At the time of writing, SpaceX has scrubbed and delayed Starship’s 13th launch, the company’s stock has reached an all-time low, and SpaceX is being sued for allowing its chatbot to generate tens of thousands of CSAM images and millions of unconsensual sexualised images. In return, xAI/SpaceX is suing one user from South Carolina for allegedly using the chatbot in this manner. Oh, and China landed a rocket booster for the first time, using a design that is arguably better than SpaceX’s. Needless to say, SpaceX hasn’t had the best time as of late. But it is about to get so much worse for them.

Let’s zoom in on that stock slide, because we all know that is what Musk cares about above anything else. Just over a month ago, SpaceX peaked at $225 a share; at the time of writing, it has slipped down past its $135 IPO price to just $115. That means in just 36 days, SpaceX lost 49% of its value, wiping out roughly $1.4 trillion in market cap (which is more than Tesla’s current worth) and, in the process, forcing every single IPO or open market SpaceX investor still holding their position into a loss.

That is monumentally awful. But it is somehow even worse than you think.

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As I have covered before, SpaceX engineered its IPO heavily to send the price skyrocketing. One of the most concerning of these manipulations was how Musk got NASDAQ to fast track SpaceX’s inclusion into index funds. Normally, stocks have to go through a year of “seasoning” after an IPO to be included in these products, to protect index investors from volatility. But NASDAQ changed these rules for SpaceX, allowing it to be included in its index after just 15 days of trading on the 7th of July. There are no exact numbers being reported, but it has been estimated that this would have caused $4.3 billion in forced purchases of SpaceX stock as NASDAQ rebalanced 100 investors’ portfolios to include it.

The total value of stock in the market (or ‘float’) on July 7th was roughly $86 billion, meaning this $4.3 billion NASDAQ index purchase would have tried to buy roughly 5% of SpaceX’s float. That should have permanently bumped up the stock price, given that this is a huge influx of consistent demand. But it didn’t. In fact, this barely changed the stock price at all.

Why? Well, just before this inclusion, SpaceX’s stock’s volume (how much is being sold/bought) spiked. In other words, a significant portion of SpaceX IPO investors sold at the exact same time the NASDAQ index was buying. Now, investors knew about NASDAQ’s rule change well ahead of time. So this all just looks like stock flipping. A considerable number of SpaceX IPO investors bought in with the express purpose of making a quick buck by flipping the stock to the index funds.

This means that SpaceX’s peak valuation was falsely inflated. But it also makes this rapid downward spiral even worse. After all, SpaceX lost almost half its value while billions’ worth of stock was being bought up. This shows that investors aren’t buying SpaceX as a company but the grift that surrounds it. It paints SpaceX investors as more similar to memecoin shills than stock traders.

This isn’t exactly good news, considering what is about to come.

Right now, SpaceX insiders (those who held shares pre-IPO) are unable to sell due to the IPO’s lock-in period. SpaceX, rather smartly, opted for a phased release, where insiders could sell 20% of their shares when SpaceX’s Q2 earnings are published (likely at the end of July or early August). The rest of their shares will be unlocked for sale in 20% chunks 90, 105, 120 and 135 days post-IPO. This staggering is not normal at all. But it does mean that insiders trying to exit won’t flood the market and crash the price.

However, because of how few shares were sold in the IPO, that is nowhere near sufficient enough protection!

Musk owns roughly 42% of SpaceX and is excluded from this lockup, meaning he can only sell his position a year post-IPO. SpaceX’s IPO only sold roughly 4.5% of its shares to the open market. That means that these insiders own 53.5% of the company, and in a few weeks, they will be able to sell 20% of these shares. Therefore, if all insiders maximise how much they can sell, then 10.7% of SpaceX’s shares will enter the market. That would triple the number of SpaceX shares in the open market, which would utterly crash it.

Then, this issue will snowball. By December of this year, 40% of SpaceX’s stock will be eligible to sell on the open market, meaning the float could increase by up to nearly nine times before the end of the year! There is a potential tsunami of supply coming, and that can only do one thing to the price.

You might think that all these insiders are venture capital firms or Big Tech companies, making them less likely to undertake such a destructive sale and hold the line together. But actually, there are a multitude of individuals in this lock-up. SpaceX employees own 10% to 15% of the company, and directors own massive portions, like Antonio Gracias, who owns 7.3%. These people are less incentivised to hold the line and are instead incentivised to maximise their position and cash out before anyone else does. So let’s say Gracias is the only insider to sell on this upcoming lock-up expiry, and he sells the maximum 20% of his shares. Well then, he alone would increase the available SpaceX shares on the open market by 30%. That is enough to crash the price. If he sells these shares for $100 a piece, this sale would also net him just over $10 billion in hard cash. This single sale alone would make him the 300th wealthiest person on planet Earth.

It is foolish to believe that SpaceX insiders are going to try and GameStop this sale and all hold together. The incentive to sell everything as soon as possible is palpable!

Especially when you consider where SpaceX is going.

Because there is very little justifying SpaceX’s valuation, its value can truly plummet. Morningstar pegs SpaceX’s true value at $780 billion, or just $63 per share.

That suggests that SpaceX can fall another 50% before hitting its floor. However, Morningstar is still very optimistic that ventures like AI, orbital data centres and Starship will eventually work out and be valuable businesses. But I, along with many others, believe these ventures are dead ends and that the true value is far, far lower.

Sadly, I do not have time today to fully explain how I would value SpaceX — that is for another article, another day. But what I can say is that it is more like $20 per share.

SpaceX insiders, particularly those with an engineering background, might not share my pessimistic outlook, but they will recognise that even Morningstar’s valuation is optimistically high — particularly when SpaceX’s last internal fund raising in 2024 valued the company at $350 billion, and the company hasn’t actually grown dramatically since then.

My point being, insiders are aware that SpaceX has an enormous potential to lose a catastrophic amount of value. Combine that with these lock-up periods expiring imminently, along with the AI bubble wobbling, and insiders are likely very anxious to liquidate before the value of their holdings vanishes into thin air.

Basically, it looks like SpaceX is at the top of a dangerous slippery slope. Moreover, SpaceX’s true value is increasingly looking like a grift, in the same way NFTs or memecoins had “value”. The value is in flipping the stock to a gullible bagholder, not the asset the shares represent. But that also means this stock won’t follow typical market forces. So, while the price has been consistently ticking downwards, expect chaos and volatility. But there is a storm coming for SpaceX, and it isn’t going to be pretty. My advice would be to grab some popcorn.

Thanks for reading! Everything expressed in this article is my opinion, and should not be taken as financial advice or accusations. Don’t forget to check out my YouTube channel for more from me, or Subscribe. Oh, and don’t forget to hit the share button below to get the word out!

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When will this damn AI bubble burst? How many clear-as-day signals does the market need shoved into its face for this silly charade to fall apart? Without a functional crystal ball, we simply won’t know until it is too late. But the frustration of watching the market aggressively mash the accelerator rather than the brake in response to this impending car crash is palpable. Those paying attention, who know just how insanely over-inflated this industry is, are nervously twitching at even the slightest sign that the bubble will pop. But, over the past week, we have seen some major movements that absolutely should have popped this bubble.

How does a bubble burst?

Well, a bubble is driven by speculative demand, which fuels unfounded investment that pushes an asset’s price beyond its true value and spurs a huge increase in supply as investors rush to meet this demand. A bubble bursts when this speculative demand fails to materialise, and investors realise there is a supply-side glut that undermines the value of their investments. So, they flee and sell their positions to reduce their losses, only for this mass sell-off to crash the price of these overvalued assets.

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From tulip mania to the dot-com bubble to even the US housing bubble, this dynamic has played out time and time again.

So, if you don’t have a crystal ball, the next best way to try to predict when a bubble will burst is to notice when the lack of speculative demand starts to cause serious issues, a pivot, or even a walkback from those most exposed to the bubble. That is a giant red flag that the pop is rapidly incoming.

Again, I cannot predict when this bubble will pop more than I can predict when Trump will actually follow through on a peace deal with Iran. This world is a chaotic mess after all.

But we have seen three of these giant red flags over the past few weeks.

As I wrote about a few weeks ago, Elon Musk’s xAI has rented out almost all of its AI compute capacity at its Colossus data centre to its competitors, Anthropic and Google. Anthropic agreed to rent 222,000 GPUs for $1.25 billion a month, and Google has agreed to rent 110,000 GPUs for $920 million a month. Bear in mind the highest verifiable figure of operational GPUs at xAI’s only data centre is 230,000 as of September 2025. So, these deals could represent xAI renting out the vast majority of its own compute.

These deals make no sense. Musk believes that AI needs way more compute capacity, which is why he plans to launch a million orbital data centres. So why sell pretty much all of xAI’s compute capacity to xAI’s competitors?

The only way these deals make sense is if the demand for xAI’s Grok and the utilisation of this data centre are so painfully low that it is not just more profitable but more advantageous in the long run to sell this compute directly to xAI’s main competitors.

Now, yes, Grok is objectively and measurably less capable than Claude, and it carries far more legal baggage, too. So, is this lack of demand just down to a lack of competitiveness? Well, I’d argue that if AI is going to be the general-use tool it promises to be, then Grok is nearly as capable as every other LLM AI out there. On top of that, xAI operates using much smaller infrastructure than Anthropic or Google, which both have multiple GW of compute capacity to hand. xAI was set up to only ever be a niche player, and it still couldn’t generate enough demand to justify its comparatively microscopic operation.

Ultimately, this is a major sign that the speculated demand for AI simply isn’t there and that those who bet big on it are desperately trying to create an escape route, even if it gives competitors an advantage.

And it isn’t just xAI doing this. So is the Zuck!

Meta, formerly known as Facebook, plans to sell its ‘excess’ AI compute after failing to stir up enough demand for its models. This is even more catastrophic than xAI’s pivot to renting out compute because Meta has really bet the farm on their AI being wildly popular. Meta has its own immense AI data centres and has also signed countless deals to buy and rent AI compute from a variety of companies. They signed a $100 billion agreement with AMD to build 6GW of AI compute in February, signed a $21 billion agreement with Coreweave to rent compute in April, have a $27 billion contract to rent AI compute from Nebius over the next five years, and raised $30 billion in corporate bond debt last October, explicitly to expand its own data centres.

In other words, Meta has done a Metaverse — bet big on a stupid idea that no one wants. Quite frankly, Zuck becoming bearish on a business venture should be seen as a bubble-pop indicator; the man is brilliant at betting large and late and stumbling right as the proverbial rug is pulled.

It is kind of obvious that Zuckerberg severely overestimated demand for his AI. But is that due to low industry demand or just this product being bad? After all, Meta’s latest model is far behind even Grok.

Well, I don’t think it matters either way. Lenders and Meta shareholders were happy to support Meta’s AI expenditure, believing it would generate demand for internal models. This pivot still shows a catastrophic lack of demand. Moreover, renting the vast amount of compute on the open market will surely flood it, crashing the price and choking AI circular funding (with more on that in a second). But also, rent this to whom? If xAI and Meta need to rent out, the only people buying are Anthropic, Google and OpenAI. They already have their own infrastructure, and who is to say they don’t have a fatal lack of demand? After all, companies are making a huge U-turn on AI adoption right now as they realise it doesn’t do what it says on the tin. Also, if there are only a handful of buyers of AI compute, doesn’t that prove that demand hasn’t materialised? No matter how you cut it, Meta giving up and selling their AI infrastructure is a sign that industry-wide demand simply doesn’t exist.

As a side note, this heavily suggests that current AI models are not compute-constrained, despite what the industry has parroted for a long time. If xAI and Meta can turn around and rent out huge portions of their AI infrastructure without massively impacting their current AI operations, it shows just how horrifically overbuilt and overinvested their AI infrastructure is, which, again, suggests a colossal supply-side glut.

There is another reason why Anthropic and OpenAI potentially renting this ‘excess’ compute could pop the bubble. The AI bubble is actually a bubble within a bubble. You see, inside the overall AI bubble is a computer hardware and compute leasing bubble, propped up by circular financing. The main AI labs, namely Anthropic and OpenAI, are not just wildly unprofitable, but they also receive much of their funding from chip makers and AI compute providers. For example, when Nvidia, Microsoft or Amazon invests in OpenAI, it isn’t in cash but in GPU lease agreements. This enables these providers to effectively buy their own products via an AI company, which enables them to register it as revenue, boosting their revenue. They also get a share of the AI lab in return, and by ‘buying’ ever smaller portions of the AI lab each time they do this, they can raise the price of the shares, making their previous investments more valuable, once again raising their revenue. Sasha Yanshin has a great video on this topic if you would like to know more.

So, Anthropic and OpenAI can’t really afford to rent all this GPU capacity, and that will get in the way of this circular financing. And they desperately need this circular financing to stave off bankruptcy. Not to mention that if either one of them goes bust, that will pop the bubble too.

Because OpenAI and Anthropic are still private, we can’t tell if they are experiencing the same demand problems as xAI and Meta. But there is one thing that suggests they are and that investors are already trying to exit and mitigate losses: Oracle.

OpenAI has agreed to buy $300 billion of AI capacity from Oracle over the next five years. Oracle itself has taken on a truly horrendous amount of debt to build out this infrastructure for OpenAI. In other words, Oracle has bet everything on the speculative demand for AI existing, because if it doesn’t, they could be drowned by the debt they have taken on for these deals. So, you’d think if investors were confident this AI demand was coming, that Oracle’s shares would be doing well. Well, they aren’t. Oracle’s share price has dropped by more than 40% in the last month.

If you ask me, those are two massive red flags: that the speculative demand for AI simply isn’t there and that investors are potentially starting to recognise this fact and pull out.

Given how insanely overleveraged and overvalued this industry is, these should be the pins that pop the bubble. But that assumes the market and investors are behaving rationally, and quite simply, they aren’t. Instead, it feels like all these billionaires are playing a giant game of chicken, seeing how far they can push this scheme, with the first to exit taking all the gain and everyone else taking the loss.

Thanks for reading! Everything expressed in this article is my opinion, and should not be taken as financial advice or accusations. Don’t forget to check out my YouTube channel for more from me, or Subscribe. Oh, and don’t forget to hit the share button below to get the word out!

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We all know Musk is an oligarchic kleptomaniac with a severe allergy to responsibility and accountability. Thankfully, he is also a painful narcissist, and it seems this deep, crippling desire to be adored was the only thing keeping him even slightly on the rails for the past few decades. But now that everyone hates him, it appears he has devolved into the “if you can’t handle me at my worst” phase of his midlife crisis, and over the past few years, he’s gone full crazy train. Arguably, the most transparent flexing of his authoritarian rage is on Twitter (sorry, I mean X.com) — you know, the place where his Grok AI posts CSAM.

First, we need to wind the clock back a little bit. It’s fair to say that Trump’s cronyism and kleptocratic ways are becoming a serious concern for many in the US. As The Guardian reported, Trump’s personal income in 2025 nearly quadrupled his 2024 business venture earnings, with most of the $2.2 billion gain coming from cryptocurrency schemes many have called scams. This is utterly unprecedented and almost certainly a blatant abuse of power. But it gets worse. Trump’s trades look very ‘insidery’. For example, he bought $2 million in Dell stock just before it was handed a near $10 billion Pentagon contract. The government has also given Trump’s family enormous government contracts, and a criminal Trump donor won a no-bid contract to restore the reflecting pool.

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This is arguably more kleptocratic and motivated by cronyism than in Putin’s Russia, so understandably, a huge portion of the US wants Trump’s wheelings and dealings to be far more public and transparent so they can hold this disastrous scheming to account.

This is where we come back to Elon.

Twitter/X has always been famous for accounts that use APIs to automatically collect and post public data into a nice, neat, and transparent timeline, such as the countless Nancy Pelosi trade-tracker accounts. Well, one ingenious fellow created a similar account for Trump and his stock trades, setting up the “Donald Trump Portfolio Tracker” on July 1st.

Unfortunately, the account disappeared the following day after X suspended it for ‘violating platform rules’. X has not stated which rule this account broke, nor why all the other accounts tracking politician trades have not faced suspension.

If you haven’t yet, I highly recommend reading X.com’s rules. They seem like legitimate and fair rules for such a platform. But, after reading it, you will quickly notice that Musk and his moronic AI Grok, as well as plenty of accounts that he interacts with, flagrantly violate these rules on an almost daily basis. For example, it explicitly bans inciting violence, the promotion of hateful entities, child sexual exploitation, artificially boosting or suppressing information and the use of fake identities, all of which Elon has arguably done in broad daylight on X.com many, many many, times. If the rules of X.com were worth the bytes of memory they take up in some distant server rack, then the very owner of Twitter/X.com would have been kicked out years ago.

But what you will notice is that these rules do not ban the collection and posting of publicly available data. That is why those Pelosi tracker accounts were never banned, because they followed the rules. So, why was this Trump tracker account banned? That seems deeply hypocritical.

This isn’t the first time Musk has done this, either. A few years ago, Musk banned an account called ElonJet, which used publicly available FAA data to track his private jet in real time. The purpose of accounts like this was to hold the super-wealthy, in particular those claiming to be climate-conscious, accountable for their colossal personal carbon footprints. Musk claimed the account was banned because it constituted doxxing, which is defined as “publicly revealing private or identifying information about a person without their consent”. This is objectively not true, as the data doesn’t reveal any previously unknown identifying information about Elon and is completely public. Such accounts now have a 24-hour delay rather than posting in real time and are currently not banned, as Musk doesn’t see this as doxxing. But again, if posting real-time jet locations is doxxing, Musk should go after the FAA, not these users.

And it’s not just ElonJet. If you have been paying attention, you will know that Musk has tried every method to silence those he doesn’t like on his little platform.

In that way, ElonJet was just an amuse-bouche to the Trump Tracker fiasco. It doesn’t matter what the rules Musk himself laid out say; it is whether what is posted gets in the way of Musk’s self-enrichment.

As a really quick side note: why is Musk like this? Well, Musk is a clear narcissist (given that he is the richest man in the world that does zero philanthropy), and narcissism is strongly correlated with authoritarian beliefs, conspiratorial thinking, and psychopathy, all of which are Musk’s fundamental personality traits. When you think about it, this makes sense. If you believe you deserve ungodly amounts of power and authority, you perceive the world through that lens and advocate for concentrating power, which is the nature of authoritarianism. But if this power is taken away, or perceived to be taken away, severe narcissism will inhibit the ability to rationally handle that loss, so these people fill in the blank space with a totally made-up conspiracy about people out to get them. This is why the likes of Elon view cancel culture, #MeToo, wokeness, multiculturalism, and progressivism not as the decentralised movement of individual voices calling for accountability and a better planet, but as an evil cabal out to destroy him and, therefore, the world. They can use this totally unfounded belief to justify whatever heinous shit they want to do to gain more power. So, the reason Elon and his ilk are like this is almost certainly because they are both pathetic and mentally ill.

