Claims checked against evidence by Ed Zitron, page 7 of 11
Deadlines are judged as of 2026-10-29. About Ed Zitron and the sources covered.
Claims checked against evidence by Ed Zitron (1044)
Each of these claims was checked against dated evidence.
Page 7 of 11: claims 601 to 700 of 1044, ordered by the date first said, newest first. To find a claim by its words, use search.
Assessment: UnresolvedSaid
Observation
“AI data centers are nothing like fiber, because there are very few actual use cases for these GPUs outside of AI, and none of them are remotely hyperscale revenue drivers.”
Our reading: AI data center GPUs have very few use cases outside AI, none of which drive hyperscale revenue, unlike fiber.
No horizon Evidence as of
Limitation: The examples are not a comprehensive workload survey.
Source: The Case Against Generative AI
Assessment: UnresolvedSaid
Causal claim
“In fact, every user loses you money in generative AI, because it’s impossible to do cost control in a consistent manner.”
Our reading: Every generative AI user loses the provider money because consistent cost control is impossible.
No horizon Evidence as of
Limitation: Public pricing is not the provider's actual cost.
Source: The Case Against Generative AI
Assessment: UnresolvedSaid
Forecast
“In the current relationship, NVIDIA Is Not Sending OpenAI $100 Billion, nor will it send it that much money, because 90% of OpenAI’s funding is gated behind building 9 or 10 gigawatts of data center capacity.”
Our reading: Under the current deal NVIDIA will not send OpenAI $100 billion, because 90% depends on building 9-10GW of capacity.
No deadline stated Evidence as of
Limitation: A later funding round cannot automatically be treated as the final outcome of the original arrangement.
Source: The Case Against Generative AI
Assessment: ContradictedSaid
Causal claim
“AI does not have a “profit lever,” because the raw costs of providing access to AI models are so high (and they’re only increasing) that the basic economics of how the tech industry sells software don’t make sense.”
Our reading: AI businesses have no profit lever because model access costs are high and rising.
No horizon Evidence as of
Limitation: The result turns on reading 'profit lever' as reaching operating profit through scale.
Source: The Case Against Generative AI
Assessment: ContradictedSaid
Observation
“As I discussed a month or so ago, data center development accounted for more of America’s GDP growth than all consumer spending combined, and there really isn’t any demand for AI in general, let alone at the scale that these hundreds of billions of dollars are being sunk into.”
Our reading: There really isn't any demand for AI in general, let alone at the scale of the hundreds of billions of dollars being sunk into it.
No horizon Evidence as of
Limitation: The weekly usage figure does not distinguish free from paid users [e1].
Source: The Case Against Generative AI
Assessment: UnresolvedSaid
Forecast
“I am telling you, as I have been telling you for years, again and again and again, that the demand is not there for generative AI, and the demand is never, ever arriving.”
Our reading: Demand for generative AI is not there and will never arrive.
No deadline stated Evidence as of
Limitation: The outcome depends on the contextual meaning of demand; a simple paying-customer reading would face direct counterevidence.
Source: The Case Against Generative AI
Assessment: UnresolvedSaid
Forecast
“There is simply no space in these projections to absorb that $300 billion, and from what I can tell, by 2029, OpenAI will have actually burned more than $290 billion, assuming that it survives that long, which I do not believe it will.”
Our reading: OpenAI will not survive until 2029.
Not yet due Evidence as of
Limitation: Operating announcements are not an audit of financial solvency.
Source: The Case Against Generative AI
Assessment: UnresolvedSaid
Observation
“In reality, LLMs can generate code, and do some software engineering-adjacent tasks, but, like all Large Language Models, break down and go totally insane, hallucinating more as the tasks get more complex.”
Our reading: Coding LLMs can generate code but break down and hallucinate more as tasks get more complex.
No horizon Evidence as of
Limitation: Prompt variations are not established here as a measure of engineering-task complexity [e1].
Source: The Case Against Generative AI
Assessment: MixedSaid
Causal claim
“They don’t even speed up engineers — they actually make them slower!”
Our reading: Coding LLMs do not speed up engineers and actually make them slower.
No horizon Evidence as of
Limitation: The trials should not be pooled into an industry-wide estimate without accounting for their different settings.
Source: The Case Against Generative AI
Assessment: UnresolvedSaid
Forecast
“I’m going to walk you through where I see this industry today, and why I see no future for it beyond a fiery apocalypse.”
Our reading: He sees no future for the generative AI industry beyond a fiery apocalypse.
No deadline stated Evidence as of
Limitation: The collapse criterion interprets a metaphor.
Source: The Case Against Generative AI
Assessment: UnresolvedSaid
Forecast
“To Summarize: Coding LLMs Don’t Actually Replace Software Engineers, and Never Will, Due To The Inherent Unreliability Of Large Language Models”
Our reading: Coding LLMs do not and never will replace software engineers because LLMs are inherently unreliable.
