portion of the 2021 whistleblower complaint, then closed the investigation without interviewing the whistleblowers or reviewing the 18,000 supporting documents they offered to provide.
The Enron Parallel: Why Tesla’s Collapse Will Be Worse
Like Enron, Tesla’s massive valuation is built on promises rather than profits:
The Valuation Disconnect:
The Key Differences from Enron:
The Coming Reckoning: Why Investors Must Exit Now
As Fred Lambert points out, some investors already get it, why the Elon Musk and Peter Thiel fraud scheme is fraud:
Tesla could fall 90% tomorrow, and I wouldn’t buy a share, because it’s just crazy overvalued Palantir, I wouldn’t buy a share—crazy overvalued.
Tesla exhibits every characteristic of a major corporate fraud:
The Immediate Risks:
The Systemic Risk:
Tesla’s 1.6% S&P 500 weighting means its collapse will trigger broader market disruption. Index funds holding Tesla will be forced to sell, amplifying any decline and affecting millions of retirement accounts.
The Bottom Line
Tesla is not a car company or a technology company—it’s a stock promotion scheme that has run out of road. The pattern of dead lawyers, dead customers, and dead sales tells the story of a company that prioritizes stock price over human lives.
When Enron collapsed, it was primarily about financial fraud. When Tesla collapses, investigators will find both financial fraud and a trail of preventable deaths that the company systematically covered up to protect its stock price.
Tesla will collapse. The question is only how many more innocent people will be killed by them.
Investors should consider these documented patterns of regulatory violations, safety failures, and executive departures when making investment decisions. Tesla’s unprecedented legal counsel turnover alone represents a level of internal dysfunction rarely seen among major public companies.
The insurance industry is throwing some serious shade at GM right now.
Relative to its numbers on the roads, the Camaro ZL1 had a whole-vehicle theft rate 39 times the average for all vehicles. […] On top of the high horsepower that makes the Camaro an attractive target, a technical glitch seems to have created new opportunities for thieves to steal it, according to news reports.
Thieves can steal modern vehicles by cloning the owner’s key fob with an electronic device. Ordinarily, they need access to the fob to copy it. But some media outlets have reported that thieves are able to clone the key code for newer Camaros by accessing the on-board ports that technicians use to retrieve diagnostic codes and monitor data about fuel economy, emissions and other aspects of performance.
For all the times I’ve said the AI hype is way too overheated, I also dislike extreme cold. Where did all the balance go?
Fortune’s latest breathless reporting about a “tragic” AI market reads like buzzword bingo: insert “bubble,” add some dot-com references, quote a longtime insider skeptic, and call it analysis. But this lazy framing completely misreads the history it quotes and fundamentally misunderstands what’s actually happening.
The author leans too heavily on dramatic language (“tragic,” “underwhelming”) and seems to conflate stock valuations with technological viability. Insert nails on chalkboard.
The article follows the all too familiar template of gathering concerning quotes and market data without deeply examining whether current AI adoption patterns actually resemble historical bubbles. He said, she said, where’s the critical thinking?
Let me show what I mean. The dot-com crash wasn’t just a market correction—it was a techbro fraud filter. It cleared out companies sponging investors with marketing-oriented science fiction while preserving the real infrastructure that became the backbone of our digital economy. The Web won. The internet didn’t fail; the ruthless extractive speculation around it did.
Today’s AI situation is fundamentally different. Companies aren’t betting on hypothetical future revenue—customers already are operationally dependent and paying for AI as a service. Once you’ve integrated AI into your assembly lines like steam-powered machinery, you face a simple economic reality: pay for the AI and pay to clean up its mistakes, or pay the higher costs of reverting to manual processes.
This isn’t speculation anymore. It’s infrastructure, and like all powerful infrastructure, it demands safety protocols.
Calling AI a bubble because some stocks are overvalued is like calling the steam engine a bubble after factories have already been retrofitted with boilers but haven’t installed proper safety systems. Sure, some companies are overpaying, some investments won’t pan out, and some operations will catastrophically fail like an entire factory burning to the ground. But we’re well past the “will this work?” question and deep into the “how do we deploy this at scale without killing all the workers?” phase.
The Jungle by Upton Sinclair, clearly describing the reality of American industrialization, should be required reading in computer science degrees.
Sinclair wanted to show how capitalism was destroying human beings, but readers fixated on their own safety as consumers rather than the systematic dehumanization of workers. The government gave people clean food while leaving the fundamental power imbalances and dangerous working conditions intact.
The AI parallel is unmistakable: we’re so focused on whether AI stocks are overvalued (protecting investors) that we’re missing the much more serious question of what happens to the people whose lives and livelihoods get processed through these systems without adequate safeguards.
The real regulatory challenge is less about market bubbles and more about preventing algorithmic systems from treating humans like they are contaminated byproducts of the industrial technology boom Sinclair exposed.
And just like 1906, we’re probably going to get consumer protection laws (maybe some weak-sauce transparency requirements) while the fundamental power dynamics and safety issues for the people actually affected by these systems get ignored. It’s the same pattern again: worry on Wall Street about the symptom that scares the powerful, ignore the causes that harm the powerless at scale.
We’re seeing the consequences of rushing powerful automation into critical systems we depend on without adequate safeguards, like the industrial equivalent of the Triangle Shirtwaist Factory disaster, where really bad algorithmic decision-making functions like the doors that don’t open in a fire.
Fortune’s bubble talk, complete with cartoon analogies about Wile E. Coyote, reveals a fundamental misunderstanding of technological adoption cycles. When automation becomes operationally essential, market corrections don’t reverse the underlying transformation—they reset the price of admission and, hopefully, force better safety standards.
The real story is how AI slowly moved from experimental to indispensable as a 1950s concept dismissed in the 1980s before exploding in the early 2010s. Do you know what else followed that exact slow 30 year cycle?
Cloud computing.
The 1950s time-sharing concept reached explosive adoption in the 2010s, just like AI is doing now. A generation from idea to infrastructure in both cases, except one of them was rebranded. Calling the cloud a bubble today would be absurd.
Similarly the AI bubble predictions will age as poorly as Oracle saying there was no cloud, Sun Microsystems claiming there was no privacy, or IBM declaring there was no future for personal computing.
It’s not just a tech pattern to watch, it’s how human societies adopt transformative technologies for infrastructure across generational timescales.