That is at least one argument as to why Musk has done this. But why should we care that this Trump tracker was suspended?

Let me put it this way: an oligarch is abusing his position of power to protect the president he was instrumental in putting into office from accusations of corruption, cronyism, and kleptocracy. This is not just a worrying attack on free speech — though, because social media is not seen as protected speech, the actual legal case here is that X.com has violated its own terms of use with this user — but also an attack upon the mechanisms that enable democracy and upward accountability, those being the collection and dissemination of data that hold those in power accountable for their wrongdoings.

At this point, X.com is tiny and almost inconsequential, as so many people have left the platform. The impact of this one ban is arguably not that significant. But if Elon is doing this in the open, what kind of stuff is he doing, or has already done, behind the scenes that we don’t know about? These actions set a worrying precedent and are a clear sign we should investigate and dig deeper.

It also shows why we need to divest, boycott and diversify our lives away from these platforms and points of control these oligarchs wield. We need to loudly say, “This isn’t acceptable,” and actively not support it. This is why I strongly oppose giving the likes of Starlink, X, or xAI a more prevalent place in our lives. As we have seen, you can’t use X.com to disseminate information that holds the oligarch class to account. Musk could stop the operations of businesses that rely on Grok if he decides he doesn’t like the business. Musk could ban certain websites from Starlink, stomping out information from any website, not just X.com, that could impact him or his political class. Particularly in the digital age, these are mechanisms critical to fostering a healthy democracy and a fair, equitable society. And that is what we all want, right?

Thanks for reading! Everything expressed in this article is my opinion, and should not be taken as financial advice or accusations. Don’t forget to check out my YouTube channel for more from me, or Subscribe. Oh, and don’t forget to hit the share button below to get the word out!

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For the past year and a bit, we all stood around, almost gleefully watching Mr DOGE’s sales figures plummet. It felt like karma for this oligarchic edgelord’s political machinations, like there was some vague sense of justice in this messed-up world. Many others, including myself, called this downward spiral the death of Tesla because, in truth, this wasn’t just down to Musk’s bad PR but a chronic mismanagement of the company, causing it to lose its lead, stagnate, and fall behind the competition in many ways. After all, Tesla is but a shell of what was once promised. But, over the past few months, Tesla’s sales have suddenly shot up. Is this a rebound? Is Tesla recovering? Is Tesla still doomed? Is it on track to meet the colossal targets that have justified its insane sky-high stock price? Is the proverbial egg on my face? Well, let’s find out.

I want to give credit where credit is due. Tesla’s recent sales rally is damn impressive, particularly when you consider the current political environment of its main markets. The FT reported that Tesla delivered 480,126 vehicles in 2026 Q2, smashing its 404,000 prediction. That is 24.99% growth year-over-year and is the best Q2 delivery in Tesla’s history(though still approximately 10,000 short of the best quarter ever). This is no guarantee that 2026 will be a good overall sales year for Tesla; Q2 has historically been quite a variable period for the company. Also, the vast majority of this sales growth happened in Europe and China, and this locality could make this sudden growth short-lived. However, growing sales in Europe, considering the entire continent is beginning to turn on US hegemony and its tech oligarchs like Musk, is remarkable. Whatever the sales and PR teams are doing, it’s working.

Tesla brown-nosers have used these sales figures as evidence that Tesla isn’t dying and that critics, like me, were wrong.

So, let’s clarify what I mean when I say Tesla is dying. I mean that its speculative value is dying.

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As I have said before, if the market valued Tesla along standard norms, it would lose some 94% of its market cap. This insane speculative value is based entirely on Musk’s promises, not just for self-driving cars but also for technological dominance, colossal scale, market dominance, and massive profit margins. Moreover, Musk’s interest in the company seems to now solely be based on pushing this speculative valuation. In order to keep that speculative value, Tesla either needs to follow through and become the veritable monopoly it promised to become, or it needs to at least be perceived to be making progress in that direction.

The question is, is this sales growth enough to keep this speculation alive? Because if it isn’t, the value will drop, and Musk won’t be interested in sustaining Tesla anymore. He could let it run its course into obscurity or merge it with SpaceX to pump the market manipulation scheme he has going on over there. But either way, Tesla has to demonstrate enough progress to justify its speculation, or it’s curtains for the EV maker.

Sadly, I don’t think this sales increase will be enough.

This growth has far more to do with the market than Tesla.

In 2026 Q1, European BEV sales increased 26.2% year-over-year; in April alone, European BEV sales grew a gigantic 42% year-over-year, and while 2026 Q2 European BEV sales figures haven’t been released yet, they are likely to be far higher than the previous year. While China’s overall BEV market is currently shrinking due to its small, short-range BEV segment sharply contracting, its premium BEV segment (where Tesla operates) is growing rapidly. A variety of factors can cause this growth. Europe and China are already being hit hard by oil price increases from the Iran war, which are predicted to get worse, prompting many consumers to switch to electric vehicles to avoid rising bills. The cost-of-living crisis in Europe worsens this cost avoidance, causing even well-off buyers to behave cautiously and buy EVs.

This is a case of a rising tide raising all boats. But Tesla’s boat isn’t floating quite as well as it should. It appears Tesla’s sales growth might actually be behind the market growth. In other words, Tesla likely hasn’t made up ground in the market and is still trailing behind its competition. So these delivery figures have less to do with Tesla itself doing better and more to do with the fact that the number of people buying EVs has dramatically risen.

This is perfectly demonstrated by BYD, Tesla’s main global competitor. For years, Tesla was the world’s largest BEV maker, with BYD being a distant second. Over the past few quarters, BYD has caught up and been vying for Tesla’s №1 spot. But, in 2026 Q2, they delivered 557,090 BEVs globally. That is 16% or 77,000 more than Tesla delivered and is actually substantially more than Tesla has ever delivered in a quarter! BYD has now comfortably cemented its lead against Tesla to become the new largest electric vehicle maker on the planet. Bear in mind that the global BEV market is also becoming significantly more diverse as new players enter and succeed.

I don’t think you quite realise the monumental nature of this change. Back in Q1 of 2020, Tesla held a 29% share in the global BEV market. It has dropped consistently since then to just 13% by Q1 2026, and as BYD shows, it is likely to have fallen again in Q2 2026. Tesla is falling behind rapidly.

The narrative of Tesla’s speculative value over the past decade has been that it would keep ahead of the competition and retain its dominant market share as the market grew. The astronomical profits from that dominance would then fund its self-driving and Robotaxi rollout, enabling it to smother the competition until Tesla essentially dominates global personal transport and rakes in hundreds of billions of dollars in revenue each year.

If Tesla really was winning back its market share and becoming as dominant as it needed to be to justify its stock price, BYD simply wouldn’t be pulling ahead of it like this.

Because Tesla’s sales growth can’t really be attributed to Tesla, and the company is falling behind the wider market, I doubt these sales figures are enough to justify Tesla’s speculative value — at least for the vehicle sales side of the company.

I also think people don’t realise just how rapidly Tesla has to grow to even come close to the targets that created this speculation.

Take Tesla’s mythical $25,000 ‘Model 2’. This vehicle was meant to actually deliver Tesla’s sales growth. Musk said it would launch in 2025, with production rapidly scaling to well over a million deliveries a year, which would have boosted Tesla sales by over 60%. I cannot stress how important this vehicle was to Tesla’s future. Automotive analysts at Bloomberg NEF stated it was critical for Tesla to remain at the forefront of the market and fend off Chinese rivals. Yet this promised vehicle and its growth are nowhere to be seen. Against his executives’ advice, Musk scrapped the vehicle to focus development funds on the Robotaxi (read more here). These funds were supposed to have come from rising profits from sales growth that Model 2 was expected to deliver, so this decision made no sense at all. And now, rival BEVs with almost identical specs and price are selling like hotcakes (read more here).

Tesla has to do something drastic to make up for the lack of Model 2 and the sales and profit it would have garnered.

But, truth be told, the Model 2 wasn’t enough on its own to meet Musk’s targets. Musk originally set a target of selling at least 20 million vehicles annually by 2030. That is the same as Toyota and VW (including their subbrands) combined. To achieve this, Tesla would have had to launch multiple new models each year (including vans, economy cars, sports cars, large SUVs, different kinds of trucks, you name it) and have every single one be a market leader.

Musk has since quietly dropped this target, though speculation about future market dominance of this scale remains. Musk’s insane Tesla pay packet does have a very watered-down condition that Tesla’s total lifetime vehicle deliveries must reach 20 million by 2035 (read more here). By the end of 2025, Tesla had sold cumulatively 9.24 million vehicles. So, to meet this pathetic target, Tesla only has to sell 1.3 million vehicles a year, or roughly 20% less than it currently does. It’s obvious what Musk is doing: he is trying to quietly move the goalposts to make it look like he is delivering on the growth he once promised. But this is hollow. The speculative value isn’t based on his new, heavily lowered targets but on the lofty targets he once set and the feeling of limitless growth they inspired in investors.

Tesla was never going to reach 20 million sales a year; that was bombastic hyperbole. But to meet the speculative value this target gave Tesla and to generate the profits needed to fund its other speculative programs (i.e., the Robotaxi), it needed to at least deliver on what these targets signalled to investors: unprecedented growth.

A 25% year-over-year sales increase after multiple years of declining sales is not indicative of unprecedented growth, especially when the wider BEV market is growing even faster and your main competitor is substantially pulling ahead. So no, this growth isn’t egg on my face. It doesn’t prove the critics wrong, because the improvement still isn’t enough.

Tesla’s worsening profit problem only compounds this issue.

Tesla’s net profit margin peaked in 2023 at over 15%, buoyed by government grants and considerable demand. Tesla’s in-house 4680 battery was supposed to drastically cut production costs, enabling it to keep this sizeable margin even when these grants were eventually phased out or competition drove down prices. But again, thanks to Musk’s mismanagement, this project has gone nowhere, and the 4680 is well behind the market, leading Tesla to source better, cheaper batteries from the likes of its mortal enemy BYD (read more here). Then, Tesla’s global sales began to drop, so they had to dramatically reduce prices to compete, slashing their profitability. By the end of Q1 2026, Tesla’s net profit margin was just 3.94%. Moreover, the vast majority, if not all, of that profit does not come from vehicle sales but from Tesla selling carbon offset credits to other automakers, a scheme which Trump is ending in the US, and global automakers are buying less and less of these credits as they sell more of their own EVs (read more here). In other words, Tesla could very soon be unprofitable or, at the very least, have an unsustainably small profit.

For comparison, Toyota’s profit margin is currently 8.91%, which it almost entirely receives from vehicle sales, not government programs.

This profit problem is a deeply important piece of context for the recent increase in sales, as Tesla may have actively made it worse for themselves.

At the end of last year, Tesla cut Model 3 & Y prices by up to 50%, which have since dropped even further. In fact, you can still lease a Model Y for as little as £274 a month (roughly $365) in the UK, which is insanely cheap for a £40,000+ car that depreciates like the Hindenburg. Additionally, they have offered huge trade-in incentives, which is remarkable given Tesla’s depreciation, aggressive 0% finance options and so on.

My point is, Tesla’s profitability was already in a precarious position. If they have had to slash profits even further to generate these sales, which is how it looks, that destroys the speculative narrative. The entire point is that they would dominate the BEV market while also being incredibly profitable, enabling them to invest heavily in autonomy and win the autonomous race.

Tesla has created a cavernous gap between what it promised and what it is delivering, technologically, market-wise and financially. Yes, this 25% increase in deliveries is likely a positive sign for Tesla, particularly if it simultaneously pushes up overall profit. But it is not enough to prop up and justify Tesla’s speculative value, especially when its flagship Robotaxi service is laughably awful and miles behind the competition (read more here).

And guess what, it seems we are already witnessing this fallout. Despite this 25% increase in deliveries, Tesla’s stock has fallen by 8%. The speculative value is crumbling.

Sales decline is an obvious route to corporate death, but so is disappearing profit and a sharp loss in market cap (particularly when the only thing the CEO cares about is the latter). That is why Tesla is still headed for the grave. It has no real options to solve this problem and deliver anything that remotely resembles the promises Musk made to boost speculation. So, when there is eventually a proper market correction, this lack of evidence supporting Tesla’s speculative value will leave it wildly vulnerable to a crash in value the likes of which we have never seen.

That death scenario is still very much on the table, and Musk’s only real way of avoiding it is to merge Tesla and SpaceX (though that would also be a death knell for Tesla, but that is a conversation for another time). Without a crystal ball, I can’t predict what will happen to Tesla or when this downfall will occur. But I do know that the existential risks Tesla faces have not disappeared at all.

Thanks for reading! Everything expressed in this article is my opinion, and should not be taken as financial advice or accusations. Don’t forget to check out my YouTube channel for more from me, or Subscribe. Oh, and don’t forget to hit the share button below to get the word out!

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Many others, including myself, have stated that SpaceX’s IPO could have popped the AI bubble if it didn’t go well. Well, Musk’s AI/social media/rocket company hasn’t exactly had a smooth ride. After a rapid initial climb, over just a week of trading, SpaceX lost 28.7% of its value from its peak (read more here)! In fact, its share price has crashed so much that it is now below its debut price. With its share price rising and crashing back to Earth as quickly as one of countless failed Starship launches, it’s hard to call this an out-and-out success. This mini boom-and-bust is not a good sign for an industry that requires exponential growth. As such, there is an argument that SpaceX has just popped the AI bubble—we just don’t know it yet.

Let’s start with what we mean by the AI bubble bursting. According to Investopedia, an economic bubble is when trades are made “at a price, or within a price range, that greatly exceeds the asset’s intrinsic value.” SpaceX’s, OpenAI’s and Anthropic’s current valuations fall well within this definition. Investopedia defines bubbles as having four stages, with the final ‘pop’ being the panic stage, which it defines as “asset prices reversing and falling, often as quickly as they had risen. Investors want to liquidate them at any price. Asset prices decline as supply outshines demand.”

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This is why SpaceX’s IPO is so shocking to those at the top of the AI bubble. Its rapid fall suggests that investors were more interested in flipping or liquidating their shares, even at a loss. It portrayed these investors as seeking a get-rich-quick flip scheme, more akin to trading card scalpers than actual long-term investors. Such speculative investors are highly fickle and ready to sell at the drop of a hat. Now, I must add that this is only how it appears. Thanks to how SpaceX structured its IPO, such volatility should be expected. But if this downward trend continues, and SpaceX insiders liquidate as soon as their lock-up periods expire, it will confirm this is the case.

However, it seems this concern is enough to deeply spook the major AI players.

As reported by the New York Times last week, OpenAI is considering delaying its IPO until 2027, citing SpaceX’s IPO and claiming they do not think they can reach their $1 trillion target price under today’s market conditions. In other words, they have witnessed the SpaceX sell-off, and it has made them deeply worried.

Now, do I believe OpenAI’s excuse that SpaceX’s IPO is the only reason? Hell no. The leaked financials Ed Zitron published completely torpedoed their chance of controlling the narrative over their heinous finances, making a smooth IPO totally impossible. Moreover, OpenAI CFO Sarah Friar stated the company isn’t ready for an IPO this year, citing its horrendous financial position but also the colossal organisational issues that needs to be ironed out first. I have also seen an argument that OpenAI’s IPO underwriters may have valued the company below its previous $852 billion valuation, which would be a total disaster for its speculative value. OpenAI has plenty of reasons to back away from its IPO plans; the signals from SpaceX’s IPO might have just been a nail in the coffin.

There are rumours that Anthropic has also delayed its IPO to next year, such as this article and this article. That being said, no major publication has covered or verified this topic, and both these sites rank very high on AI checkers. So, this could be total BS to generate clicks. As such, I still believe Anthropic is still going for its October 2026 IPO target. But it is nonetheless interesting that people are spreading these kinds of rumours. It suggests that this backtracking is what a reasonable portion of the market expects.

This is all well and good, but there is just one problem: neither Anthropic nor OpenAI can afford to delay their IPOs because they need the cash to stave off bankruptcy.

AI is heinously unprofitable. Again, Ed Zitron discovered that OpenAI lost $38.5 billion in 2025 (read more here). In fact, OpenAI’s losses are mounting so rapidly that even with huge investments, analysts have projected it could go bankrupt by 2027.

Anthropic has tried to meekly suggest that its books are better and headed towards profitability. But as Ed Zitron has pointed out, this is more of an accounting trick and market circumstance than actual profitability. In fact, Anthropic’s CEO, Dario Amodei, has said that if growth forecasts are off by a year, they will go bankrupt and that they need to generate hundreds of billions of dollars in revenue to become financially sustainable. Considering how AI revenues and AI returns are not increasing as predicted, and we are seeing the beginning of an AI U-turn, as businesses realise how useless and expensive it is (read more here), that revenue likely isn’t going to materialise. So, just like OpenAI, Anthropic will need to plug that gaping hole in its books with investor funds.

The AI industry is no stranger to paying for catastrophic annual losses with investor cash. These AI labs and the ecosystem they live in have covered their losses for years through private equity sales and debt. But these sources of funds are beginning to dry up. Venture capital firms are struggling to raise enough capital to invest, their Big Tech backers are nearing the limit of how much they can invest, and the bond market is so saturated with risky AI debt that borrowing costs are getting too high. These sources of cash simply can’t provide them with enough funds to survive anymore. So, they need to turn to the only capital market they haven’t tapped into yet, retail investors, and to do that, they need to go public.

This is why SpaceX may have already popped the bubble, but we just don’t know it yet.

If SpaceX is responsible for OpenAI delaying its IPO until 2027, then it will be pushing it fatally close to bankruptcy, as it needed these funds yesterday. Being so close to death will make it nearly impossible to raise enough capital via an IPO. I can’t stress this enough: OpenAI delaying its IPO is effectively sealing its fate.

But what about Anthropic? If these rumours somehow turn out to be true and they too delay their IPO until 2027, then it’s the same problem. But here is the thing: because of the circular nature of AI, where the chip and compute suppliers fund the AI labs to buy their own products (also known as round-tripping), if OpenAI crashes out of this circle, the entire grift collapses. Not only that, but the collapse (or even perceived collapse) of OpenAI under its own financial idiocracy will cause a market correction and reassessment of the value of these companies. Both of these forces will choke Anthropic. They do not get out of this lightly at all.

Now, could something come along and save Altman and Amodei’s sorry arses? Yes. It’s no secret that Altman can see this car crash coming from a mile away and is basically begging Trump for a government bailout. We live in crazy, unprecedented times, so no cards are off the table, and wrenches are constantly thrown into the machine. Equally, Altman could still plough ahead with OpenAI’s IPO, or Anthropic could conduct an IPO earlier and do strangely well.

As I said, we don’t know how this will play out. Don’t try to time the market; it’s a fool’s game. But there are signs that SpaceX’s rocky IPO may create a domino effect and cause this bubble to pop. The fact that this is, at the very least, a possibility is not good news for the AI industry.