No deadline stated Evidence as of
Limitation: A broader reading that includes any avoided hiring would make the company survey more directly contradictory.
Source: The Case Against Generative AI
Assessment: UnresolvedSaid
Observation
“Generative AI can’t do the job of a software engineer, and it fails while also costing abominable amounts of money.”
Our reading: Generative AI cannot do a software engineer's job, and it fails while costing huge amounts.
No horizon Evidence as of
Limitation: A benchmark of individual problems does not reproduce an engineer's full responsibilities.
Source: The Case Against Generative AI
Assessment: UnresolvedSaid
Observation
“Can Sora — OpenAI’s video creation tool — replace actors or animators? No, not at all, but it still fills the air full of tension because you can immediately see who is pre-registered to replace everyone that works for them.”
Our reading: OpenAI's Sora cannot replace actors or animators.
No horizon Evidence as of
Limitation: The strongest firsthand music-video account predates the target period.
Source: The Case Against Generative AI
Assessment: ContradictedSaid
Observation
“To be clear, some people have lost jobs to AI, just not the white collar workers, software engineers, or really any of the career paths that the mainstream media and AI investors would have you believe.”
Our reading: Some people have lost jobs to AI, but not white collar workers or software engineers as media and investors claim.
No horizon Evidence as of
Limitation: The result depends on assessing the categorical sentence rather than replacing it with the surrounding at-scale argument.
Source: The Case Against Generative AI
Assessment: UnresolvedSaid
Observation
“Which is to say that Microsoft clearly doesn’t have any good news to share, and as I’ll reveal later, they can’t even get 3% of their 440 million Microsoft 365 subscribers to pay for Microsoft 365 Copilot.”
Our reading: Microsoft cannot get even 3% of its 440 million Microsoft 365 subscribers to pay for Microsoft 365 Copilot.
No horizon Evidence as of
Limitation: The January 2026 count cannot establish whether August 2025 adoption was below the claim's threshold [e1].
Source: The Case Against Generative AI
Assessment: UnresolvedSaid
Causal claim
“This is, by the way, why we are still yet to get any tangible proof that AI is replacing software engineers…because it can’t.”
Our reading: There is still no tangible proof that AI is replacing software engineers because AI can't replace them.
No horizon Evidence as of
Limitation: The result depends on distinguishing full-role replacement from partial automation and avoided hiring.
Source: The Case Against Generative AI
Assessment: SupportedSaid
Observation
“You cannot rely on a large language model to do what you want.”
Our reading: Large language models cannot be relied on to do what users want.
No horizon Evidence as of
Limitation: The strongest outcome evidence concerns difficult software-engineering tasks.
Source: The Case Against Generative AI
Assessment: SupportedSaid
Observation
“You can add as many levels of intrigue and “reasoning” as you want, but Large Language Models cannot be trusted to do something correctly, or even consistently, every time.”
Our reading: Regardless of added reasoning, LLMs cannot be trusted to do things correctly or consistently every time.
No horizon Evidence as of
Limitation: One source is a vendor evaluation and the independent study is a preprint.
Source: The Case Against Generative AI
Assessment: UnresolvedSaid
Causal claim
“You do not know what you’re going to get each time, and hallucinations multiply with the complexity of the thing you’re asking for, or whether a task contains multiple steps (which is a fatal blow to the idea of “agents.””
Our reading: LLM hallucinations multiply with task complexity and multi-step tasks, which is fatal to the idea of agents.
No horizon Evidence as of
Limitation: Puzzle performance is not a direct measure of practical autonomous work.
Source: The Case Against Generative AI
Assessment: UnresolvedSaid
Observation
“Even Anthropic and OpenAI seem a little lethargic, both burning billions of dollars while making, by my estimates, no more than $2 billion and $6.26 billion in 2025 so far, despite projections of $5 billion and $13 billion respectively.”
Our reading: Anthropic and OpenAI seem a little lethargic, both burning billions while making, by his estimates, no more than $2 billion and $6.26 billion respectively in 2025 so far, against full-year projections of $5 billion and $13 billion.
No horizon Evidence as of
Limitation: Treating September 30 as equivalent to September 29 could change the result; that substitution is not made.
Source: The Case Against Generative AI
Assessment: UnresolvedSaid
Observation
“Can large language models actually replace coders? Not really, no, and I’ll get into why later in the piece.”
Our reading: Large language models cannot really replace coders.
No horizon Evidence as of
Limitation: Task success, developer productivity and job replacement are different outcomes.
Source: The Case Against Generative AI
Assessment: UnresolvedSaid
Forecast
“As a reminder, OpenAI has leaked that it’ll burn $115 billion in the next four years, and based on my estimates, it needs to raise more than $290 billion in the next four years based on its $300 billion deal with Oracle alone.”