Thanks for reading! Everything expressed in this article is my opinion, and should not be taken as financial advice or accusations. Don’t forget to check out my YouTube channel for more from me, or Subscribe. Oh, and don’t forget to hit the share button below to get the word out!

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Anthropic recently sent a letter to US officials accusing Alibaba of “brazenly” and “illicitly” attempting to extract its AI capabilities with the largest known distillation attacks on them to date. Anthropic flagged 16 million interactions with its Claude chatbot across 24,000 fake accounts as distillation attacks tied to Alibaba and the Chinese AI models it works with. If you have never heard of distillation, it is a relatively common AI training method where the output of a larger, more advanced model is used to train a much smaller, lighter model. When conducted “legitimately”, this is a great way of making smaller, cheaper-to-run models that are still relatively capable. But when an AI company uses the output of a rival’s more advanced model to train its small, lightweight model, it is considered an illicit distillation attack, as the original developer isn’t being paid for their work. If you have even a passing understanding of how LLM chatbot AIs work, you may already see the cosmic-sized hypocrisy at play here. But I guarantee you that Anthropic’s argument is far more stupid than you realise.

For those who don’t know why this is hypocritical, let’s start at the beginning. Chatbot LLMs need to be trained on a truly colossal amount of data. So much so that if AI labs like Anthropic or OpenAI actually paid the copyright holders of said data, it would immediately bankrupt them. So, they just take the data without paying.

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Anthropic downloaded seven million books from LibGen’s “shadow library” without paying for a single one and shoved them into Claude. Reddit also sued Anthropic for illegally scraping user data from its site over 100,000 times, again to shove this data into its AI. To get around copyright laws, Anthropic bought millions of physical books and scanned them all (destroying them in the process) to create a digital copy to feed into Claude. This utilised a rather dubious loophole in copyright law, as they did technically buy the works, but the copyright holder did not agree for their works to be used in this manner and wasn’t appropriately compensated. On top of all of this, just like every other AI company out there, Anthropic has been accused of scraping virtually the entire internet for data, hoovering up a truly astronomical amount of copyrighted material, and once again, feeding it all into Claude without paying the creators a cent.

I don’t think people realise the scale of this copyright-nicking scheme. Take Common Crawl, a web scraping service used by Anthropic. Its dataset was a whopping 9.5 petabytes in early 2024 and has since added 3–5 billion new pages each month (roughly 380 terabytes of uncompressed data per month), meaning their current total filtered dataset could quite easily be well over 20 petabytes.

I want to put that number into context. Let’s say we wanted to download this dataset onto bog-standard 256-gigabyte iPhone 17. We would need 72,760 of them in order to store all 20 petabytes. An iPhone 17 is incredibly thin at just 8.8 mm, but if we stacked all of these on top of each other, it would make an iPhone tower over 640 metres tall (2,100 ft). Now, not all of this dataset is made up of copyrighted data; much of it exists in the public domain, but a considerable portion is copyrighted material, such as articles, blogs, guides, books and video transcripts.

You get the picture. If we saw this as copyright theft — which arguably we should, considering the precedent set by the horrific case against Reddit founder Aaron Swartz for ‘stealing’ scholarly articles, which led to his eventual suicide — then this would be one of the largest criminal thefts in modern history.

So, how are AI labs getting away with this? Well, they argue that this process falls under ‘fair use’ as they are being ‘transformative’ with the copyrighted material.

In copyright law, people are allowed to use your copyrighted material as long as it is ‘transformative’, which is classified as ‘fair use’. This is why YouTube movie critics are allowed to use extensive clips from the films they discuss. They aren’t reproducing the material, and their use of it isn’t directly competing with the original work (which is critical). Instead, they have ‘transformed’ it into something different by commenting on it.

Now, here is the problem with the AI labs’ argument that using copyrighted data to train an AI should be classified as ‘transformative’. LLMs can fully reproduce their training data verbatim. Moreover, LLMs can only replicate the patterns present in their training data. So, if you want a chatbot to be able to produce recipes, you train it on a ton of recipes. This means that AI output trained on copyrighted data directly competes with the original copyrighted material. It will negatively impact the revenue and value of the original work.

If the laws of the land were actually used as intended to protect people, this ‘fair use’ argument would have been laughed out of court. As it stands, those in power wield and bend the law to protect capital, not people, which has meant they have let off AI labs scot-free.

But doesn’t this argument set a precedent that completely undermines Anthropic’s stance against distillation attacks?

If training an AI on data counts as transformative enough to be fair use, then doesn’t Alibaba’s distillation of Anthropic’s model fall under the same umbrella?

If the law were applied consistently and the output of a near-trillion-dollar AI had the same legal protections as living, breathing people’s work, then by Anthropic’s own argument, no wrongdoing has occurred here.

There is a second issue, though, as Google recently discovered. You see, if training AI on copyrighted data counts as transformative and the machine isn’t just plagiarising and regurgitating what it was trained on, then the output is considered new material, and the AI lab can be held responsible for its output. This is the same way that our hypothetical YouTube movie critic can still face a slander lawsuit. Google found this out recently in court, where it was ruled that it can be held responsible for any damaging inaccuracies found in its AI search summaries (read more here).

Likewise, Anthropic’s own argument against Alibaba means that it is also arguing for itself to be held responsible for any damaging errors its AI produces. Now, I don’t know if you have used an LLM recently, but these things get stuff horribly wrong all the time! I can assure you that Anthropic does not want to be held responsible for Claude’s mistakes, because that would be devastating.

Okay, so let’s say that Anthropic acknowledges these issues, U-turns, and drops this letter-writing campaign against Alibaba. That would heavily imply that AI training is not transformative and doesn’t fall under fair use. Again, if the law were enacted ethically, consistently and properly (i.e., for the benefit of the people, not capital), then that would mean that Anthropic and all other AI labs would have to pay billions of dollars in compensation to copyright holders they have stolen from.

In other words, this is a catch-22.

Either AI training is fair use, which means distillation attacks are perfectly legal, even though it will pop the AI bubble. Anthropic, OpenAI and other AI labs are only valuable because their AIs are seen as unique and owned by them, meaning customers have to go to them to benefit from their AI. But if distillation attacks are legal, that is no longer the case, as rival AIs can provide an equally good model that costs less to use. The exclusivity and value of AI labs’ proprietary tech dissolves to functionally zero, as it no longer becomes theirs exclusively, and the justification for their near-trillion-dollar valuations evaporates.

Or distillation attacks are illegal, which means AI training isn’t fair use. That would mean any copyright holder who has shared their work online, or shared anything online, which is essentially all of them (including you; if you have posted something online, you technically have copyright over that ‘work’), deserves compensation for their work being used to train these giant LLMs from the likes of Anthropic and OpenAI. Such compensation could balloon into the hundreds of billions of dollars, particularly when many copyright holders have been materially harmed by AI encroaching on their work (such as musicians).

For the AI industry to exist as it currently does, it requires an insane legal contortion. It requires systematic hypocrisy to deflect accountability on all sides to retain its precarious position. Now, in our current insane political landscape, this hypocrisy is evidently possible. This is a lawless neoliberal hellscape where what is left of the law of the land is bent to protect capital and leverage and brutalise the people who stand in its way. But, if a modicum of sanity prevails, if this chaos is toned down, if this epidemic of unreality is replaced with grounded reality, and if the law is actually applied consistently for the protection of the people — the very bedrock of democracy — then the AI industry crumbles. The AI industry is inherently betting that today’s reign of idiocracy and oligarchic power won’t end. I hope with all my heart you haven’t lost hope and, like me, believe this era of brutal stupidity will soon come to its natural conclusion. If you believe that is the case, then where do you think the AI industry is going?

Thanks for reading! Everything expressed in this article is my opinion, and should not be taken as financial advice or accusations. Don’t forget to check out my YouTube channel for more from me, or Subscribe. Oh, and don’t forget to hit the share button below to get the word out!

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Britain and Europe have just experienced one of the worst heatwaves in living memory, with temperatures climbing over 40 degrees Celsius (104 Fahrenheit). However, unlike the last record-breaking single heatwave from the ’70s, this was no anomaly; it was the result of a stark upwards trend. The summer of 2024 was the hottest ever recorded until 2025 smashed that record, and now 2026 is set to possibly top that too. This is why scientists have found that this recent heatwave was impossible without man-made climate change. We also know that this heatwave has killed well over a thousand peopleand dealt serious damage to infrastructure, economies, and society. But hold on, we know who is responsible for, and profits hugely from, climate change: fossil fuel companies. In a just world, they would be made to pay for the damage their goods cause. But that isn’t happening. Why? Truth be told, there are a lot of reasons. This is a complex topic. But there is one glaringly obvious reason — taking accountability would destroy them. Let me explain.

But first, how much damage does climate change-induced extreme weather cause? Well, there are a few different ways of calculating that.

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The charity Christian Aid found that the ten worst climate-related disasters of 2025 caused more than $120 billion in insurance losses. That is a dramatic figure, but it doesn’t reflect all the damages. Much of the developed world hasn’t been accounted for, nor have down-the-line losses or economic damage. It doesn’t even take into account all the lives lost, as much of the world’s population doesn’t have life insurance. As such, while this is a shockingly high figure, it is very much an underestimation.

This is where the study of weather attribution comes in. This process looks at extreme weather events, like heatwaves, hurricanes, floods, and snowstorms, and uses tools like computer simulations to figure out if man-made climate change caused the event or how much worse the event was because of climate change. Weather attribution studies are incredibly detailed and time-consuming, as they use a truly staggering amount of data and analysis to arrive at defensible conclusions. But, once you have completed this study, you can then attempt another which looks at how much damage these extreme weather events caused and establishes how much of this damage was due to man-made climate change.

Studies like these take a long time to complete, but their results are shocking.

A study published in Nature assessed the climate-change-attributed costs caused by 185 global extreme weather events from 2000 to 2019. Over that period, the total figure was $2.86 trillion, with climate change causing an average annual damage of $143 billion. But the annual cost was highly variable, with 2001 being the lowest at just $23.9 billion and 2008 being the highest at $620 billion.

However, these researchers also stated that their results are likely far lower than the actual costs, as they lack critical data. For example, over that time period, they do not have any data on extreme weather deaths in sub-Saharan Africa. Indeed, a lead researcher on this paper stated that “our headline number of $140bn is a significant understatement.”

It isn’t like such data is impossible to obtain; it is just that we aren’t collecting it. If we did, then we could accurately and rapidly calculate just how much damage climate change is causing each year. But, as it stands, we still don’t even know the true extent of the damage climate change caused last year. That alone is a bit concerning. It shows that this is an area of science that needs far more attention and funding.

But we do know that things are going to get much worse, as climate change has only just begun to sink its teeth into our planet. Moreover, future projections will encompass more of the total global damages, as they can simulate and project the critical missing data plaguing retroactive analysis.

It has been estimated that annual climate-change-caused damages will reach at least $300 billion to $700 billion by 2030. And it will only get exponentially worse from there! By 2035, the global cost of climate change damages is estimated to be between $1.7 trillion and $3.1 trillion a year. That isn’t even rock bottom. Climate change damages will spiral even further if we do not rapidly decarbonise. This is partly why net zero makes so much sense, because the cost to reach net zero is substantially less than the damages climate change will cause. In fact, recent studies have found that reaching net zero will only cost an extra $1 trillion per year, yet annual climate change damage will reach $12 trillion (equal to a third of the current US economy) by 2095.

More than 99% of scientists agree that climate change is real and caused by our carbon emissions. We also know that fossil fuels account for around 89% of our carbon emissions and that the oil and gas industry is one of the most profitable industries on the planet.

I don’t know about you, but I think that if a company sells a product that causes damage, it should be made to pay for said damage. This is not some fringe interpretation of the law — just look at the Roundup cancer class action lawsuit.

So, why isn’t this legal framework being used to arrest oil executives or claim damages from this industry? We know we are suffering, we know who is causing it, and yet we are seemingly refusing to hold them accountable.

To be clear, similar legal action is being taken, and the concept of ecocide is actively being considered to be included as a crime under international law. But even these movements are tiny in comparison to the actions required to hold such a colossal industry accountable. If governments actually had their citizens’ best interests at heart, there is a reasonable argument that they would already be striving to receive compensation for the damage the fossil fuel industry is causing them.

So, why isn’t such large-scale action being taken?

To answer that question, you could write countless PhDs. After all, this is a deeply complex socioeconomic, political and technical issue.

But sometimes it does help to take the simplified approach, and we can do that here by simply following the money.

The world’s largest oil company is Saudi Aramco. They account for 13.8% of global oil supply and made $93.4 billion in profit in 2025. Aramco has some of the lowest oil extraction costs, at just $10 per barrel, with countries like the US having production costs that are three to four times higher. As such, Aramco has one of the largest profit margins in the fossil fuel industry (potentially double that of the US oil industry). Using Aramco’s annual profit and market share, we can estimate an upper limit for total global oil industry profits. If you do the maths, it works out to $676.8 billion.

That is about equal to the estimated annual costs of damage climate change will cause by 2030 and less than half the costs of the lowest possible estimated annual damage it will cause by 2035. And, just to remind you, the oil industry is making nowhere near $676.8 billion in profits per year. That number assumes the entire industry is as profitable as its most profitable company. I fear this is why no government has taken any serious measures to hold the fossil fuel industry accountable for the climate damage they create. If they were made to pay for such damage, the industry overall would be woefully unprofitable.

This is one of the main flaws in utopian free-market thinking. It doesn’t take into account these external costs (also known as externalities). I would argue that for a market to be genuinely free and equitable, such externalities should be actively taken into account so that a product’s price reflects its true costs. Indeed, most governments operate in this way, hence why the Monsanto Roundup lawsuit succeeded.

But they can’t attempt that for fossil fuel companies, because it would open a floodgate and destroy this industry. Don’t forget that society still requires fossil fuels to function, our financial system relies on this industry, and modern Western politics is funded by this industry. It doesn’t have to be this way — today’s technology is sufficient for us to drastically cut our reliance on fossil fuels. But, because we haven’t taken those steps yet, governments can’t risk holding this damaging industry to account, because it will kneecap them.

So the next time there is a climate-change-induced extreme weather event, thousands die, infrastructure is damaged beyond repair and economies are depleted, take the time to remember that this is one of the main reasons no one is held responsible. The only way the fossil fuel industry can survive is if it evades even the tiniest drop of accountability for its planet-destroying ways, and thanks to its place in society, it can effectively hold the entire planet’s economies hostage to ensure it avoids any culpability. To save the world as we know it, we need to break this dynamic. How we achieve that is a question for another day. But as I prepare for yet another heatwave coming my way next week, I hope it’s not just me contemplating this dire question.

Thanks for reading! Everything expressed in this article is my opinion, and should not be taken as financial advice or accusations. Don’t forget to check out my YouTube channel for more from me, or Subscribe. Oh, and don’t forget to hit the share button below to get the word out!

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An ouroboros is an ancient symbol of a serpentine dragon eating its own tail, which is meant to depict the cyclical nature of destruction and rebirth. In the modern world, however, its meaning has shifted to a shorthand for self-destruction. A system so flawed it eats itself alive until nothing is left. It’s easy to see why AI has been labelled an ouroboros. These machines need to consume our data to exist, yet they are simultaneously expected to be existential threats to us and our livelihoods. But AI’s self-consuming nature goes far, far deeper than this, and it could have horrific consequences.

This problem all starts with data.

Modern LLMs require an unfathomable amount of data to function. As such, an entire data economy has been set up to scrape, gather, sort and label every conceivable kind of human-generated data out there for these models. This operation collates practically the entire internet and offers it up to train AIs on a colossal scale.

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But the pool of data has dried up. This isn’t a surprise; three years ago, researchers predicted that AI labs would run out of new training data by late this year. And indeed, a report from Stanford has found that the pool of human-generated data has been exhausted, and the supply of new data is not enough to meet AI labs’ ludicrous demand.

This had motivated these AI labs to desperately try to fill this void while spending as little cash as possible.

AI labs are using third-party contractors to pay people a shockingly low wage to generate high-quality conversational training data. These remote workers, most likely in developing nations, are paid pennies on the dollar to effectively talk to chatbots for hours on end. This won’t solve the AI data problem, given that this method physically can’t produce enough data, but it reduces the issue in the short term.

However, what happens when you pay humans a horrifically low wage? They find shortcuts.

The New Scientist recently reported that many of these workers aren’t actually talking to these AIs but getting an AI to respond. As such, this huge push to source human-generated data is actually causing the AIs to be fed their own output.

One of the workers New Scientist interviewed, called “Alice”, said she feels “not in the slightest” guilty about using ChatGPT to complete these training tasks. She claimed that it is easy to get away with and that “If these companies want quality data, then they should offer quality contracts.” I couldn’t agree more, Alice!

So, by being massive cheap skates and paying people wages that amount to abuse, these contractors and AI companies have effectively kneecapped their data generation scheme. Rather than getting a large amount of high-quality human-generated training data, they now have a data set containing an awful lot of AI-generated training data.

You do not want to train AI on AI-generated data. This is known as “AI inbreeding”, and it’s an ouroboros that ends in disaster — namely, model collapse.

AI-generated content contains tiny, almost indistinguishable trends that human-generated content doesn’t have. That is why we feel we can spot when AI has written something. If you then start feeding this data back into the AI model, it will place more and more weight on these non-human trends. At first, this looks like the AI becoming less capable, but if you continue feeding it its own output, it will eventually place more weight on the generated trends than on human ones, causing the model to collapse and produce gibberish. This issue compounds further when the AI-generated data from a previous, less capable model feeds into the training set. Studies like this one have shown just how rapidly model collapse can occur.

So, not only can these third-party contractors not produce enough data to satisfy the AI companies’ demands, but also because their operations are so grotesquely cash-tight, the data they do generate is actually hurting the AI models they are supposed to help train.

And this isn’t the first time AI companies skimping out on wages has shot them in the foot.

Three years ago, I covered a very similar story about AI data labellers — people who label data so the AI has context for what the data actually is (such as labelling a picture of a dog a dog) — who were so underpaid at just $2 per hour that they asked ChatGPT to write the labelling summaries. It’s almost like this moronic industry is incapable of introspection and learning; how ironic!

Then, there is the rather obvious issue of poisoning the well.

Almost all of that data used to train AI is scraped from the internet. Yet over half of the new content being posted online is AI-generated. So, AI companies have literally poisoned their well. They can use AI detection tools to identify and remove AI-generated data, but these tools are not particularly effective, with some only correctly recognising AI-generated content 57% of the time. Not to mention their false positive rate, where these tools label human-written content as AI, is considerable, with one study even finding a 50% false positive rate for a leading tool. So, AI detection tools will allow a sizeable amount of AI-generated content through their filter and scrap a vast amount of actual human-generated data.

It’s an ouroboros within an ouroboros… within an ouroboros.

But it gets worse!

Much of the scraped training data comes from online articles, as they are abundant and mostly high quality (or at least far higher quality than the likes of social media). However, since the advent of AI tools like Google’s AI search summaries, the publishers of these articles have witnessed a catastrophic drop in viewers, as readers are consuming the summary version of their work instead of clicking the article. Indeed, a recent report from Chartbeat and Axiosinvestigated how Google’s AI search summaries were affecting online publishers of all sizes and found that many had had their viewership drop by as much as 60%! This will dramatically reduce their revenue, forcing them to either accept the pay cut, resort to producing copious amounts of AI-generated slop to make up for the loss, or simply pack in operations (read more here).

In short, the use of these AIs is causing one of the most valuable sources of training data to both shrink and become far more infected with AI-generated content…

Oh, and do you want to know the real kicker? None of this matters anyway.

OpenAI researchers have found that adding more data won’t make AI more accurate. In fact, they found no viable way to reduce their hallucination rate. This means these AI models don’t actually need exponentially more data. So, why are they doing this? Surely it would be more productive to ensure they had a smaller amount of higher-quality data?

Well, the AI bubble itself is actually a compute chip rally. Chip manufacturers like Nvidia and cloud operators like Google or Amazon are AI’s main investors. They pour tens of billions of dollars into AI labs, despite the fact that they are so unprofitable that they should have gone bankrupt years ago, because they know that investment will come back to them, as these AI labs spend their tens of billions on AI cloud computing and chips. This circular financing scheme and its associated AI bubble are predicated on the idea that AI will need exponentially more and more data, and therefore exponentially more compute power and chips. So, if the likes of OpenAI want the hundreds of billions of dollars in investor cash they desperately need over the next few years to prevent bankruptcy, they need to find more data to justify buying compute power and chips from the people who will invest in them.

That is how moronic this entire plan is. These AI labs have admitted that they do not need this exponentially ballooning pool of training data. But, because of the stupid circular nature of this myopic bubble, they are storming ahead anyway. It’s just unfortunate that their deeply unethical method is actually poisoning the AI, as they are inadvertently feeding its own shitty output back to itself.

The AI data economy is an absolute shambles, and this is a setup for disaster.

Thanks for reading! Everything expressed in this article is my opinion, and should not be taken as financial advice or accusations. Don’t forget to check out my YouTube channel for more from me, or Subscribe. Oh, and don’t forget to hit the share button below to get the word out!

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AI is in a really weird place right now. The public’s collective sentiment towards the technology is at an all-time lowand is somehow still going down. Corporations have only just discovered how expensive and useless AI is and are poised to majorly U-turn on their AI rollout plans (read more here). Yet valuations of AI companies are through the roof. For example, OpenAI was only worth $29 billion back in 2023. Since then, its annual revenue has increased by roughly seven times, but it is expected to be valued at over $1 trillion in its coming IPO, or 34 times its 2023 valuation. There is a tension here that is begging to break, and it might soon. You see, over the past few months, people have put actions in motion that could lead to the death of consumer AI.

Okay, that is a bold statement, so I should probably define what I mean by death. I don’t mean the technology will no longer be available or used. I mean that the current vision of consumer AI — where it sits between us and what we want at every conceivable place possible — dies. LLMs, chatbots, and people dabbling with AI will most certainly still exist; that isn’t up for debate. But the consumer AI business model that this entire bubble is partially predicated on will no longer be viable.

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If you have been paying attention to the news, you might already know where this is going.

Google recently lost a court case regarding its AI-generated search summaries in Germany. The court ruled that these AI summaries are the company’s own content and are therefore legally liable for any false claims the bot spits out. The implications for Google are immense, which we discuss soon, but this will also seriously impact other AI companies. For example, xAI’s proclivity for producing unconsensual sexualised images or even CSAM could be framed as xAI’s legal liability and not the users’.

Now, Google plans to appeal this ruling, but I don’t rate their chances, as any defence they could make is nonsensical from a copyright point of view. AI labs have been able to get away with stealing vast reams of copyrighted data to train their AI under the guise of ‘fair use’. To qualify the unpaid use of copyrighted material as ‘fair use’, you need to transform the material and make it something different. This is why YouTube film critics can use clips of films without facing a copyright claim, because they are transforming the material into something fresh, rather than just reproducing it. AI labs like Google, OpenAI and Anthropic have claimed that training an AI on copyrighted material counts as transformation. But that, in turn, means that they are the originator of the AI’s output and are therefore liable for whatever it claims. Again, if a film critic YouTuber were slanderous, they would personally be liable for it, rather than the creator of the movie they clipped. So, if Google wants to claim that the AI isn’t transformative and that it simply regurgitates what it finds in its training data, then it will undermine the entire industry’s argument of fair use. And boy, if you think the AI industry’s financial woes are bad now, just wait until they have to pay the people they stole from!

But I can see why Google has appealed, because they have to if they want to continue rolling out AI in the same manner.

Ultimately, AIs ‘hallucinate’. I hate that jargon — it’s just humanity anthropomorphising a soulless machine, which clouds our judgement. AI ‘hallucinations’ are just errors. AI is a statistical model, and that means, statistically, it will get things wrong. New benchmarks have shown that even the best AI models still ‘hallucinate’ 22% of the time, and those models are extremely expensive to run. The lighter, cheaper models used for things like Google’s AI search summaries have a considerably higher hallucination rate.

You might think that ‘hallucinations’ can be solved by pouring more training data and compute power into these AI models. Indeed, a decade ago, there was talk of AI potentially having ‘emergent properties’ once it scaled that would totally eradicate issues like hallucinations. Sadly, that was just science fiction. OpenAI researchers recently discovered that, no matter how much data or power you put into these machines, the hallucination rate won’t be meaningfully reduced from today’s levels. In fact, they found no viable way to reduce hallucinations or even mitigate them.

And it isn’t just OpenAI that has discovered that hallucinations are here to stay. Vishal Sikka and his son Varin Sikka published a paper which claims to mathematically prove that AIs “are incapable of carrying out computational and agentic tasks beyond a certain complexity”. In other words, the maths says AIs will always hallucinate. Likewise, there is a considerable amount of research into the efficient compute frontier, which describes how AIs need exponentially more data and compute power to increase their accuracy at a linear rate. This basically makes a hallucination-free AI impossible, as there simply isn’t enough data or computing power available. Then there is the Floridi Conjecture, which posits that AI systems can either have great scope but no certainty or a constrained scope and great certainty. LLMs like ChatGPT and Google’s search summaries are incredibly broad applications for an AI, meaning they can’t have great certainty, which is just another way of saying they will constantly hallucinate.

In layman’s terms, all the science points to the fact that Google can’t prevent its search summary from hallucinating.

This makes the German ruling a bit of a problem.

How can Google stop their AI search summaries from spraying out misinformation and rendering them liable for whatever damage is caused?

I mean, sure, they could attempt the arduous task of ensuring the AI is exclusively trained on trustworthy sources and only summarises them, rather than pulling from places like Reddit (which is how we got the famous incident of this AI stating you should put glue in your pizza and eat rocks). But even then, these AI search summaries will hallucinate at a staggering rate and, thanks to this ruling, leave Google liable for their mistakes.

This is why this ruling is potentially catastrophic for consumer AI.

The current business model for consumer AI is to use it as a filter through which we access information. The Google AI search summary is a great example of this. You don’t actually click and read the website with the answers — the answer is filtered through the AI. In fact, most consumers use chatbots like ChatGPT effectively as search-engine-style summary bots, like Google’s, because these bots are useless at agentic tasks. This approach enables Big Tech to implement AI almost everywhere and force people to use it, driving up metrics and, in theory, driving up revenue.

However, this system only works if those providing the AI aren’t held accountable for its errors/hallucinations. As soon as even a modicum of liability is imposed, this entire model becomes legally unviable.

Because consumers hate AI more than they use it, and this ruling potentially sets a damning precedent for Big Tech (that could easily impact them globally), the trap is set for the death of consumer AI. Consumers do not want AI standing between them and what they want, and using AI in this manner could soon place enormously expensive legal liabilities on AI companies, who already have colossal legal and financial woes. If legal systems hold their ground and actually hold these companies liable for the mistakes their tools make, then there is a reasonable chance that consumer AI in its current guise will disappear. However, in today’s deranged world, that is sadly a very big “if”.

Thanks for reading! Everything expressed in this article is my opinion, and should not be taken as financial advice or accusations. Don’t forget to check out my YouTube channel for more from me, or Subscribe. Oh, and don’t forget to hit the share button below to get the word out!

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SpaceX’s IPO was always going to be a wild, erratic and chaotic ride. That is why I have waited so long to comment on it. Due to the obviously BS nature of SpaceX’s business plan and the rigged way the IPO was launched, it was anyone’s guess as to what would happen. . You might think that because Elon Musk is now comfortably the most grotesquely greedy man alive — sorry, I mean, the world’s first trillionaire — that this IPO was a success. After all, the pathetic man got the hollow title he desperately craved so that he could feel something. But now, a few weeks down the line, we can see that this IPO is actually quite a mixed bag.

So, let’s start with a positive point. It was a success because SpaceX managed to sell its shares at a $1.77 trillion valuation, raising just over $85 billion, which is $10 billion more than they targeted. That makes this by far the largest IPO in history. The fact that they were able to pull this off is objectively impressive — particularly when you realise that many analysts claimed SpaceX was worth half of this colossal figure.

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But this IPO was heavily engineered. SpaceX only floated 5% of its stock, with 70% of that reserved for retail investors, and many brokers heavily reduced the barrier to entry to buy the IPO. That means the supply was greatly restricted and demand skyrocketed because a lot of non-professional investors were suddenly able to buy it. So, while it is impressive that this IPO went well at all, a sizeable chunk of its success is thanks to this manipulation, not necessarily SpaceX itself.

Okay, but what is an IPO actually for? Well, to raise cheap funds for the company to grow rapidly. Indeed, the general notion is that this mountain of cash will be used to finally get Starship working (which it won’t, read more here) and to put a ton of AI data centre satellites in space (which will never be economically viable or scalable, read more here).

Unfortunately, to meet these goals, $85 billion is nowhere near enough. As Cape Fear Advisors points out, SpaceX faces around $235 billion in spending commitments through to 2030. On top of that, SpaceX was going to use its IPO to pay down an extremely expensive $20 billion loan (which SpaceX inherited from xAI and X), meaning that only $65 billion from this IPO would go to these spending commitments over the next three and a half years. That is just 27.6% of what they need! But, in reality, the financial situation is likely even worse. Cape Fear Advisors did a fantastic job going to primary sources to figure out SpaceX’s actual spending commitments for things like Terrafab (because they were omitted in the S-1). However, the costs associated with developing Starship and orbital AI data centres, and then deploying them at scale, don’t have such solid sources, and SpaceX has a history of massively underplaying said costs. I, and many others, think these costs could be enormous. As such, Cape Fear Advisors’ figure could be seen as a solid minimum spend commitment, as there is the possibility it could be noticeably larger.

Either way, over the next few years, SpaceX will have to raise hundreds of billions of dollars more in cash, or its main value proposition will fail.

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It seems like everyone, including the AI bros, now know that the AI industry is one of the biggest bubbles humanity has ever created. Yet, rather than actually do something about it, like regulate Big Tech, enforce anti-trust laws, crack down on round-trip investments, or have the stewards of our delicate financial systems tell these morons to f**king stop it, we collectively seem to be enabling these idiots and letting this entire circus self-implode. The trouble is, when something is this detached from reality, devoid of logic, and utterly irrational, like the AI bubble, all the typical forces that would cause said self-implosion achieve nothing. They can seemingly just choose to ignore something that would be a fatal blow to any other business/industry and carry on unscathed. It is going to take a mind-altering tsunami of reality to break this cult-like hysteria and pop the bubble. Well, that sucker punch of reality is just over the horizon.

As with all bubbles, this starts with the biblical cash bonfire at its heart.

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The poster child for the cash black hole is OpenAI. Despite a reported $5.7 billion revenue in Q1 2026, they posted a non-GAAP loss of $6.9 billion. That means they lost $1.22 for every dollar of revenue they made. To make matters even worse, GAAP stands for ‘Generally Accepted Accounting Principles’, and non-GAAP accounts can exclude certain expenses, such as restructuring costs and certain kinds of depreciation, both of which OpenAI has in spades. So, their actual loss is likely noticeably higher. This is after racking up a $38.5 billion loss last year (read more here).

Oh, and don’t go thinking Anthropic is any better. They have tried to make it look like they are close to profitability, but this isn’t really the case. As Ed Zitron points out, this profit comes from an unsustainable market position and sleight-of-hand accounting that doesn’t reflect their reality (EBITDA profitability).

At this rate of burning cash, OpenAI and Anthropic are on a one-way trip to bankruptcy. In fact, OpenAI is on course to go bankrupt this year or early next year, even with the substantial private investment it receives (read more here).

So, what are they going to do to solve this?

Well, thanks to AI’s insanely poor economies of scale, they can’t really lower their costs. Plus, due to the circular-financing nature of the AI bubble, reducing their costs would also reduce the flow of incoming investment from their Big Tech investors (read more here), and they need that to pay for their losses.

Logically, the only other option is to increase the amount of incoming money. That is why Anthropic and OpenAI have shifted to token-based pricing rather than flat-fee subscriptions and are preparing for IPOs.

Let’s start with the switch to token pricing. We have known for a long time that AI subscriptions were heavily discounted. Famously, OpenAI was losing thousands of dollars per month on its $200 subscriptions. Not only that, but because this was a flat monthly fee, the revenue these customers generated wasn’t proportional to the cost to service them. So let’s say these customers tried to maximise their use of this AI; the losses incurred could spiral. In other words, this sales structure doesn’t give the vendor any real ability to control or predict losses.

That’s why, a few months ago, OpenAI and Anthropic switched to token-based pricing, which meant the price a customer paid was proportional to the cost of serving them. To be very clear, the current token prices are likely still discounted to the point of making losses. It’s just that OpenAI and Anthropic can actually decide how much money they are losing, which potentially allows them to reduce the amount.

However, this has had an enormous impact. It turns out, all the AI bros, the heaviest users of their products, had been using this service at a catastrophically low cost. So even this tiny correction, which is likely still losing them money, has made many AI services so expensive that they are functionally useless. It’s like renting someone a Ferrari for $10 a month — of course, they are going to love it. But as soon as you charge them the actual cost, they might suddenly not like it anymore.

For example, the cost of GitHub’s Copilot AI has increased by 10x to 50x for many users; Uber blew through its entire 2026 AI budget in just a few months; Microsoft has cancelled its Claude Code licence because it cost too much; and one company racked up a half-billion-dollar bill with Anthropic in a single month. In fact, leaders at Uber have said that AI is now more expensive than human workers. Yowch.

Token-based pricing has shone a light on a major flaw in the AI industry. It was only useful to people when sold at a catastrophic loss. As soon as these companies tried to even control how much money they were setting on fire, the costs became too much, destroying the very ‘usefulness’ of these tools.

I genuinely think the people at Anthropic and OpenAI knew they would receive a significant amount of backlash for the switch to token-based pricing. But they had to do it anyway.

These AI labs and the ecosystem they live in have funded themselves through private equity sales and debt. But these sources of funds are beginning to dry up. Venture capital firms are struggling to raise enough capital to invest, their Big Tech backers are nearing the limit of how much they can invest, and the bond market is so saturated with risky AI debt that borrowing costs are getting too high. These sources of cash simply can’t provide them with enough to survive anymore.

So, if selling AI services at a profit renders these tools useless because the cost is too high and their current source of funds is rapidly drying up, how can these AI companies bring in the cash they need to stave off bankruptcy for another year?

Simple. Go public. Let the retail investors who have been forcibly fed AI propaganda for years hand over their hard-earned cash to be burned. How about their pension funds, too, while we are at it? This is the only capital market the AI bubble has yet to fully tap.

There is just one problem. They have colossal, uncontrollable losses on their books, which makes an IPO impossible. Even after the insanity of the SpaceX IPO, they need to show some level of progress towards sustainability and profitability if they want the IPO to go even half as well as SpaceX’s. Let’s not forget, Altman and Amodei do not have the cult-like salesman skills of Elon Musk, so they need something more material to peddle their snake oil.

That is why they had to switch to token-based pricing: it makes it appear like they are making progress by controlling their losses, even if only for a short while. That alone is worth the backlash and worth releasing the biggest secret of this industry, that these models are exponentially more expensive than anyone thought, because it enables them to go public. And wouldn’t you know, that is exactly what they are doing.

However, there is one giant stick in the mud here.

OpenAI lost a ton of market share to Anthropic early this year, and the two have been in a battle for AI customers ever since. But this price jacking and widespread backlash are the perfect environment for both companies to grab huge slices of the market.

So, both Anthropic and OpenAI are considering making huge price cuts to gain more customers. They can do this because the timing of these cuts means they likely won’t be reflected in their IPO financials, as they will be for the previous quarter.

But do you see the problem here?

Yeah, cutting prices is just going to make their already bad money situation even worse. If you think these companies are experiencing horrific losses now, just wait until this strategy takes effect! Going forward with these price cuts is just speedrunning bankruptcy — especially when it isn’t that hard for users to switch to a different AI model. So, even if they can stomach the losses, when they eventually come to put the prices back up, the customers they once gained will just jump ship to whoever is cheaper at that time.

These AI companies are now effectively trapped in a giant trillion-dollar game of chicken. This is the setup for a rapid race to the bottom, followed by a devastating cash crunch. It has the potential to take all the oxygen out of the AI bubble and provide that heavy dose of reality that eventually pops it. That is, unless something utterly unprecedented and drastic happens soon.

That is the issue with bubbles like this. We know it is a bubble, we know it will pop, and we can see many different ways it could pop. But the markets can stay irrational far longer than you might think, so trying to predict when it will all come crumbling down is a fool’s game. All I can say is that the biggest problem the AI industry faces right now is about to get even worse.

Thanks for reading! Everything expressed in this article is my opinion, and should not be taken as financial advice or accusations. Don’t forget to check out my YouTube channel for more from me, or Subscribe. Oh, and don’t forget to hit the share button below to get the word out!

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I, along with many others, have been yelling from the sidelines that AI can’t replace jobs for a while now. They cost too much, are far too inaccurate to be genuinely autonomous (given that they aren’t actually intelligent), and don’t increase productivity in a way that threatens human labour on any meaningful scale. Well, it turns out the people who have been almost pervertedly pushing for AI to eradicate human workers have only just discovered the truth and are now rapidly U-turning on this ‘revolutionary’ technology. Who would have guessed?

There is a lot of evidence out there that the corporate world is rapidly bailing on AI.