Our reading: By his estimates, based on its $300 billion Oracle deal alone, OpenAI needs to raise more than $290 billion in the next four years.
Not yet due Evidence as of
Limitation: The executed agreement and detailed payment obligations were not available.
Source: The Case Against Generative AI
Assessment: UnresolvedSaid
Observation
“OpenAI is now, at this point, on the hook for over a trillion dollars, an egregious sum for a company that already forecast billions in losses, with no clear explanation as to how it’ll afford any of this beyond “we need more money” and the vague hope that there’s another Softbank or Microsoft waiting in the wings to swoop in and save the day.”
Our reading: OpenAI is committed to over a trillion dollars of spending with no clear explanation of how it will pay.
No horizon Evidence as of
Limitation: The partnership and project totals do not fully identify OpenAI's own payment obligations or their overlap [e1] [e3].
Source: The Case Against Generative AI
Assessment: ContradictedSaid
Observation
“...maybe? It sure isn't trending that way, nor has it gone down yet.”
Our reading: The cost of inference is not trending downward and has not gone down yet, though he says it maybe could go down.
No horizon Evidence as of
Limitation: Fixed-capability benchmark costs do not establish aggregate application spending or costs for increasingly demanding workloads.
Source: How To Argue With An AI Booster
Assessment: UnresolvedSaid
Observation
“Based on my (conservative) calculations, the true "cost" of OpenAI is around $82 billion, and that only includes capex from 2024 onward, based on 30% of Microsoft's capex (as not everything has been invested yet in 2025, and OpenAI is not necessarily all of the capex) and the $41.4 billion of funding it’s received so far.”
Our reading: Attributing 30% of Microsoft's capex since 2024 and adding OpenAI's $41.4B in funding, OpenAI's true cost is about $82 billion.
No horizon Evidence as of
Limitation: The allocation percentage is an explicit assumption.
Source: How To Argue With An AI Booster
Assessment: UnresolvedSaid
Observation
“The cost of inference has gone up. Statements otherwise are purely false, and are the opinion of somebody who does not know what he's talking about.”
Our reading: The real cost of AI inference has risen, and claims that it is falling are false.
No horizon Evidence as of
Limitation: The disclosures do not provide representative longitudinal application bills linked to completed work, quality and model versions.
Source: How To Argue With An AI Booster
Assessment: UnresolvedSaid
Forecast
“Unlike CPUs, a GPU can't easily change tasks, or handle many little distinct operations, meaning that these things aren't going to be adopted for another mass-scale use case.”
Our reading: AI GPUs will not be adopted for another mass-scale use case, because GPUs cannot easily switch tasks.
No deadline stated Evidence as of
Limitation: The scale required for contradiction is interpretive.
Source: How To Argue With An AI Booster
Assessment: UnresolvedSaid
Forecast
“QUIP: AI Will Do Science Research, Somehow — No it won't, here's a writeup about why foundation models can't do this.”
Our reading: AI foundation models will not do scientific research.
No deadline stated Evidence as of
Limitation: Initial workshop review does not by itself establish independent validation of the scientific result.
Source: How To Argue With An AI Booster
Assessment: UnresolvedSaid
Forecast
“There is no proof this is possible, nobody has done it, nobody will do it.”
Our reading: Nobody will build the self-learning, self-teaching AI agent that AI 2027 assumes.
No deadline stated Evidence as of
Limitation: The outcome depends on retaining the AI 2027 context; a reading covering any self-modifying agent could produce a different assessment.
Source: How To Argue With An AI Booster
Assessment: SupportedSaid
Observation
“While young people are having trouble finding jobs, there is no proof that AI is the reason.”
Our reading: There is no proof that AI is why young people are struggling to find jobs.
No horizon Evidence as of
Limitation: The conclusion concerns what had been established by publication, not whether AI actually affected hiring.
Source: How To Argue With An AI Booster
Assessment: UnresolvedSaid
Forecast
“This isn't a case where "the enterprise" is suddenly going to save these companies, because the enterprise already tried, and it isn't working.”
Our reading: Enterprise demand will not rescue generative AI companies, because enterprises have already tried the technology and it is not working.
No deadline stated Evidence as of
Limitation: The financial evidence covers one provider and does not settle outcomes for the unspecified group of companies.
Source: How To Argue With An AI Booster
Assessment: UnresolvedSaid
Conditional forecast
“The "fiber optic cable" of this era is mostly owned by a few companies. 42% of NVIDIA's revenue is from the magnificent 7, and the companies buying these GPUs are, for the most part, not going to go bust once the AI bubble bursts.”
Our reading: For the most part, the companies buying AI GPUs are not going to go bust once the AI bubble bursts.