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Forrester Research found that 55% of employers who laid off workers because of AI now regret that decision. OrgVuefound something almost identical, with 55% of business leaders admitting they made the wrong call eliminating jobs and replacing them with AI. Earlier this year, Gartner predicted that half of all companies that replaced customer service or operational employees with AI will be forced to restaff those roles. Sinch, a cloud communications company, surveyed 2,500 AI decision-makers across multiple industries and countries and found that AI customer communications agents have a 74% rollback or shutdown rate. Customer communications was once considered one of the easiest jobs for AI to replace, yet it is failing catastrophically. Robert Half and Orgvue found that 32% of HR say organisations have already had to rehire positions previously replaced by AI, explicitly because AI couldn’t actually replace these roles.

This has been dubbed the “AI boomerang effect”. This is when companies that once bullishly ploughed headlong into AI layoffs are having to rapidly U-turn, given that their ignorance and unfounded confidence in the technology has been laid bare.

But why is AI failing to replace these jobs? Well, there are actually multiple reasons — namely, cost, hallucinations, and lack of productivity gains.

I have written about the AI cost issue before. In an attempt to stem their spiralling losses, AI companies like OpenAI and Anthropic have switched from using heavily discounted flat-fee subscriptions to token pricing. This way, the amount a customer pays for an AI more closely reflects how much it costs to operate. These token prices are almost certainly still so heavily discounted that OpenAI and Anthropic lose money on them, but this allows them to better control their losses.

However, this change has made many AI tools skyrocket in price. For example, the cost of GitHub’s Copilot-based AI has increased tenfold; Uber has said that AI is more expensive than human workers; Microsoft has cancelled its Claude Code licence because it is too expensive; and one company even racked up a $500 million bill with Anthropic in a single month.

Quite simply, even if AI were able to replace human labour, it would be more expensive than human labour in the overwhelming majority of cases, making AI layoffs a pretty dumb business move. Particularly when these AI companies are still selling their models at a catastrophic loss and will almost certainly have to massively jack up the prices down the road. So, firing cheaper labour is doubly stupid because it makes your business dependent and vulnerable.

But, here is the thing: AI isn’t capable of replacing most jobs because it isn’t actually intelligent; it’s just a statistical model, meaning it makes constant errors, also known as hallucinations.

AI hallucinations prevent these machines from being able to do tasks autonomously. For example, Carnegie Mellon University found that even the best “agentic” AIs fail basic tasks 70% of the time. Another study found that the best current AIs failed 97.5% of realistic real-world freelancing jobs. Both of these studies specifically highlight AI hallucinations as the primary cause of this astonishing failure rate.

You can’t simply ask an AI to complete a task; it requires extensive human oversight to catch and mitigate these errors. In no task is this issue better exposed than in coding. The University of Waterloo found that even the best generative AI coders only have a 75% accuracy rate when tasked with very basic coding tasks. Research from Veracode found that 45% of AI-generated code contained security flaws. A study from Coderabbit found that AI-generated code has 70% more bugs than human-written code. In fact, both Anthropic and METR found that AI coding tools actually reduce productivity, because the time the coder saved by using AI to write the code is less than the time they spent debugging the AI-generated code. In other words, it is often faster to write and debug code by hand than to use AI tools to code and spend hours trying to debug it to an acceptable level.

We often see this problem with AI. It might appear to dramatically increase your productivity for a single task, but once you zoom out and look at your overall productivity, those gains totally disappear. As such, we don’t actually see any AI productivity gains on an economic, organisational, or individual scale. Take this study published in February, which surveyed 6,000 CEOs, CFOs and other C-suite executives across various countries. Nearly 90% of these firms said AI has had no impact on employment or productivity over the past three years. Likewise, the recent Oxford Economicsreport says that if AI were increasing productivity enough to replace jobs, we would see that in economy-wide productivity data, but we don’t; our productivity is actually quite stagnant. In other words, AI does not increase overall productivity in the overwhelming majority of cases.

You might think that with the current colossal investment in AI, these systems will become better and soon solve these problems. But that isn’t the case. Even OpenAI has admitted that they can’t reduce hallucinations from their current level, and the cost of operating these models is only increasing (read more here). There will be tweaks and refinements that make these models less dreadful, such as with Anthropic’s new batch of models, but they will still suffer from these critical flaws.

The entire AI bubble is predicated on the idea that AI will take jobs and generate hundreds of billions of dollars in annual revenue within just a few years. If that doesn’t happen, the AI industry can’t service its debts, and the whole circus collapses.

The fact that the AI boomerang effect exists at all is seriously bad news for the AI bubble. But we have only just begun to witness this U-turn. It takes time for companies to notice that AI is damaging their bottom line. Many companies feel behind on the trend of adopting AI, and so are blindly and rapidly adopting it out of FOMO. When AI companies run out of cash to burn, they will drive up prices, causing more businesses to ditch them. All of these factors suggest that the AI boomerang effect is likely only just getting started and that we will only see this trend strengthen over time. What does that mean for the economy, businesses, or the AI bubble? I don’t know — I don’t have a crystal ball. But what I do know is that this is not good news for the AI industry at all.

Thanks for reading! Everything expressed in this article is my opinion, and should not be taken as financial advice or accusations. Don’t forget to check out my YouTube channel for more from me, or Subscribe. Oh, and don’t forget to hit the share button below to get the word out!

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Have you noticed that the AI bros are starting to panic? All of a sudden, they have dramatically U-turned on the notion that AI will take vast swathes of the job market despite that claim being critical to AI’s hype and the entire point of this ‘revolution’, they are desperate to gain US government backing, and they are pushing some of the most overpriced IPOs in history despite all of their companies being closer to a cash bonfire than a functional business. This is peak frenzy behaviour, the kind of stuff you see right before the bubble bursts. It is a thinly veiled, desperate attempt for them to find a stooge of a bagholder and cash out before the entire thing collapses. But why? Well, a lot has changed over the past few weeks, so let me walk you through everything.

The Debt & Bet

Here is a question: how much money do you think has been invested in the AI industry?

According to Reuters, investors and companies had poured around $2 trillion into AI by the end of 2025. But that isn’t quite the whole picture, as the AI industry has not just received cash investments but also racked up a colossal amount of debt. Much of this debt is obfuscated using weird company structures, which means Reuters probably hasn’t taken all of it into account.

J.P. Morgan alone has $1.5 trillion in AI-related debt. AI debt also makes up roughly 15 -20% of corporate bond indices(referring to debt a company issues, not a bank), meaning there is well over $1.5 trillion in AI-related bond debt in the open market, too. In fact, AI is now linked to more debt than banks!

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It is difficult to pull together an accurate total figure, but we can be sure that multiple trillions of dollars have been invested in the AI industry with much of that being debt. This debt is also set to balloon, with just a handful of companies poised to rack up another $1 trillion in AI-tied debt over the next few years. So, in the next year or two, AI-related debt alone could easily reach $3 trillion.

This creates an implicit bet.

To provide some rough context, let’s say that the entire AI industry has $3 trillion of bond debt, at a typical market rate of 3% over a ten-year period. Paying the interest and capital each year will total $309 billion.

This means that for the AI industry to service its debt, it needs to generate hundreds of billions of dollars in profit each year.

Even giant monopolies like Google don’t make enough profit to service that much debt. AI can’t just be a novelty industry; it needs to replace human labour on a colossal scale to service this debt. Let’s optimistically assume AI one day reaches a 10% profitability margin, a cost parity with human labour, and the ability to complete most jobs (none of which are currently the case). Well, the average US salary is roughly $66,000, so at a 10% profit, the AI company will make on average $6,600 per year per job it replaces. To generate the $309 billion needed to service their debt, the AI industry will need to replace 46.8 million jobs, equivalent to around 27% of the current number of jobs in the US.

While this is all very rough maths, it highlights the implicit bet created by the debt the AI industry has racked up. To simply not default on this debt, the AI industry has to rapidly displace human labour at a staggering scale, even if we are extremely optimistic about AI’s economics. In other words, the AI industry has made a gargantuan bet that AI will create a jobpocalypse, and if it fails, their entire industry, and much of the financial systems that our modern society depends upon, are on the line.

This is not just my opinion, but that of “AI Godfather” Geoffrey Hinton.

There is just one problem with this bet: it obviously isn’t paying off.

No Jobpocalypse

You may have noticed that, despite a whirlwind of propaganda trying to convince you otherwise, people are not losing their jobs to AI — at least not at a meaningful scale.

Oxford Economics has found that companies “don’t appear to be replacing workers with AI on a significant scale” and instead suggest that they are actually using the AI layoff narrative to cover up their own shortcomings.

Why? Because AI doesn’t actually increase overall productivity. It might increase the productivity of a single task when quality control is disregarded, but this increase doesn’t scale to job, organisational or economy-wide productivity growth. Indeed, Oxford Economics said, “If jobs are being replaced, where’s the productivity surge?” which is apt, given that economy-wide productivity is relatively stagnant at the moment.

More recent studies have supported this theory, such as this one, which surveyed 6,000 CEOs, CFOs and other C-suite executives across various countries, nearly 90% of whom reported that AI has had no impact on employment or productivity over the past three years.

In fact, we are actually seeing the opposite effect.

Companies like Starbucks and Pizza Hut tried to deploy AIs to replace human workers, but performance issues forced them to pull those systems. . Sinch also conducted an AI production paradox study. It surveyed 2,500 AI decision-makers from across multiple countries and industries and found that AI customer communications agents have a 74% rollback or shutdown rate. Customer communications was considered one of the easiest jobs for AI to replace, yet it is failing catastrophically. Indeed, many firms that claimed to replace workers with AI have had to desperately rehire them because it did not work.

But Jobpocalypse Soon?

Okay, so why is AI struggling to replace jobs? Is it because it isn’t being deployed correctly? And will the immense amount of investment being poured into AI solve this problem in the near future?

AI is struggling to replace jobs because it keeps making mistakes. For example, Carnegie Mellon University found that even the best “agentic” AIs fail basic tasks 70% of the time. Another study found that the best current AIs failed 97.5% of realistic real-world freelancing jobs. These studies identified AI errors, or “hallucinations”, as the primary cause of this astonishing failure rate. This is why AI doesn’t actually increase productivity or threaten jobs.

Take coding, for example. The University of Waterloo found that even the best generative AI coders only have a 75% accuracy rate when tasked with very basic coding tasks. Or what about research from Veracode, which found that 45% of AI-generated code contained security flaws? Or the study from Coderabbit, which found that AI-generated code has 70% more bugs than human-written code? A human coder might save a lot of time using AI to write their code, but they will have to spend more time debugging and correcting said code than the time the AI originally saved them. Indeed, METR found in their last conclusive AI coding study that these tools actually decrease coders’ overall productivity because they spend so much more time correcting the AI’s mistakes.

This is not a flaw that can be solved with a different kind of deployment. These systems require extensive human oversight in order to locate and mitigate hallucinations to function correctly. As such, AI inherently can’t replace human labour at any meaningful scale.

But will this always be the case? There is a boatload of money currently being poured into AI to give it more data and computing power to make these AIs more capable and accurate. So, surely something will solve or reduce this problem, right?

Well, no.

OpenAI’s own research found that increasing the computing power behind AI, or providing it with more data, can’t reduce AI “hallucinations” below its current level. In fact, they found no viable way to reduce AI hallucinations. This has been supported by a more recent paper by Vishal Sikka and his son, Varin Sikka, which found that AIs are mathematically incapable of reliability or carrying out computational and agentic tasks beyond a certain complexity. This is because AI isn’t actually intelligent; it is just statistics, and there is a limit to how accurate a statistical model can be.

In other words, AIs are not going to get much more accurate than they currently are, meaning they can’t boost productivity on a wide scale or replace jobs anytime soon. To do that, an entirely different technology is needed, which we simply don’t have.

The Costpocalypse

But, even if AI were able to replace jobs, it wouldn’t actually solve the problem at hand, because over the past few months, the AI costpocalypse has reared its ugly head.

For reasons we will get into in a second, AI companies have recently switched most of their enterprise contracts from a flat subscription to token-based pricing. This effectively means that companies will be charged relatively for the cost ofoperating these AIs, rather than a heavily discounted flat fee.

The fallout of this slight change has been monumental.

The cost of GitHub’s AI has increased five to nine times; Uber blew through its entire 2026 AI budget in just a few months; Microsoft has cancelled its Claude Code licence because it cost too much; and one company racked up a half a billion dollar bill with Anthropic in a single month.

In fact, leaders at Uber have publicly stated that AI is now more expensive than human workers. But they also said that AI usage doesn’t correlate to delivering new features, meaning that, unlike human workers, AI has no real measurable return on investment (ROI).

This costpocalypse is turning many AI bulls into bears, as the cost of AI not only dwarfs that of human workers but also forces them to pay attention to AI’s complete uselessness.

This all heavily suggests that corporate use of AI is set to dramatically drop. When you consider that all the major AI labs and their backers were relying on widespread corporate use to generate them the vast sums of revenue they need to pay their colossal debts, that isn’t good news.

So, even if AI could be improved to the point where it can actually complete tasks effectively, it won’t replace human workers, because it costs more than they do. This is why every AI CEO has suddenly U-turned on their crass, degrading and aggressive jobpocalypse narrative, because you can’t go around saying your AI is going to take people’s jobs if the AI costs significantly more than the workers — it makes you look like a total idiot. But this begs the question: where are these companies going to get those hundreds of billions of dollars to pay their debt?

The Cash Drought

Okay, but first, why did they switch to token-based pricing? Surely they knew such a cost increase wouldn’t do their already bad PR any favours.

Well, truth be told, they had to, because they are running out of cash at an unprecedented rate.

AI companies are chronically unprofitable. Despite OpenAI raising an insane amount of investment over the past few months, some analysts are predicting it will declare bankruptcy in 2027 (read more here); that is how much cash it is bleeding. In fact, OpenAI’s losses are only increasing as the company grows, with its operating margin for 2026 Q1 being a staggering -122%, meaning it lost $1.22 for every dollar of revenue it made. Anthropic has tried to meekly suggest that its books are better, but as Ed Zitron has pointed out, this is more of an accounting trick and market circumstance than actual profit. There is also the context here that both Anthropic and OpenAI are in this position despite them running on heavily discounted compute supplied by their backers like AWS and Microsoft (as per Ed Zitron). This means that all major AI suppliers are losing money hand over fist, even though their operating costs have been significantly artificially lowered.

To ward off bankruptcy, these AI companies need a constant and ever-growing flow of investor cash. Initially, this was provided by selling equity to Big Tech backers and venture capital firms, but these sources of money have begun to dry up. So, they switched to the debt market through corporate bonds, bank loans and private credit. But now these sources are drying up too. Risky AI debt has saturated the corporate bond market so heavily that borrowing costs have skyrocketed to unusable levels.

The flow of actual cash investment into AI companies (not investment in the form of compute vouchers) is slowing down dramatically. If they don’t do something to resolve this issue, their ravenous cash burn will eat them alive from the inside out and bankrupt them.

This is why the industry has turned to token-based pricing, because it can help reduce their losses. OpenAI had to admit a while ago that its top-of-the-line $200 per month subscription was losing a huge amount of money, with some inside the company estimating that if this subscription were sold at cost, it would be more than $2,000 per month. Now that token-based pricing is here, we can see that their claims are relatively accurate. The flat-fee subscriptions that 99.999% of AI users had been paying were aggressively subsidised in an obvious attempt to hook new users. Turning to token-based pricing doesn’t mean AI companies aren’t losing money, but it enables them to control and manage their losses.

That will not only enable them to reduce the cash burn and delay their pending bankruptcy, but it might also make their books look favourable enough to go public. SpaceX will have already gone public by the time this article is published, but OpenAI and Anthropic are poised to follow suit soon after. This will unlock a new pool of money from retail investors and institutional investors like pension funds. But for an IPO to be successful, a company can’t be bleeding cash and on the verge of going bust. Token-based pricing might polish their books just enough for their IPOs to succeed.

The Failed Blitzscale

If this all seems like it was destined to fail, you’d be right. The AI industry has effectively tried to Blitzscale. This is where you use large amounts of investors’ cash to offer a rapidly scalable service at a heavily subsidised rate to soak up as much market share as possible, and once your competition has been driven out of the market (also known as having a monopoly), you can jack up your prices and make a killing. Airbnb and Uber are famous examples of this method.

The AI industry obviously thought that they could out-compete the human job market and replace an incredible number of jobs by selling AI at an astronomical loss. Once this market was disrupted and AI systems became integral to businesses, they could jack up the price and make their bag.

The only problem is the AI is so damn expensive that it has bled their wallets dry before they have enough of a monopoly to be able to squeeze the market and jack up the prices! Not to mention thatAI isn’t good enough to replace human workers, so they were never going to be able to secure enough market share anyway.

The Panic

This is why the AI industry is panicking. Their plan for world domination has utterly failed, and unless they can find a new direction to bring in unprecedented, obscene amounts of revenue, the entire industry will implode under the colossal weight of its own debt.

SpaceX’s, Anthropic’s and OpenAI’s IPOs are a test of this. If a single one goes badly, this bubble will burst, because it is a clear sign that investors aren’t willing to keep feeding this cash-hungry monster. That revelation will stop or slow down the flow of cash into these AI giants, which will kill them.

But even if these IPOs go well, they aren’t out of the woods yet. The amount of cash these IPOs will generate is only enough to keep these companies going for another year or two, maybe three if they are lucky. In that time, they have to somehow find a use for AI that is profitable and will generate hundreds of billions of dollars of profit a year just to prevent their debts from eating them alive. The chances of them doing that are functionally zero. So even if these IPOs go well, the companies are still doomed; they have just delayed the inevitable, and AI bros now know it.

The myth of AI is rapidly dying, and the grifting leaders of the industry and their moronic backers have only just realised they have drunk their own Kool-Aid. Only now do they understand that AI is too inaccurate to increase overall productivity and too expensive to replace human workers, and that the vast amount of debt they have racked up on the premise that AI will replace the job market is now threatening to crash down on their heads hard. Expect more panicking, more U-turns, more pleas for government backing, more desperate new directions, and more delusional outbursts from these people, because the AI bros are now desperate to keep this gravy train going at any cost. But sadly, the question isn’t whether this entire thing will collapse, but when.

Thanks for reading! Everything expressed in this article is my opinion, and should not be taken as financial advice or accusations. Don’t forget to check out my YouTube channel for more from me, or Subscribe. Oh, and don’t forget to hit the share button below to get the word out!

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Everyone with even a modicum of sense has been predicting OpenAI’s self-inflicted demise for a while now. It is painfully obvious that this is not a sustainable business. Their product is insanely expensive and has basically no economies of scale, and while it might be impressive to a specific breed of narcissists and sycophants, it simply doesn’t bring in enough cash to cover its ludicrous operational costs. OpenAI is more akin to a cash-hungry black hole than a sustainable business. Many, including myself, have predicted that it will go bankrupt soon (read more here). But it turns out OpenAI’s ship is sinking significantly faster than even the most pessimistic predicted. Let me explain.

This revelation comes from Ed Zitron, who received an exclusive look at OpenAI’s IPO filings, which detailed the company’s actual balance sheet for 2024 and 2025. But before we dive into this information, to truly wrap our heads around the news, we need some context. Currently, OpenAI is a private company, which means that we don’t have a clear view of its financials. So, analysts have had to estimate its balance sheets based on Sam Altman’s breadcrumb clues. Would you like to hear people’s predictions for OpenAI’s 2025 financials?

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On the very optimistic end, one external report predicted that OpenAI would post a net loss of $8 billion in 2025. That is a major escalation from 2024, in which they posted a $5 billion loss and had to effectively be bailed out by their corporate backers. I cannot emphasise this point enough, but an $8 billion loss would still be horrific news for OpenAI, as it proves their losses grow as they scale, not shrink. That heavily implies that, no matter how much they grow, they will always burn cash.

On the more realistic end, others predicted 2025 would be significantly worse for Altman. Take my previous article, in which I used analysis from The Information, which predicted that OpenAI would have operating costs of $28 billion in 2025 and a grounded estimate of $11.9 billion in revenue for the year, resulting in an estimated net loss of $15.6 billion. That is nearly double the previous prediction. This is actually higher than some predictions for their 2026 net loss ($14 billion), and these predictions also maintained OpenAI would go bankrupt by 2027. That should give you an idea of the horrific consequences involved in such a colossal loss.

However, other estimates painted a much more grim outlook. I covered The Information’s reporting on OpenAI’s 2025 H1 financials in my previous article. In the first half of 2025, OpenAI reportedly generated $4.3 billion in revenue while incurring $17.8 billion in operating costs, resulting in a net loss of $13.5 billion. That meant OpenAI was losing about three times more money than it was earning and was on track to post a net loss of roughly $27 billion by the end of the year.

So, in the first half of 2025, OpenAI had already exceeded the more optimistic $8 billion annual loss prediction and nearly met the $15.6 billion annual loss prediction in just six months! Their six-month loss was also triple their posted loss for the entire year of 2024, demonstrating a major escalation.

Now we understand the context, we can talk about Ed Zitron’s exclusive reporting.

Zitron was able to get a hold of audited financial documents for OpenAI’s upcoming IPO that have been independently verified by the FT, which detail the company’s actual finances for 2024 and 2025.

These documents verified that OpenAI did indeed incur $5 billion in losses in 2024. But they also revealed that OpenAI lost $38.5 billion of $13.07 billion in revenue in 2025. That is a nearly eightfold increase from 2024 and blows past even my most pessimistic predictions by more than $10 billion.

Admittedly, there is some nuance to that $38.5 billion figure, given that audited accounting is a lot messier than you might think. As more information comes out, I and many others will delve further into that nuance. However, $38.5 billion is the more realistic overall figure.

As Zitron put it, “$38.53 billion in losses are astronomical and far higher than most believed it would be. Losses also appear to be mounting year-over-year at a dramatic rate, and I’m not sure how this company finds a way toward any kind of sustainability or profitability.”

I couldn’t agree more.

There is zero evidence that any AI company is inference-positive, where the cost of querying the AI exceeds the amount the customer pays. As such, the larger these companies scale, the deeper their losses become, even if they completely disregard the crippling costs of training these AIs. OpenAI’s skyrocketing losses support this theory and strongly suggest that scaling is not a viable path to profitability.

But there is also the fact that AI has to be sold at a loss to attract customers. Take the recent report that discovered AI is more expensive than the workers it is intended to replace, even when it is being sold at an astronomical loss (read more here). So, even if OpenAI charged enough to cover their costs, their customer base would collapse, causing their revenue to collapse, and their losses would skyrocket.

Okay, so what does this mean for the future?

Well, Ed Zitron has said that he will release a much more in-depth analysis of these documents soon, so if you’re interested in reading that, go and subscribe to him. I am sure some part of it will cover what this means going forward.

But we already have some hints as to just how bad 2026 is shaping up to be for OpenAI.

The Information recently released a report on OpenAI’s 2026 Q1 financials. In this quarter, OpenAI incurred a $6.95 billion non-GAAP loss on $5.7 billion in revenue. GAAP stands for ‘Generally Accepted Accounting Principles’ and is the standard for clear and honest accounting. As such, non-GAAP financials can be very deceptive, given that expenses such as restructuring costs and certain forms of depreciation can be completely excluded. Because of this, their actual loss for the quarter is almost certainly much higher than $6.95 billion.

This isn’t great, considering that if nothing changes over the remaining three quarters, OpenAI would post a non-GAAP loss of $27.8 billion for 2026. But we know that OpenAI is losing customers to Anthropic and debating aggressively slashing prices to win them back, while its projected $50 billion in compute and training costs snowball out of control. When we take all of these factors, and the expenses hidden by non-GAAP accounting, into account, OpenAI’s losses for 2026 could be substantially larger than 2025. There is a genuine possibility that OpenAI’s 2026 loss will significantly exceed $50 billion (Zitron has even said it could go up to $70 billion), as that is their current trajectory.

This is a slight problem. As Zitron pointed out, OpenAI only has $50 billion in asset value on its books. You might be forgiven for assuming it has more, with all the recent headlines insisting it has raised over $100 billion in investment over the past few months from the likes of Amazon and Nvidia. But the reality is, the vast majority of that investment isn’t cash but effectively vouchers for OpenAI to use these investors’ data centres.

What does all of this mean?

It means that OpenAI could soon rack up losses that exceed its asset value. In other words, they are on course to slam into bankruptcy this year, or at the very latest, early next year, unless they receive tens of billions of dollars of actual cash investment. Even then, that cash injection will likely only delay bankruptcy until late 2027.

This explains why Sam Altman is angling for an IPO as soon as possible, despite internal pushback. But we now know the causes of this internal pushback and why OpenAI is desperate to get government backing. OpenAI needs an IPO to drum up the actual hard cash it requires to delay imminent bankruptcy and keep the ship going for another year. However, with financials this dire, would anyone even buy OpenAI shares? Is an IPO actually possible? Even if OpenAI has enough meme-stock status to allow it to pull the same trick as SpaceX, that will only delay bankruptcy by a year, maybe a year and a half, tops.

These financials are painfully clear — OpenAI is on a trajectory toward imminent collapse. We already knew this. It was obvious to everyone that this scheme was a car crash waiting to happen. This revelation only establishes that the situation is far more dire, and the crunch is coming far sooner, than anyone thought.

Thanks for reading! Everything expressed in this article is my opinion, and should not be taken as financial advice or accusations. Don’t forget to check out my YouTube channel for more from me, or Subscribe. Oh, and don’t forget to hit the share button below to get the word out!

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I have had a little theory bubbling away in the back of my mind for a while now. Truth be told, it is not a unique theory at all; many seem to have arrived at it independently. You see, a lot of people have been paying attention to the deranged actions of Big Tech CEOs, major business leaders, high-level investors and AI labs and asking, “Are these guys suffering from AI psychosis?” Because, when you stop to think about it for a second, that theory would explain so much of this bubble’s insanity and the factors propping it up. In fact, there are even people within this bubble starting to claim that their peers are being driven by AI-derived delusions. So, do we have a case of mass AI psychosis?

Let’s start with what AI psychosis actually is. It is a new term, so its definition is still a little loose, but Healthline defines it as “a term used to describe psychosis-like mental changes that can happen after heavy use of AI tools, such as chatbots”. More recent studies have sought to determine what actually causes this psychosis. One such study found that the extreme level of sycophancy in AI chatbots can cause spiralling delusions if used heavily, as it erodes users’ sense of reality and isolates them from real, grounding human connections, eventually culminating in AI psychosis. Other studies have backed this theory, finding that AI’s sociopathic behaviour is responsible for creating delusional thinking in its users.

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Let’s not kid ourselves. This isn’t a new problem, particularly in the world of business. Since their inception, the yes-man problem has plagued corporations.

People in positions of power, from CEOs to middle managers, tend to surround themselves with people who agree with them, rather than those who hold them to account or engage in critical thinking. This is because it validates them not just on a personal level but also for others who rely on them. This can be harmless, as long as the systems of accountability and critical thinking remain intact. But as soon as these leaders abandon the uncomfortable truth or undermine accountability and replace it with sycophantic flattery, a death spiral starts. It creates an “organisational blindness” as leaders lose touch with reality and the systems designed to keep them under control are cast aside. Their decisions become increasingly erratic, delusional and self-serving until eventually they are either forcibly kicked out or the organisation crumbles around them.

This yes-man problem is so damaging and so notorious that avoiding it has been an integral part of business management for decades.

But it’s fair to say that in our modern world, we have forgotten those lessons. For a whole host of reasons, from rampant union busting, wage theft, undermining workers’ rights, and overvaluing C-suite executives to extortionate executive pay, the power and decision-making dynamics in modern corporations have become increasingly authoritarian and stratified. In other words, what little upward feedback, decision-making or accountability there once was has been drastically reduced to functionally zero. Corporate control, particularly in tech companies, venture capital firms, and rapidly growing startups, is now concentrated at the tippy top with no accountability systems in place. This is a perfect environment for the yes-man problem to rear its ugly head, given that becoming a sycophantic yes-man is one of the only ways to climb that corporate ladder.

But it is also an environment that leaves these decision-makers highly vulnerable to AI psychosis.

Those at the tippy top have two main goals: to make their corporation more valuable and to increase its efficiency. AI proposes to do both of those things, and, as I have written before, it is surprisingly good at doing its job, which mostly consists of boilerplate meetings and emails. So, these higher-ups don’t see a problem with AI, despite the fact that it doesn’t increase productivity and actually harms workers (read more here). This means these leaders are likely to become heavy AI users and be routinely exposed to these deeply sycophantic AIs. With the total lack of upward accountability, or upward feedback that grounds these people in the real world, this heavy exposure will make them highly susceptible to full-blown AI psychosis.

Such psychosis could explain why CEOs of normal companies have doubled down on AI, despite the fact that workers and consumers overwhelmingly don’t want it and the colossal amount of evidence that such systems don’t work.

But it would also explain why AI and Big Tech leaders have pushed the AI bubble to utterly ludicrous levels. These leaders have drunk the proverbial Kool-Aid, and their potential AI psychosis has prompted them to functionally gamble our entire economy on this technology becoming the biggest industrial revolution ever in the next few years (read more here). That is not a decision based on facts or reality. It is one based purely on unfounded, rampant speculation that can only arise from blatantly delusional thinking. The question isn’t whether this is delusional, psychosis-like thinking but whether these delusions stemmed from their use of AI.

There are a few examples of this.

For example, Futurism reported that colleagues of Geoff Lewis, managing partner of the multi-billion-dollar investment firm Bedrock, were deeply concerned that he was suffering from AI psychosis after spending so much time with ChatGPT that he started to claim that he was mapping an incomprehensible “non-governmental system” that was designed to disrupt his life.

Apparently, Uber’s CEO also suffered from a heavy dose of AI psychosis after seriously claiming he was discovering new levels of quantum physics by talking to chatbots. That is an incredible level of grandeur and delusion.

I would argue you could look at the bats**t crazy predictions from the CEOs of OpenAI, Anthropic and Microsoft (along with Musk’s recent predictions) and see them as possible symptoms of AI psychosis. These companies, particularly AI startups, have even less vertical accountability, and leaders are inherently exposed to AI, given that it has become central to these businesses. That is a perfect environment for AI psychosis to grip leadership.

And it isn’t just me thinking it — even some tech CEOs are beginning to notice the pattern. A few weeks ago, Aaron Levie, CEO of the cloud computing company Box, stated that many of his colleagues and peers are suffering from AI psychosis. He pointed out that their separation from actual work and reality, along with how attractive AI is to them, leaves them incredibly susceptible to AI psychosis.

So, do we have a pandemic of AI psychosis? Are some of the most influential and powerful people on the planet driving themselves mad by strapping a digital delusional pseudo-productivity bot straight to their neocortex?

Because I am not a psychiatrist, and this is still a new condition we are figuring out, I cannot say.

What I can say is that the current science and understanding of this condition heavily suggest that the modern corporate structure is a perfect environment for the wealthiest and most powerful people on the planet to be highly susceptible to horrifically dangerous levels of AI psychosis. I can also say that this is one of AI’s dangers that we should take deadly seriously. There are no real checks and balances in place to screen for or catch this condition before it causes damage to these individuals, the organisations they run, or the societies that depend on those organisations.

In other words, we know AI psychosis is bad. There are now multiple documented heinous crimes linked to the condition. While these individual cases are tragic and graphic, the problem of AI psychosis may be far larger and more detrimental to society than we previously thought.

Thanks for reading! Everything expressed in this article is my opinion, and should not be taken as financial advice or accusations. Don’t forget to check out my YouTube channel for more from me, or Subscribe. Oh, and don’t forget to hit the share button below to get the word out!

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By now, we all know that AI is just one giant circle jerk of a bubble. Big Tech companies like Nvidia, Microsoft, Google, OpenAI and Oracle have been passing the same few billion dollars amongst themselves for a while, creating a broken financial perpetual motion machine that does nothing but artificially boost their value. However, there was one glaring outlier to this arrangement: Elon Musk’s xAI, which kind of had its own thing going on. But thanks to Musk merging xAI with SpaceX and taking it public, xAI/SpaceX is now the centrepiece of this circular bubble, and no one is trying to capitalise on it more than Google. In fact, there is an argument that Google is trying to pump Musk so hard that it could cause the entire bubble to collapse. Let me explain.

Did you know Google is a shareholder in SpaceX? Back in 2015, they invested $900 million into the company and still hold a 6.11% stake (pre-IPO). If SpaceX reaches its utterly ludicrous IPO valuation of $1.75 trillion, then this stake would be worth $122 billion. In other words, if this IPO goes well, Google stands to make well over $100 billion in profit from a less-than $1 billion investment. For some context, Google made just under $110 billion in profit last year. So this one investment could literally double their profits this year, if all goes well.

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But there is a problem — SpaceX is not exactly IPO-friendly.

Financially, the company is in a sticky situation, having lost $4.9 billion last year and losses are set to rise dramatically this year. The xAI side of the business is dead, with crushingly low infrastructure utilisation, functionally no market share, severe legal troubles, and horrifically large losses. The only profitable arm of the company was Starlink, which wasn’t actually all that profitable, and while it is growing, it shows signs it can’t sustain that growth in the long-term (read more here). All these problems should dramatically undermine SpaceX’s speculative value.

But SpaceX is asking to be valued at a level no company has ever seen. They made $18.7 billion in revenue last year, and are targeting a $1.75 trillion valuation, which equates to a revenue multiplier (how to value a business based on its annual revenue) of well over 90. For some context, a revenue multiplier of ten is considered extremely high. This has led some analysts to suggest that SpaceX is worth half its target IPO valuation, or just $780 billion. I, along with many others, believe even this figure is too high. Considering the immense amount of baggage SpaceX is carrying, a low-end revenue multiplier would be closer to six, and a high-end, extremely optimistic one would be 12. That would yield a more realistic valuation of $112 billion to $224 billion, or less than 13% of SpaceX’s targeted IPO value.

So, for Google to make their bank, they have to polish this turd, improve the perception of its internal workings, and push the hype to draw in as many suckers as possible.

Now, here is an interesting fact: Google also holds a rather large stake in xAI competitor Anthropic. Google owned 14% from a $3 billion investment but has since invested an additional $10 billion, specifically for compute power, which has likely raised its stake to the upper teens. Why does that matter?

Well, both Google and Anthropic have revealed that they would be interested in using Musk’s orbital AI data centres, making them so far the only companies to do so. The fact that anyone is interested is surprising enough, as the concept is utterly stupid. As I have covered before, orbital data centres will be exponentially more expensive than those on Earth, devastatingly unreliable, and almost impossible to deploy at scale. As it is, AI data centres are already a cash black hole, so no sane business would go anywhere near Musk’s moronic orbital ones. Therefore, this looks like Google and Anthropic using their clout to legitimise SpaceX’s orbital AI push, generate hype, and pump up that speculative value. It’s either that, or the business leaders at both companies are utterly braindead.

But Google and its partner, Anthropic, went one step further and have just signed deals to rent terrestrial AI computing power from SpaceX at extortionate prices.

Google has recently agreed to rent the equivalent of 110,000 Nvidia GPUs from SpaceX’s Colossus data centre for $920 million per month from October 2026 to June 2029. Back in May, Anthropic agreed to rent 222,000 Nvidia GPUs from SpaceX’s Colossus data centre for $1.25 billion a month for the next three years. Together, these agreements will generate $2.17 billion in revenue a month, or $26 billion a year, for SpaceX.

I cannot overemphasise the instability and overpricing of these deals.

For one, both agreements have a 90-day termination notice, meaning they can pull out with very little notice. In other words, this revenue is not guaranteed, and it allows both Anthropic and Google to rapidly jump ship once a cheaper option is available — whichthere will almost certainly be, because these agreements are shockingly overpriced!

SpaceX’s data centres use a combination of Nvidia H100, H200 and B200 chips. On the low end, a single H100 GPU rents for $3–4 per hour, and on the high end, a B200 GPU rents for $4–6 per hour. Let’s be generous and say that the going market rate for a GPU in SpaceX’s data centre is $5 per hour, and because there are 730.5 hours in a month, each chip’s going rate is $3,652.50 per month.

Google is paying $920 million per month for 110,000 of these chips. But 110,000 chips should cost $401.78 million per month, based on this $3,652.50-per-chip-per-month estimate. So, Google is paying double the market rate!

Anthropic is paying $1.25 billion a month for 220,000 of these chips. But 220,000 of these GPUs should cost $803,550 per month. So, Anthropic is paying 56% over the going rate.

No wonder they included these 90-day clauses in the deals. Neither Google nor Anthropic can stomach paying such excessive prices for compute. They will need to jump ship as soon as doing so benefits them.

So, why have Google and its underling paid so much?

Well, again, it legitimises SpaceX’s AI credentials, bringing the company further into the AI bubble and validating much of its speculative value. You could see this move as a flagrant PR stunt to pump speculation ahead of the IPO.

But this will also save SpaceX in the mid-term. Thanks to its AI and currently failing Starship project, SpaceX’s losses are mounting quickly. It posted a $4.27 billion loss for 2026 Q1, which is nearly as much as it lost in the entire year of 2025. Right now, it is losing about $1.4 billion a month, and if that trend continues, monthly losses could easily exceed $2 billion, if not $3 billion. This $2.17 billion a month from Anthropic and Google will almost certainly prevent SpaceX from posting devastating losses for its quarterly reports after its IPO, which could undermine and tank its gargantuan, lofty valuation.