No deadline stated Evidence as of
Only if: once the AI bubble bursts
Source: How To Argue With An AI Booster
Assessment: UnresolvedSaid
Observation
“The true cost of Anthropic is around $77.1 billion, including all its funding and 30% of Amazon's capex from the beginning of 2024.”
Our reading: Counting all its funding plus 30% of Amazon's capex since 2024, Anthropic's true cost is about $77.1 billion.
No horizon Evidence as of
Limitation: The reconstruction of the unstated funding subtotal is an evaluator inference.
Source: How To Argue With An AI Booster
Assessment: UnresolvedSaid
Conditional forecast
“Every member of the government having access to ChatGPT — something that is not even necessarily the case! — does not make this software useful, let alone essential, and if OpenAI burns a bunch of money "making it work for them," it still won't be essential, because Large Language Models are not actually that useful for doing stuff!”
Our reading: Even if OpenAI spends heavily tailoring ChatGPT for government use, it will not become essential, because LLMs are not very useful.
No deadline stated Evidence as of
Only if: even if OpenAI spends heavily to make it work for the government
Source: How To Argue With An AI Booster
Assessment: UnresolvedSaid
Observation
“ChatGPT is basically the same product as Claude or Grok or any number of different LLMs.”
Our reading: ChatGPT is essentially the same product as Claude, Grok and other LLMs.
No horizon Evidence as of
Limitation: Basically the same permits differences, so finding any difference would not automatically refute the claim.
Source: How To Argue With An AI Booster
Assessment: UnresolvedSaid
Observation
“These data centers, once built, also create very little economic activity. They don't create jobs, they take up massive amounts of land and utilities, and they piss off and poison their neighbors.”
Our reading: Once built, AI data centers create very little economic activity, do not create jobs, take up massive amounts of land and utilities, and piss off and poison their neighbors.
No horizon Evidence as of
Limitation: Most directly relevant evidence concerns one facility.
Source: How To Argue With An AI Booster
Assessment: ContradictedSaid
Forecast
“In any case, there really is no changing this path. They are out of ideas.”
Our reading: AI model developers are out of ideas and will not escape the path of improving models by burning more tokens.
Settled before its deadline Evidence as of
Limitation: The result concerns the tested model comparisons, not every task or every subsequent model.
Source: How To Argue With An AI Booster
Assessment: ContradictedSaid
Observation
“Reasoning models are also the only way that model developers have been able to improve the efficacy of new models, using something called "test-time compute" to burn extra tokens to complete a task.”
Our reading: Model developers have only managed to make new models more effective through reasoning models that burn extra tokens via test-time compute.
No horizon Evidence as of
Limitation: The performance results and attribution to unsupervised scaling come from OpenAI; the packet provides no independent replication.
Source: How To Argue With An AI Booster
Assessment: UnresolvedSaid
Forecast
“I don't buy that this company will continue growing forever, and its stinky conversion rate isn't going to change anytime soon.”
Our reading: He doesn't believe OpenAI will continue growing forever, and OpenAI's poor conversion rate isn't going to change anytime soon.
No deadline stated Evidence as of
Limitation: The packet lacks a verified publication-period share using matched, nonoverlapping paid-user and active-user populations.
Source: How To Argue With An AI Booster
Assessment: UnresolvedSaid
Observation
“You won't be able to find this as it isn't possible.”
Our reading: Someone who cannot code cannot build a fully functional, secure, useful app entirely by vibe coding.
No horizon Evidence as of
Limitation: The main candidate evidence comes from a tool vendor.
Source: How To Argue With An AI Booster
Assessment: ContradictedSaid
Observation
“In simpler terms, these companies are flush with cash, have all the attention and investment they could possibly need, and are still unable to create a product with a defined, meaningful, mass-market use case.”
Our reading: Generative AI companies, despite ample funding and attention, have not produced a product with a defined, meaningful mass-market use case.
No horizon Evidence as of
Limitation: Meaningful and mass-market require interpretation because Zitron supplied no thresholds.
Source: How To Argue With An AI Booster
Assessment: UnresolvedSaid
Observation
“And models keep getting bigger and more expensive, too.”
Our reading: AI models keep getting larger and more expensive.
No horizon Evidence as of
Limitation: The observation window and cost measure require interpretation.
Source: How To Argue With An AI Booster
Assessment: UnresolvedSaid
Forecast
“OpenAI's growth as the single-most-prominent company in AI (and if we're honest, one of the most prominent in software writ large) makes sense, but at some point will slow, because the actual returns for the businesses aren't there.”
Our reading: OpenAI's growth will eventually slow because businesses are not getting real returns from AI.
No deadline stated Evidence as of
Limitation: The revenue interpretation is supplied from context.
Source: How To Argue With An AI Booster
Assessment: UnresolvedSaid
Observation
“These data centers are not, in and of themselves, driving much economic growth other than in the costs of building them.”