Oh, and wouldn’t you know it, SpaceX has considerably watered down the insider lock-up rules (read more here), meaning Google can legally sell most of its SpaceX shares to the public market just a few quarters after the IPO. So, Google only needs to keep SpaceX looking financially buoyant or preserve its gargantuan speculative value for six to nine months, which will enable it to sell its shares at an eyewateringly high profit.

In other words, it looks like Google is using its sizeable coffers and its ‘desperate for cash’ underling Anthropic to prop up SpaceX via extortionately expensive and short-term compute deals to increase the value of its shares in SpaceX by legitimising its bad business, plugging its gaping financial holes, and pushing its laughably false speculative hype so it can offload its shares in SpaceX at a staggering profit.

That 90-day clause is the smoking gun. It enables them to make a profit and jump ship quickly.

Now, am I saying they will do this? No. Businesses like these do weird things. What I am saying is that this is what it looks like they are trying to do.

It’s also not like we don’t have precedent for such financial tomfoolery. Google’s profits recently jumped by $28 billion or 81% year on year! The media claimed this was due to their cloud computing doing rather well, but that isn’t entirely true. The vast majority of this $28 billion came from an increase in the value of their stake in Anthropic. What drove that increase? Well, they invested $10 billion into Anthropic for a tiny share, which dramatically raised the value of their previous stake and generated almost all of this $28 billion ‘profit’. Not to mention that this fake profit situation gets so much worse. Go watch Sasha Yashin’s amazing video to find out more. But my point here is that Google has no qualms about using questionable financial engineering to its benefit. So we have good reasons to be sceptical about their decisions with SpaceX.

So, how could all of this pop the bubble? If SpaceX’s IPO doesn’t fully play out — i.e.,if its value drops before these insiders, like Google, can make their billions — then this gravy train stops. This circular financing scheme only works if the money perpetually spins around and around, and more and more investors are willing to add fresh money into the perpetual motion machine. Google’s doubling down on SpaceX’s IPO creates a weakness in that system because if this plan doesn’t pay off, it will break this cycle, spook investors, and bring the perpetual motion machine to a screeching halt. The question is, was this enormous risk worth it? In time, we will see.

Thanks for reading! Everything expressed in this article is my opinion, and should not be taken as financial advice or accusations. Don’t forget to check out my YouTube channel for more from me, or Subscribe. Oh, and don’t forget to hit the share button below to get the word out!

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Does anyone actually think the Tesla robotaxi rollout is going well? I’m not sure even Musk fanatics are all that convinced. The news broke a few days ago that Tesla’s Texas robotaxi fleet is just 42 strong, a far cry from the 1,000-a-month after-launch total Musk promised. I’m used to seeing Tesla fans drink the copium and run to Musk’s defence, but it seems their fight has run out. It’s almost like they have finally realised you can trust a damn thing this compulsive liar says! Well, even Tesla insiders are loudly calling BS on Musk’s self-driving taxi.

You see, Reuters interviewed nine former Tesla data labellers and a former self-driving engineer about Tesla’s Full Self-Driving (FSD), the system behind Tesla’s robotaxis and ‘supervised’ self-driving consumer cars. And, let’s just say their reviews weren’t exactly glowing.

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Seven of the data specialists outright said they wouldn’t ride in a Tesla being controlled by FSD. They said they had “all seen it fail,” with one even saying that they wouldn’t ride in a Tesla robotaxi “if you f**king paid me.” Which isn’t surprising, considering five of them reported constantly seeing the robotaxis exceed speed limits. The self-driving engineer said that Musk’s proclamations that FSD is ready for “safe unsupervised” rides are flat-out lies, saying “Definitely don’t trust Elon on this.”

When the people whose job it is to get these systems working tell you that it is this crap, you run for the hills.

But, quite frankly, we shouldn’t be shocked.

FSD Tracker crowdsources data from customers who bought FSD for their Tesla, and its results are fascinating. As of writing, they have logged more than 344,000 miles with the V14 version of FSD, the same version used in Tesla’s Robotaxis. They found that in the city, you know, where robotaxis operate, FSD V14 has an average distance to disengagement (when the system has to be disengaged to stop a minor incident) of just 36 miles, and an average distance to critical disengagement (when the system has to be disengaged to stop a major incident) of just 869 miles. A typical taxi drives roughly 200 miles a day. So, these numbers suggest that a truly unsupervised Tesla robotaxi would get into five minor scrapes a day, and one proper accident every week.

FSD tracker currently has the overall critical disengagement rate (which includes highway, side roads, and city driving) at an average of once every 1,996 miles. Which sounds a lot better. But, don’t forget, the average American crashes roughly once every 375,000 miles. So as it stands, FSD tracker suggests the same system Musk claims is ready for unsupervised rides, crashes at a rate 187 times more than human drivers.

But are you ready for the icing on this cake?

FSD tracker is inherently biased towards Tesla. These customers have spent a fortune to get the latest Tesla with the latest version of FSD. As such, they are far less likely to self-report when it goes wrong or underreport the severity of the failures. Moreover, they will likely use FSD only when they feel it is safe to do so, which serves as an inbuilt cherry-picking filter.

So, FSD will likely perform substantially worse than this when used to operate a robotaxi with zero human oversight.

So, why is FSD such an awful system? It’s been in development for a decade by this point; you’d think it would be at least passably good.

Well, thanks to Musk’s vicious combination of idiocracy, cavernously crippling ego, and ignorance, he set the entire system up for failure.

Most self-driving cars use a plethora of different sensors, 3D reference maps, and multiple redundant systems. This might make the vehicle more expensive and limit its operating area to places that have been mapped correctly, but it greatly improves performance. The different sensor types and detailed 3D reference maps provide redundancy, meaning that if the AI gets it wrong, as it almost certainly will, there are redundant systems in place which can check and mitigate these errors.

Cars set up like this, such as Waymo’s, don’t require 100% accurate AIs to drive relatively safely. Which is a damn good idea, because no AI can be 100% accurate, as they are statistical, not cognitive machines.

Ever the contrarian, Musk pushed for a far more ‘naked’ approach. Despite his own engineers’ pleas, he forced FSD to use only cameras (read more here). In fact, the only way FSD ‘understands’ the world around it is with its nine low-quality cameras, and a basic GPS reference map similar to Google or Apple Maps, that is it. To drive even remotely safely, the computer vision AI has to interpret the world around it with near-100 % accuracy, as there are no redundant systems to check for errors or anomalies, and even a minor error or anomaly can cause an incident. Likewise, to avoid constantly crashing, the AI driving the car needs to make decisions with near-100 % accuracy using this very sparse data.

This is why FSD has such a bad disengagement rate. The entire system is set up on the premise that the AI will eventually become functionally 100% accurate once enough data and computing power are shoved into it.

Not only is this total lack of redundancy against all good engineering practices, but a 100% accurate AI physically can’t happen, and anyone with even a passing knowledge of how AI works knows that.

For example, there is the Floridi Conjecture, which I have talked about before. Yale Professor Luciano Floridi put this forward in a paper, detailing how an AI system can either have a narrow scope and high certainty (more accuracy) or a wide scope and low certainty (less accuracy). Crucially, Floridi’s Conjecture states that an AI absolutely can’t have both a great scope and great certainty. FSD has an incredibly wide scope, even wider than other self-driving cars, as it has to cope with chaos using less data, so it can never been 100% accurate, meaning it can never be safe enough for unsupervised operations.

But as Waymo recently found out, when they had to pause their operations across much of the US due to their vehicles driving horrifically dangerously, just because you try to narrow down the scope with redundant systems and greater data fidelity doesn’t mean you have solved the problem. People seem to think I prefer Waymo to Tesla. I don’t. Self-driving cars, as a concept, are utterly moronic to the core. AI is not intelligent; it doesn’t actually ‘understand’ what it is doing, it can’t cope with chaos, and it will statistically get it wrong frequently. We shouldn’t use AI in applications like self-driving, which have such high risk, and require accuracy that it simply can’t deliver. Waymo’s solution is just less worse than Tesla’s as it actually reflects some level of engineering sense.

So, I guess the question is, how long before we actually heed the warnings of the people actually making these inherently flawed and utterly moronic contraptions? There is enough data and testimonials out there to prove that these stupid machines should be nowhere near a public road. So, can lawmakers stop pandering to authoritarian oligarchic Big Tech goons and actually serve and protect the very people they are meant to represent? You know how a democracy is supposed to work.

Thanks for reading! Everything expressed in this article is my opinion, and should not be taken as financial advice or accusations. Don’t forget to check out my YouTubechannel for more from me, or Subscribe. Oh, and don’t forget to hit the share button below to get the word out!

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The SpaceX IPO is looking less and less like a galaxy-brained business decision and more like a carefully engineered way to give the billionaire SpaceX insiders an exit strategy and to make the scorching-hot potato that is SpaceX stock everyone else’s problem. The notion that this looks like a pump-and-dump scheme to scam the world of their retirement funds is now so widely recognised that it has become a meme. But how does this painfully obvious IPO grift actually work? Well, let’s go through it step by step.

I am not the first to talk about this topic. A More Perfect Union and Patrick Boyle have some great videos diving into the SpaceX IPO, and I highly recommend giving both a watch.

The Pump

All pump-and-dump schemes have two phases, hence the name. First, the price is artificially boosted, or pumped, way beyond its actual value. Then the insiders sell, or dump, at the peak, pulling the rug out from under everyone else, who are left holding the bag when the price comes crashing down. Thanks to the torrent of Crypto and NFT grifts over the past few years, most people are now fairly familiar with how this scheme plays out, how wealthy it can make the perpetrators, and how catastrophic the losses can be for the victims.

So, where is the alleged pump in the SpaceX IPO?

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Well, how do you artificially raise the price of anything? Simple. Restrict the supply and coerce demand, and Musk is openly doing both.

Let’s start with supply. Most companies float between 10% and 30% of their shares in an IPO. Larger, more mature companies like SpaceX almost always aim for the higher end of that range. When you consider the colossal amount of capital SpaceX will require in the coming years just to remain functional (with more on that in a minute), it would make sense for SpaceX to offer more than 30%. But no, SpaceX is offering less than 5% of its stock.

This insane construction will create artificial scarcity, particularly when you take into account the company and the IPO’s publicity, and seriously jack up the price. This is the Beanie Baby approach to pricing.

But what about demand? Well, Musk has a way of jacking that up too.

In a normal IPO, 90% of the shares are allocated to institutional investors, such as banks and insurance funds, and 10% are allocated to retail investors, reflecting market demand. SpaceX has instead opted for a 70/30 split, allocating three times the typical amount of shares to retail investors! This strongly suggests that professional investors have seen the artificial scarcity tactics and don’t want to buy at that inflated price. So, despite offering an extremely constrained number of shares, SpaceX has to sell its wares to individual investors who are less likely to spot the ruse.

Musk has been hyping up SpaceX for years to his cult of brain-dead investors. Therefore, this split ratio also suggests they expect demand from those who swallow Musk’s constant lies and manipulations to be substantial

These factors alone are enough to jack the price up to insane levels. SpaceX targeted an IPO price of $1.75 trillion, then raised it to $2 trillion, and later lowered it to $1.8 trillion.

But I don’t think people realise the true extent of that estimate.IPOs are typically valued using a revenue multiple, where the overall value of the business is a multiple of its annual revenue, with the multiple being higher if the business is considered to have more growth potential. Most IPOs have a revenue multiple of around six, and famously, Facebook’s IPO was astonishingly high at around 11. Now, SpaceX generated $18.7 billion in revenue in 2025(including X and xAI, which recently merged). That means that it is targeting a revenue multiple of 96!

Even if you believe all of SpaceX’s BS (again, with more on that in a second), that is a totally fanciful and unjustifiable valuation.

Fortunately, Musk has found a way to push it even further and pull practically all of us into his grift.

SpaceX will be one of the biggest IPOs in history. As such, Musk could make some quite shocking demands given that exchanges would fight over who gets to list them. Ultimately, the NASDAQ won that fight by changing one of its rules at SpaceX’s behest, calling it the ‘fast entry rule’. Normally, a company has to be traded for a year before index funds, like the S&P 500, are allowed to include it. This is known as ‘seasoning’, and it is supposed to protect index funds from the crippling instability of IPOs and to give the market time to find a true value. This fast entry rule completely scraps that concept and shrinks the seasoning period to just 15 trading days!

The majority of indices, like the S&P 500, track the most valuable companies. Because of the restricted supply artificially boosting value, SpaceX will likely become one of the most valuable companies on the planet following its IPO. That means most of your investment portfolios, retirement funds, and even some savings accounts will be forced to buy a huge portion of SpaceX’s highly restricted stock after little more than a fortnight of public trading.

But here’s the thing: there is more passive money, such as these index funds, in the stock market than active money, such as traders. This means that after 15 days of public trading, a veritable tsunami of demand will strike, jacking the price up even higher!

This demonstrates all the hallmarks of a pump, particularly when it seems to totally deny the reality of SpaceX.

The Reality

SpaceX has some serious problems and baggage that should drastically weigh down its valuation.

SpaceX owns X/Twitter and xAI and after a series of bailouts — I mean, all stock mergers,neither company is doing great. I’m sure I don’t need to remind you of X/Twitter’s severe legal troubles after the horrific Grok child and unconsentual sexual abuse image debacle, but on top of that, it makes a fraction of the revenue it once did.

xAI, whose chatbot only produced the heinous material on X/Twitter, is also embroiled in said legal trouble. But it too is deeply unprofitable, losing around a billion dollars a month! Not to mention that this cash burn isn’t actually growing xAI either. Their data centres have such catastrophically low utilisation rates, thanks to no one actually using Grok, that they have rented compute to their competitor Anthropic to help pay the bills (read more here)! Moreover, the main founders have left, and Musk has admitted that xAI needs to be rebuilt from the ground up, causing many to ask what the hell SpaceX actually bought when they merged, other than all of xAI’s gargantuan debt.

Then there is SpaceX, which is complicated. Despite what you might think, SpaceX doesn’t actually make that much money from rockets, because the space launch market hasn’t really grown in decades, as demand outside of Starlink has remained quite stagnant. That is why over 70% of SpaceX’s launches last year were for its Starlink satellites. In fact, thanks to how much Starship is costing them, the launch business is losing money!

As I covered in a previous article, Starlink appears to be profitable, reportedly making just over a billion dollars last quarter. But thanks to the IPO filing, we now know that it isn’t a truly scalable business. Their service is too patchy, and their costs are too high to dominate the market, despite what Musk claims. So yes, well done — Starlink works as a business, but it doesn’t warrant excessive speculative valuation at all.

Then there is Starship, which is heading down a path to nowhere. The IPO filings and recent launch have proven that SpaceX is really struggling to increase its payload capacity to even useful levels, let alone the promised 100 tonnes to Low Earth Orbit, and that it is not getting any closer to full reusability, let alone the rapid zero-maintenance reusability Starship requires to hit its launch price goals. There is also zero evidence that SpaceX can solve the boil-off problem, which is critical for Starship to reach higher orbits, the Moon, or Mars (read more here). Starship should be seen as a project that costs SpaceX billions of dollars a year without delivering any real value. Let’s not forget this rocket has yet to even reach orbit!

Some will say that SpaceX’s plans to deploy a million orbital data centres could justify its price, but that is so far off the mark it’s painful. For one, Starship has to succeed for that to be even close to viable, and it has provided no proof that it can ever work. But as I have covered before, orbital data centres will be too catastrophically expensive, cumbersome, risky and inefficient to ever be viable. This is purely fanciful thinking.

When faced with the hard facts, SpaceX doesn’t look like a growth company. It looks like a company wasting billions of dollars a year to mak itself look like a growth company.

This brings me to the gap problem. As my fellow Substack writer Capefearadvisors points out, SpaceX faces around $235 billion in spending commitments through to 2030. Considering the overall business is losing money, you’d think SpaceX would want to raise this amount of money through the IPO, right? After all, that is the point of an IPO: to raise money to pay for growth. But no, even with its insane valuation, this IPO will ‘only’ raise $50 billion to $75 billion due to the fact that so few shares are being sold. On top of that, $20 billion of it must be used to pay down the $20 billion in debt SpaceX has on its books. As such, SpaceX actually needs roughly five times the amount of capital this IPO will raise.

So why bother going public? It won’t materially help the business. It makes no sense, unless growing the business isn’t the point of this IPO. This context should make investors cautious and seriously depress the valuation.

With all of this being considered, a more realistic yet extremely generous revenue multiplier for SpaceX would be around six. That would put SpaceX’s valuation at just $114 billion, or roughly 94% below its IPO target!

This is what I mean when I say the reality of SpaceX is at total odds with this IPO.

But, somehow, it gets worse.

The Dump

Okay, so that’s the pump; now for the (alleged) dump.

There are standards in place to prevent insiders from using an IPO to dump their stock. Normally, insiders are not allowed to sell their shares for the first 180 days of trading.

But, guess what? That isn’t the case for SpaceX. Insiders are allowed to sell 20% of their shares for just three months. If the shares are selling for 30% above the IPO price at that time, that limit is raised to 30%. After that, there is a rolling schedule where they can sell the rest of their shares in stages up to 135 days post-IPO. Musk is exempt from this policy and can’t sell his shares for a year.

By this three-month mark, the index fund tsunami has a decent chance of pushing the value of SpaceX up 30% from its IPO (up to $2.34 billion). That would mean insiders could offload 30% of their shares in just a few months’ time. These insiders, who include the likes of Google and some of the most deranged and greedy venture capital firms on the planet, own approximately 53% of SpaceX (Musk owns 42%, the public will own 5%). So, they could legally sell roughly $372 billion worth of SpaceX shares just 90 days out from the IPO. To give you an idea of how maniacal that is, SpaceX was valued at just $350 billion in late 2024. This sell-off would repay these insiders many times over the amount they invested into SpaceX (or X or xAI), but it would also utterly tank SpaceX’s valuation, destroying the investment portfolios and retirement funds of millions.

And, don’t think that Musk won’t get involved just because his loans are locked up. He is infamous for using his stock as collateral for loans to the tune of billions of dollars, and you can do that pretty much immediately after the IPO. This means that Musk could pull a an astronomical amount of cash from this IPO without ever violating his lock-in period.

Admittedly, we don’t know that these are their immediate plans. The only way we could get an inkling of that is if the IPO included provisions to give them cover to make such a brash exit.

Oh wait… It does that too!

This IPO makes it very clear that upon buying SpaceX shares, you waive all right to sue the company or its leadership. In other words, investors, retirement funds, and even banks can’t raise a class action lawsuit to hold these insiders accountable if they pull the rug out from everyone else. For the life of me, I can’t think of another reason why his IPO would include this stipulation (given that such a provision will reduce the value of the stock) apart from clearing the way for these insiders to jump the sinking ship.

Summary

If not a pump-and-dump, why is it pump-and-dump-shaped?