Our reading: Apart from construction spending, AI data centers do not drive much economic growth.
No horizon Evidence as of
Limitation: Construction dominance does not imply negligible operating activity.
Source: How To Argue With An AI Booster
Assessment: ContradictedSaid
Causal claim
“Because model developers hit a wall of diminishing returns, and the only way to make their models do more was to make them burn more tokens to generate a more accurate response (this is a very simple way of describing reasoning, a thing that OpenAI launched in September 2024 and others followed).”
Our reading: Inference costs rose because model developers hit diminishing returns and could only improve models by having them burn more tokens through reasoning.
No horizon Evidence as of
Limitation: The decisive evaluation is developer-reported.
Source: How To Argue With An AI Booster
Assessment: UnresolvedSaid
Forecast
“Long story short, agents are not autonomous, they do not replace jobs, they cannot “replace coders,” they are not going to do so because probabilistic models are a horrible means of taking precise actions, and almost anyone who brings up agents as a booster is either misinformed or in the business of misinformation.”
Our reading: AI agents are not autonomous, do not replace jobs or coders, and will not do so, because probabilistic models are poor at taking precise actions.
No deadline stated Evidence as of
Limitation: The outcome depends on distinguishing autonomous whole-job replacement from substantial task displacement.
Source: How To Argue With An AI Booster
Assessment: SupportedSaid
Observation
“It is transparently, blatantly obvious that GPT-4 did not "hire" a Taskrabbit or, indeed, make any of these actions — it was prompted to, and they do not show the prompts they used, likely because they had to use so many of them.”
Our reading: GPT-4 did not autonomously hire a TaskRabbit worker or take the actions described; it was prompted and humans carried them out.
No horizon Evidence as of
Limitation: Support is for the absence of autonomous execution, not for every possible reading of 'any of these actions'.
Source: How To Argue With An AI Booster
Assessment: UnresolvedSaid
Observation
“They don’t make enough money to survive without a continuous flow of venture capital, and they don’t seem to make impressive sums of money even when allowed to burn as much as they’d like.”
Our reading: Generative AI startups do not make enough money to survive without a continuous flow of venture capital, and they do not seem to make impressive sums even when allowed to burn as much as they like.
No horizon Evidence as of
Limitation: Neither the broad reading covering generative AI startups nor the narrow reading covering companies discussed in the post is settled.
Source: AI Is A Money Trap
Assessment: UnresolvedSaid
Forecast
“At some point capex has to slow — if not because of the lack of revenues or massive costs associated, but because we live in a world with finite space, and when said capex slow happens, so will purchases of NVIDIA GPUs, which will in turn, as proven by Kedrosky and others, slow America’s economic growth.”
Our reading: AI capex will eventually slow, reducing NVIDIA GPU purchases and slowing US economic growth.
No deadline stated Evidence as of
Limitation: Slower growth and lower absolute spending are different tests.
Source: AI Is A Money Trap
Assessment: UnresolvedSaid
Observation
“There are standard ways to handle annualized revenue, and it's clear they're not following them.”
Our reading: OpenAI is not following standard methods for calculating annualized revenue.
No horizon Evidence as of
Limitation: The available methodological descriptions are news reports rather than underlying company records.
Source: AI Is A Money Trap
Assessment: UnresolvedSaid
Forecast
“That capex reduction will bring with it a reduction in expenditures on NVIDIA GPUs, which will take a chunk out of the US stock market. Although the stock market isn’t the economy, the two things are inherently linked, and the popping of the AI bubble will have downstream ramifications, just like the dot com bubble did on the wider economy.”
Our reading: Reduced AI capex will cut NVIDIA GPU spending, hit the US stock market, and the AI bubble's popping will damage the wider economy like the dot-com bust.
No deadline stated Evidence as of
Limitation: One company's guidance does not establish aggregate realized AI investment.
Source: AI Is A Money Trap
Assessment: UnresolvedSaid
Observation
“Large Language Models do not scale profitably, and their functionality is not significant enough to justify the costs of running them.”
Our reading: LLMs don't scale profitably and their functionality doesn't justify their running costs.
No horizon Evidence as of
Limitation: The primary filing covers one provider.
Source: AI Is A Money Trap
Assessment: UnresolvedSaid
Conditional forecast
“Expect to see an acceleration in layoffs and offshoring, in part driven by a need for tech companies to show — for the first time in living memory — fiscal restraint.”
Our reading: When the AI bubble pops and capex falls, layoffs and offshoring will accelerate, driven partly by tech companies needing to show fiscal restraint for the first time in living memory.
No deadline stated Evidence as of
Only if: when the AI bubble pops and capex falls
Source: AI Is A Money Trap
Assessment: UnresolvedSaid
Forecast
“If I’m honest, I think the future of LLMs will be client-side on egregiously-expensive personal setups for enthusiasts, and in a handful of niche enterprise roles.”
Our reading: He thinks the future of LLMs will be client-side on egregiously expensive personal setups for enthusiasts and in a handful of niche enterprise roles.
No deadline stated Evidence as of
Limitation: The seat-growth figure covers a year, not just the period after the August 2025 prediction [e1].
Source: AI Is A Money Trap
Assessment: UnresolvedSaid
Observation
“The reality is simple: the majority of all AI capex is from big tech, which is a massive systemic weakness in our economy.”
Our reading: Most AI capex comes from big tech, which is a major systemic weakness in the US economy.
No horizon Evidence as of
Limitation: The target quarter is an interpretation of wording that mixes quarterly and annual figures.
Source: AI Is A Money Trap
Assessment: ContradictedSaid
Observation
“We also have had no exits and no IPOs.”
Our reading: Generative AI startups have had no exits and no IPOs.
No horizon Evidence as of
Limitation: The post already acknowledged Leonardo.ai elsewhere; Base44 supplies an additional acquisition example [e1, e2].
Source: AI Is A Money Trap
Assessment: UnresolvedSaid
Forecast
“It already has their own extremely-expensive coding environment, Claude Code, which I estimated loses the company 100% to 10,000% of a subscription per-customer a few weeks ago, and now Anthropic is adding weekly limits on accounts, which will, I believe, create some of the most gnarly churn in SaaS history.”
Our reading: He believes Anthropic's new weekly limits on Claude Code accounts will create some of the most gnarly churn in SaaS history.
No deadline stated Evidence as of
Limitation: The implementation evidence confirms existence by March 2026, not the exact initial rollout date.
Source: AI Is A Money Trap
Assessment: UnresolvedSaid
Conditional forecast
“While its capex may not have dipped yet (in part because the chip-heavy nature of generative AI means that capex isn’t exclusively dominated by property), it’s now obvious that if it does there will be direct effects on both the US economy and stock market, as Microsoft is part of what amounts to a stimulus package propping up America’s economic growth.”
Our reading: If Microsoft's capex dips, it will directly hurt the US economy and stock market.
No deadline stated Evidence as of
Only if: if Microsoft's capex dips
Source: AI Is A Money Trap
Assessment: UnresolvedSaid
Forecast
“Even if this were going to happen — it will not! — who would they give the money to and for how long?”
Our reading: The US government will not give generative AI companies money forever.
No deadline stated Evidence as of
Limitation: The practical reading of 'forever' is an evaluator interpretation.
Source: AI Is A Money Trap
Assessment: UnresolvedSaid
Observation
“Why isn’t there one, outside of the companies creating data to train the AI models, or Nvidia? We’re three years in, and we haven’t had one.”
Our reading: Three years in, there is no profitable generative AI company other than training-data firms and Nvidia.
No horizon Evidence as of
Limitation: These are attributed company claims, not verified net-income accounts.
Source: AI Is A Money Trap
Assessment: UnresolvedSaid
Conditional forecast
“We’ll see a drying-up of VC funding. Pension funds will take a hit — which will affect how much people have to spend in retirement. It’ll be grim.”
Our reading: When the AI bubble pops, VC funding will dry up and pension funds will take a hit that affects how much people have to spend in retirement, and it will be grim.
No deadline stated Evidence as of
Only if: when the AI bubble pops
Source: AI Is A Money Trap
Assessment: UnresolvedSaid
Forecast
“Cursor’s product is now worse. People are going to cancel their subscriptions. Its annualized revenue will drop, and its ability to raise capital will suffer as a direct result.”
Our reading: Cursor's product is now worse, so people will cancel their subscriptions, its annualized revenue will drop, and as a direct result its ability to raise capital will suffer.
No deadline stated Evidence as of
Limitation: Company and investor disclosures are interested-party evidence.
Source: AI Is A Money Trap
Assessment: UnresolvedSaid
Observation
“The complexity of these arrangements makes it hard to anticipate the full economic fallout if the cycle breaks down, but the systemic risk is building.”
Our reading: Systemic risk from private credit financing of AI infrastructure is building.
No horizon Evidence as of
Limitation: The facility announcement does not sufficiently disclose drawdowns, collateral, lender concentration or ultimate exposures [e1].
Source: AI Is A Money Trap
Assessment: ContradictedSaid
Forecast
“When? Say OpenAI and Broadcom actually build their ASIC in 2026 (they won't) — how many of them will they build?”
Our reading: OpenAI and Broadcom will not build their custom AI ASIC in 2026.
Settled before its deadline Evidence as of
Limitation: The decisive evidence is a company disclosure, not an independently reproduced hardware test.