Most of the SpaceX insiders only own their shares because they were stupid enough to invest in Musk’s catastrophic Twitter purchase or his cash-burning xAI. But even the actual shareholders who invested in SpaceX must be noticing how badly Starship is failing and how comparatively little money Starlink is making and feel worried about whether their cash will disappear into thin air. Sure, these are all heinously greedy billionaires with venture capital firms; they could easily stand to take such a loss, and quite frankly, it would be good for them to learn a lesson about not backing dumb ideas.

But no, it seems they refuse to take the hit. They are instead trying to make this our problem by grifting the stock market and clearing the way for the biggest rug-pull dump the world has ever seen. At least, that is what all of this looks like.

I can’t help but feel like these oligarchic morons have stolen control of the ship, immediately crashed it into an iceberg, and are now frantically making their way to the lifeboats while locking the rest of us below deck as the ship begins to slip under the waves.

Thanks for reading! Everything expressed in this article is my opinion, and should not be taken as financial advice or accusations. Don’t forget to check out my YouTubechannel for more from me, or Subscribe. Oh, and don’t forget to hit the share button below to get the word out!

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SpaceX is going public in a matter of weeks, which means they have filed an S1, a document that lets us finally see some of the company’s murky financial machinations. There are a tonne of revelations to be found in here — that is, once you get past the painful levels of hollow hype that come across as desperation more than vision. One revelation is about Starlink, as this S1 paints it as the crown jewel of SpaceX, not just because it is the only profitable division of SpaceX, but because it seems to suggest Starlink will dominate the internet provider market. I have even talked to people who want to invest in SpaceX’s ludicrously overvalued IPO just to get a slice of the Starlink pie. But sadly, when you look at the numbers and their actual meaning, this narrative totally falls apart.

Let’s start with the 2026 Q1 figures for Starlink as laid out by this S1 filing. Starlink had a total of 10.3 million paying users, just over double the number they had this time last year, with each paying an average of $66 per month (a sharp fall from $99 a month in 2023), generating $3.26 billion in revenue and $1.19 billion in operational profit.

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The fact that Starlink is profitable at all is utterly remarkable. For years, Starlink was wildly unprofitable, and thanks to the fact that its satellites only last a few years, it was set to never reach profitability. There are many experts and non-experts, like myself, who believed Starlink would never reach this point. You have to hand it to the engineers and sales team for pulling off this utter miracle (even though I have my suspicions about this figure). But we will get to that topic in a minute.

The Market

What was almost side-splittingly funny was seeing the projected addressable market, or how much of the internet provider market they believe they can secure for Starlink, which this IPO set at $870 billion. That equates to 67 times their current revenue! Now, the global internet service provider market size was estimated at just $598.70 billion in 2024 and is predicted to rise to $1125.10 billion by 2035. In other words, Musk predicts Starlink will dominate this market with roughly 80% market share!

This is the problem with Starlink, because that is simply not possible.

Let’s start with the fact that in order to take a majority share of the market, Starlink will need to offer better-than-average internet speeds and below-average cost.

The numbers in the S1 suggest that currently, Starlink’s operational cost per user is $50 per month ($2.07 billion in operational costs for 10.3 million users over a quarter). This broadly means that Starlink currently can’t offer its services for less than that per user without operating at a loss. This is a significant problem if Musk wants global market domination. You see, internet in the US is extremely expensive, averaging $68 per month, which makes Starlink a middle-ground option, whereas internet in Europe is significantly cheaper, at just $56.59 per month, making Starlink comparatively expensive. What’s more, the average internet speed in Europe and the US is noticeably faster than Starlink’s maximum speeds, and the average connection is more stable (with more on that in a minute).

In other words, Starlink’s current cost of operation per user prices it out of being a market dominator, because if it dropped its prices to become broadly competitive across the market, it would operate at a loss.

Here is where people will mention that the cost per user will go down, just as it seems to have done over the past few years, which is why Starlink is now profitable when it wasn’t before.

But hold your horses! There are two very significant reasons why that might not be the case: depreciation and the city problem.

Depreciation

Starlink satellites are designed to only orbit Earth for five years before naturally deorbiting and burning up in the atmosphere. SpaceX had to agree to this condition to get permission to launch so many satellites, as this restriction inherently keeps space junk to a minimum. However, it does mean that the entire Starlink internet infrastructure needs to be completely replaced every five years! As a result, the depreciation costs Starlink incurs are enormous!

This is why I am not so sure about that $1.19 billion profit. You see, companies can write off 100% of the first year’s depreciation costs thanks to the Big Beautiful Bill Musk helped get enacted in the US. So, depreciation on a good number of their satellites may not currently be recorded, making their profit margin appear higher than reality.

What’s more, the replacement costs will likely exceed depreciation. The new generation of Starlink satellites (V2 Mini) is about four times as expensive as the previous generation, but it doesn’t appear to support four times as many simultaneous users. So, when they replace all the V1 and V1.5 satellites with new V2 satellites, it could increase the cost of serving each user. Why does this matter? Well, a large portion of the Starlink constellation consists of V1 and V1.5 satellites launched three to four years ago. They are nearing the end of their lives and will be replaced by V2 Mini satellites, which, again, are likely more expensive per user.

In other words, the depreciation cost Starlink will have to absorb per user seems set to increase very soon, once they start replacing older satellites with newer, faster and more expensive ones. Let’s also not forget that, thanks to the Rampocalypse and rising fuel prices, the cost of building and launching these satellites is almost certainly increasing, too, which exacerbates the problem.

But, on top of that, I also suspect that Starlink is, on paper, extending the lifespan of these satellites to artificially lessen their depreciation cost. While this five-year lifespan has been tossed around a lot, we know Starlink has performed premature mass deorbits of satellites and that there is a notable rate of newer hardware failing and deorbiting early, too. As such, the actual average lifespan of a Starlink satellite is likely less than five years. If that is the case, and current or early hardware is depreciating over five years on the books, it may artificially boost the recorded profit margin far above where it actually is by egregiously underreporting the depreciation costs.

When you have a company that has literally billions of dollars worth of annual depreciation on its books for novel hardware, there is ample room for this type of accounting shenanigans. And we all know Musk loves that kind of stuff.

As such, that $1.19 billion profit margin and $50 per customer per month operational costs might be the result of incredibly rose-tinted glasses.

The City Problem

But arguably, the largest problem is cities.

Starlink works great in rural, less densely populated areas, where only a small number of users will connect to a single satellite at a time. But in densely populated areas, tens of thousands of users may be trying to connect to a single satellite, which exceeds the satellite’s capability, causing outages. Indeed, this is why Starlink’s service in cities is consistently very patchy and noticeably slower than advertised.

Why is this a problem? Well, globally, 55% of people live in densely populated cities. Also, most people who can afford Starlink don’t live in rural towns, which tend to be poor, but in developed urban cities. Logically, if Starlink wants to take a majority market share in the internet provider market, as they say they do, they have to solve this bottleneck problem.

This is where Starlink relying on satellites bites it in the ass. Other forms of internet infrastructure, like fibre-optic, can simply deploy more capacity locally, keeping their network utilisation rate high and their operational costs down.

But Starlink’s satellites are orbiting the Earth. You can’t make them exclusively hover over a city. So, to get rid of the city bottleneck, SpaceX has to deploy an oversupply of satellite capacity to ensure that there are always enough satellites above a city to exceed demand. They can’t just increase capacity locally; they have to do so across basically the entire globe! This means the network utilisation rate will decrease, and the number of satellites needed to service each user will increase.

In other words, to solve the bottleneck problem and make Starlink a competitive option for the vast majority of internet users, the per-user operational cost will have to spike dramatically. As we have seen, if that were to happen, Starlink would very rapidly lose its profitability.

I hear those who claim that Starship will bring down the cost to launch Starlink and make larger satellites with far greater capacity possible. But as it stands, Starship is not a viable launch vehicle and is still far from actually launching satellites, let alone doing it regularly and cheaper than the Falcon 9. Based on what SpaceX has actually shown us, not what Elon claims is coming ‘round the mountain, this problem isn’t going to be solved any time soon.

Summary

This is why Starlink’s rapidly growing user base and profitability are so damn impressive. It has demonstrated that it can be a viable, sustainable business in the niche sector of decent internet access in low-population-density areas. That is great and means that, as a standalone business, Starlink is likely worth a few tens of billions of dollars. To what degree this profit is due to accounting shenanigans and timing, I do not know. But simply registering a profit at all is huge! Again, huge kudos to the amazing workers under Musk’s thumb.

Unfortunately, Starlink’s figures and reality do not paint a picture of a company that can get anywhere close to an $870 billion market share. It shows that its recent growth is remarkable, but it is likely approaching the upper limit of its addressable market. I have no doubt that its user base will grow; after all, for many, it is a compelling product. But the fact of the matter is, it is too expensive, slow, and unreliable to be the source for how the majority of humanity connects to the internet, and something drastic will need to happen for that to change. Pretending otherwise, or attempting to claim this is what makes SpaceX worth nearly $2 trillion, is dangerously fanciful thinking.

Thanks for reading! Everything expressed in this article is my opinion, and should not be taken as financial advice or accusations. Don’t forget to check out my YouTubechannel for more from me, or Subscribe. Oh, and don’t forget to hit the share button below to get the word out!

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The media buzz around Starship has officially died. I was expecting this “highly anticipated” launch to be painted across the headlines, but it seems people simply don’t care that another one of Musk’s giant phalluses exploded in the Indian Ocean. By all accounts, the polish on this turd has tarnished as people have grown tired of waiting for this “revolutionary” tech to actually materialise instead of being an expensive trash fire. This is one of Musk’s seriously bad patterns. He has overpromised himself into multiple corners, from self-driving cars to Mars-bound rockets and even pathetic C-3PO wannabes, and now people want him to put up or shut up. The trouble is, as we have seen with DOGE and the Cybertruck, Musk can’t deliver, but he likes to ‘look like’ he has — and this launch was yet another example. If you buy SpaceX’s line, they have made a serious step forward. But if you actually look at the launch with even a slightly critical lens, that narrative totally falls apart.

The Launch

This was the first launch of the Starship V3. For this article, all you need to know is that this is a major redesign from V2. It’s bigger, carries more fuel, has upgraded Raptor 3 engines (which burn fuel faster and therefore produce more thrust), and the booster has more engines. Primarily, V3’s goal is to dramatically increase the payload of Starship. V2 was pathetic, with Musk claiming it could take just 35 tonnes to LEO, or more than a 65% shortfall in promised payload, but in reality, it only ever took 16 tonnes on a suborbital flight. V3 is aiming to reach the promised 100 tonnes to LEO (Low Earth Orbit) mark, which is critical to meeting all of Starship’s planned use cases. In other words, if they can’t achieve a payload of 100 tonnes to LEO, the project is a failure.

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So, how did the launch go? Well, despite some glowing reports, not great.

The rocket launched, and the stages separated fine. But the Booster lost an engine during launch, and then the engines failed to reignite after separation, causing it to crash into the Gulf of Mexico at 900 mph! Not exactly conducive to being reusable. The upper stage carried on its suborbital trajectory without a hitch. It reached a maximum altitude of 194 km (121 miles) at a speed of just over 26,300 km/h (16,300 mph), where it deployed its payload of 44 tonnes worth of dummy satellites. It then conducted a controlled splashdown in the Indian Ocean, with the heat shield looking extremely charred but intact.

That sounds like a relative success, right? Sure, the Booster failed spectacularly, but SpaceX has seemingly more than doubled the payload! That is a serious step forward towards their goal, right?

Well, sorry to burst your bubble, but no.

The Sleight Of Hand

It is very important to mention that this isn’t 44 tonnes to orbit. This is 44 tonnes on a suborbital flight that reached a maximum altitude of just 194 km (121 miles) and only 26,300 km/h (16,300 mph). For some context, a Starlink satellite orbits at an altitude of 550 km (340 miles), which is nearly three times as high as this Starship reached. These Starlink satellites also travel at 27,320 km/h (16,976 mph), or a thousand kilometres an hour faster than this Starship!

I don’t think people realise what a colossal difference this small detail makes. The media is seemingly parroting SpaceX’s narrative that this means Starship now has a usable payload of 44 tonnes. But it doesn’t! Instead, the only thing this does is heavily imply that Starship still has a major payload deficit. Not just a small underperformance, but an underlying critical flaw which makes it totally unusable. But, by running with the 44-tonne figure and not the 194 km figure, that isn’t what people notice.

So, let me explain what all of this implies.

The Payload Problem №1: Set Up

Based on what we know, let’s figure out roughly how much payload V3 could actually take to orbit.

In order to do this, I will have to make some assumptions.

My first assumption is that this Starship was pushing its payload limits, which is supported by the failed engine, as with Flights 7 to 11, which experienced engine failure with a high payload and had no failures with a lower payload. It also makes sense for SpaceX to try to reach the highest payload figure possible, as it will help their upcoming IPO.

I also have to make some assumptions about its mass. We know the payload was 44 tonnes, but we don’t know the dry mass of the upper stage, or the mass of the reserve propellant it was carrying when it reached its maximum altitude. I will be exceedingly generous and assume a 150-tonne dry mass, which is on the lower end of estimates, and a reserve fuel mass of 30 tonnes, which is on the higher end of other estimates. These dry mass and propellant capacity estimates are realistic because they are more than enough to enable landing, given that this propellant, plus aerodynamic drag, is theoretically enough to reduce speed to zero during landing.

This gives our Starship a total mass of 224 tonnes (150 tonne dry mass, 30 tonne reserve propellant and 44 tonnes of payload) as it reached its maximum altitude of 194 km at a velocity of 26,300 km/h.

Okay, so what would this Starship have to do to travel from here to orbit?

The Payload Problem №2: Into VLEO

Technically speaking, 194 km is just about high enough for a stable orbit. At that altitude, you are slightly above the edge of the atmosphere, so there is functionally no aerodynamic drag, and if you travel fast enough, you could technically enter orbit. However, events like small geomagnetic storms can easily create atmospheric drag at this altitude, rendering the orbit unstable. This is why orbits below 400 km are called VLEO (Very Low Earth Orbit) and are rarely used, as they are too risky for typical satellite operations.

So, even though 194 km is far too low for Starlink satellites, could this Starship have entered orbit at this altitude?

Orbital velocity at an altitude of 194 km is 28,051 km/h. So, at its maximum altitude, this Starship would need to gain an additional 1,700 km/h (1,056 mph) in velocity to enter orbit.

However, let’s say that at this max altitude, this Starship turned on its engines until it gained this extra velocity. How much fuel would it burn through? The answer is about 21 tonnes, or two-thirds of the reserve fuel it requires to land (using the rocket equation and a vacuum exhaust velocity of 3,700 m/s). The nine tonnes of reserve fuel left is not enough to complete the landing. It can only scrub off 604 km/h (375 mph) of velocity during landing, and, as the Booster from this launch demonstrated, far more speed needs to be scrubbed off for a safe landing.

In other words, this Starship could transport 44 tonnes into the lowest possible orbit, but it wouldn’t have enough fuel to land the upper stage. Also, don’t forget, I was generous with Starship’s dry mass and its reserve propellant here; there is always a chance these figures are worse.

This is one of the lowest energy orbits possible. But higher orbits will require much higher total orbit energy (the sum of an orbital object’s kinetic and gravitational potential energy). This energy comes from a rocket’s propellant and engines. Therefore, Starship can’t reach much higher orbits with this payload without also expending the upper stage.

The Payload Problem №3: Into LEO

But VLEO is utterly useless for most satellites. How much more fuel would be needed to expand this orbit out to 550 km so that Starship can actually deliver a Starlink satellite? You know, the simplest and easiest to achieve of all Starships’ designed use cases.

Using a Hohmann Transfer Calculator, we can calculate that it would require another 13.5 tonnes of propellant to transfer into this higher orbit (based on a specific impulse of 355 m/s, a commonly cited value spec for the Raptor 3 vacuum engine).

But wow! Hang on, our Starship only has nine tonnes left! So, four tonnes of our payload have to be sacrificed to gain four extra tonnes of propellant.

In other words, this Starship could deliver 40 tonnes of payload to a usable LEO in a ‘partially reusable’ configuration, where only the Booster was recovered. But the Booster wasn’t recovered! So, as far as we can tell, this hypothetical 40 tonnes to LEO would expend both the Booster and upper stage, which together cost some $500 million to build, putting the launch cost at roughly $550 million for 40 tonnes to LEO. For some context, Falcon Heavy can launch 63,800 kg to LEO when fully expended at a cost of just $150 million. That makes Starship V3 5.8 times more expensive per kg to LEO than a rocket that has been in operation for nearly a decade.

Just to remind you, even having to expend the upper stage of Starship totally ruins the premise of this launch vehicle, as even if the booster is recovered, Falcon Heavy will still be cheaper and more reliable.

If it isn’t obvious, this all proves that Starship could, in theory, reach LEO and still land the upper stage — it would just have functionally zero payload capacity (the payload to LEO when the upper stage is expended is roughly the same as the fuel needed to land from a 550 km orbit), which kind of defeats the point of this moronic rocket.

Summary

Now, I may be off with my assumptions. Maybe this Starship had far more reserve fuel in the tanks, though I can’t think of a single reason why it would. Maybe it wasn’t being pushed to its payload limit, even though the engine failures made it seem that way, and even though the IPO pressure will be motivating SpaceX to make it look like the payload capacity is improving. But it could very easily swing the other way, too. I assumed the Booster failed to land because of engine failure. Maybe it was actually because it used all its landing fuel during the launch to give the upper stage an extra push, leaving it no option but to tumble back to Earth and artificially increase the payload? Musk has done significantly shadier stuff in the past, after all.

Either way, I hope this has shown you why deploying a 44-tonne payload on a suborbital flight that maxed out at an altitude of 194 km is not a step forward for Starship, despite what many in the media are spouting. Yes, it is better than the V2’s performance, but it hasn’t really got closer to its target. It’s still a functionally useless rocket.

So, no, this test flight was not a success. It hasn’t really moved the needle forward. What’s worse is that it strongly implies the fatal flaw here is not in the minutiae. The brilliant miracleworker engineers at SpaceX can’t solve this problem because the fatal flaw lies in the underlying concept of Starship as a fully reusable two-stage rocket that Musk insists on pushing. It is even more evidence that the fuel required to land the upper stage is roughly the same weight as the payload to orbit, making this vehicle fundamentally useless and an unsalvageable dead end.

In time, we will see if my assumptions and analysis are correct. But as it stands, SpaceX hasn’t actually demonstrated any meaningful progress. As such, this launch just looks like a pathetic attempt to polish a giant festering turd. Sadly, the media seems to have lost all critical thinking abilities and is eating up SpaceX’s faecal polish hook, line and sinker.

Thanks for reading! Everything expressed in this article is my opinion, and should not be taken as financial advice or accusations. Don’t forget to check out my YouTubechannel for more from me, or Subscribe. Oh, and don’t forget to hit the share button below to get the word out!

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