Assessment: UnresolvedSaid
Observation
“We’re in a god damn bubble, it is so obvious we’re in a bubble, it’s been so obvious we’re in a bubble, a bubble that seems strong but is actually very weak, with a central point of failure.”
Our reading: AI is obviously in a bubble that looks strong but is very weak, with a central point of failure.
No horizon Evidence as of
Limitation: The cited measurements precede the target market dates and are context only.
Assessment: SupportedSaid
Forecast
“At some point the momentum behind NVIDIA slows.”
Our reading: NVIDIA's momentum will slow at some point.
Settled before its deadline Evidence as of
Limitation: The result depends on interpreting momentum to include a sustained share-price plateau.
Assessment: UnresolvedSaid
Observation
“And worse still, it has no path to profitability, and it’s not even anything new!”
Our reading: Perplexity has no path to profitability, and it is not even anything new, being a search engine.
No horizon Evidence as of
Limitation: Financial context comes from a secondary summary of reporting that could not be read.
Assessment: ContradictedSaid
Observation
“Generative AI does not do the things that it's being sold as doing, and the things it can actually do aren't the kind of things that create business returns, automate labor, or really do much more than one extension of a cloud software platform.”
Our reading: Generative AI does not do what it is marketed as doing, and what it can do neither creates business returns nor automates labor.
No horizon Evidence as of
Limitation: The decisive deployment figure is company-reported through a news outlet, not independently measured.
Assessment: UnresolvedSaid
Observation
“Someone's gonna get mad at me for saying this, but I believe that Meta is simply burning cash on generative AI.”
Our reading: Meta is simply burning cash on generative AI.
No horizon Evidence as of
Limitation: The reporting period and cash-flow meaning are evaluator interpretations.
Assessment: UnresolvedSaid
Observation
“Generative AI products do not provide significant revenue growth, its products are not useful in the way that unlocks significant business value, and the products that have some adoption run at such a grotesque loss.”
Our reading: Generative AI products deliver no significant revenue growth or business value, and those with some adoption run at large losses.
No horizon Evidence as of
Limitation: The strongest customer benefit evidence is company-reported.
Assessment: UnresolvedSaid
Observation
“OpenAI and Anthropic both lose billions of dollars a year after revenue, and their stories do not mirror any other startup in history, not Uber, not Amazon Web Services, nothing.”
Our reading: OpenAI and Anthropic each lose billions of dollars a year after revenue, and their stories do not mirror any other startup in history, including Uber and AWS.
No horizon Evidence as of
Limitation: OpenAI's detailed accounting adjustments are unavailable in the cited report.
Assessment: UnresolvedSaid
Observation
“I realize it's likely a little boring hearing about software as a service, but this is the only place where generative AI can really make money.”
Our reading: Software as a service is the only place generative AI can really make money.
No horizon Evidence as of
Limitation: The revenue-versus-profit interpretation materially affects the assessment.
Assessment: UnresolvedSaid
Forecast
“Sam Altman, Dario Amodei, Satya Nadella, Sundar Pichai, Tim Cook, Elon Musk, Mark Zuckerberg and Andy Jassy have overseen a needless, wasteful and destructive economic force that will harm our markets (and by a larger extension our economy) and the tech industry writ large, and when this is over, they must be held accountable.”
Our reading: The AI boom led by these tech CEOs will harm markets, the wider economy and the tech industry.
No deadline stated Evidence as of
Limitation: The market evidence establishes association, not a complete causal estimate.
Assessment: UnresolvedSaid
Conditional forecast
“OpenAI is Microsoft's largest Azure client — an insanely risky proposition on multiple levels, not simply in the fact that it’s serving the revenue at-cost but that Microsoft executives believed OpenAI would fail in the long term when they invested in 2023 — and Microsoft is NVIDIA's largest client for GPUs, meaning that any changes to Microsoft's future interest in OpenAI, such as reducing its data center expansion, would eventually hit NVIDIA's revenue.”
Our reading: Because Microsoft is NVIDIA's largest GPU client, any changes to Microsoft's future interest in OpenAI, such as reducing its data center expansion, would eventually hit NVIDIA's revenue.
No deadline stated Evidence as of
Only if: if Microsoft's future interest in OpenAI changes, e.g. reducing data center expansion
Assessment: ContradictedSaid
Observation
“As I wrote earlier in the year, there is really no significant adoption of generative AI services or products.”
Our reading: There is no significant adoption of generative AI services or products.
No horizon Evidence as of
Limitation: The decisive count is company-reported through CNBC, not independently audited.
Assessment: UnresolvedSaid
Forecast
“There are limited amounts of capital, but also limited amounts of data centers to actually put GPUs, and on top of that, at some point growth will slow at one of the Magnificent 7, at which point costs will have to come down from things that lose them tons of money, such as generative AI.”
Our reading: At some point growth will slow at one of the Magnificent 7, forcing it to cut money-losing costs such as generative AI.
No deadline stated Evidence as of
Limitation: Revenue growth is an evaluator-selected interpretation of growth.
Assessment: UnresolvedSaid
Observation
“While it theoretically might be, all evidence points to larger models costing more money, especially reasoning-heavy ones like Claude Opus 4.”
Our reading: Although the cost of inference theoretically might be falling, all evidence points to larger models, especially reasoning-heavy ones like Claude Opus 4, costing more money.
No horizon Evidence as of
Limitation: The available studies concern other models and earlier measurements.
Assessment: UnresolvedSaid
Causal claim
“To be clear, I am not saying that any of the Magnificent 7 are going to die — just that five companies' spend on NVIDIA GPUs largely dictate how stable the US stock market will be.”
Our reading: Five companies' spending on NVIDIA GPUs largely determines how stable the US stock market is, though none of the Magnificent 7 is necessarily going to die.
No horizon Evidence as of
Limitation: The cited assessments predate the target date and are context only.
Assessment: UnresolvedSaid
Observation
“Meta is not making any money from generative AI.”
Our reading: Meta is not making any money from generative AI.
No horizon Evidence as of
Limitation: Reported revenue-sharing arrangements do not establish when revenue was earned or recognized [e1].
Source: Make Fun Of Them
Assessment: UnresolvedSaid
Observation
“Sam Altman is nowhere near delivering a functioning agent, let alone anything approaching intelligence, and really only has one skill: making other companies risk a bunch of money on his stupid ideas.”
Our reading: OpenAI under Altman is nowhere near delivering a functioning agent, let alone anything approaching intelligence.
No horizon Evidence as of
Limitation: The strongest independent result examined predates the target period.
Source: Make Fun Of Them
Assessment: UnresolvedSaid
Observation
“According to The Information, generative AI companies have raised more than $18.8 billion in the first quarter of 2025, after investing $21 billion in Q4 2024 and $4 billion in Q3 2024 for a grand total of $43.8 billion, or a total of $370.8 billion of investment and capital expenditures for an industry that, despite being the single-most talked about thing on the planet, cannot even create a tenth of the dollars it requires to make it work.”
Our reading: The generative AI industry cannot generate even a tenth of the $370.8 billion in investment and capex it requires.
No horizon Evidence as of
Limitation: The internal numerical argument fails even though the substantive industry ratio is unresolved.
Source: Make Fun Of Them
Assessment: UnresolvedSaid
Observation
“Less than $39 billion dollars of total revenue in the entire generative AI industry.”
Our reading: By his tally of run rates and projections, the entire generative AI industry has less than $39 billion in total revenue.
No horizon Evidence as of
Limitation: A contemporaneous estimate and a realized full-year result are different claims.
Source: Make Fun Of Them
Assessment: UnresolvedSaid
Observation
“you’d see that the user numbers weren’t there outside of the 500 million people using ChatGPT, of which very few are actually paying for the product, and that the term “user” encompasses everything from the most occasional users who log in out of curiosity, to people who are actually using it as part of their daily lives.”
Our reading: Very few of ChatGPT's 500 million users pay, and user numbers outside ChatGPT are not there.
No horizon Evidence as of
Limitation: Subscriber reports share an underlying source and are not independent confirmations.
Source: Make Fun Of Them
Assessment: UnresolvedSaid
Causal claim
“agents as a concept are fucked because large language models are inherently unreliable”
Our reading: AI agents are fundamentally unworkable because LLMs are inherently unreliable and break down on multi-step tasks.
No horizon Evidence as of
Limitation: Multi-turn interaction is not synonymous with multistep execution.
Source: Make Fun Of Them
Assessment: UnresolvedSaid
Observation
“Products like Word and Excel don’t need improving, but that doesn’t stop Microsoft from trying, bloating them with odd user interface choices and forcing users to fight with popups to use an AI-powered Copilot that most of them hate.”
Our reading: Microsoft bloats Word and Excel with odd interface choices and makes users fight with popups to use an AI-powered Copilot that most of them hate.
No horizon Evidence as of
Limitation: Interface criticism contains subjective judgments.
Source: Make Fun Of Them
Assessment: UnresolvedSaid
Observation
“The difference is that OpenAI has a consumer product that loses billions of dollars, and Lockheed Martin has products that makes billions of dollars by removing consumers from the Earth.”
Our reading: OpenAI's consumer product loses billions of dollars.
No horizon Evidence as of
Limitation: Calendar 2024 is an inferred reporting period.
Source: Make Fun Of Them
Assessment: UnresolvedSaid
Forecast
“No, the cost that people are being charged is going down, we have no firm data on the actual costs because the companies don’t want to talk about it, and yes, it will absolutely lower prices to compete with other companies.”
Our reading: OpenAI will lower its prices to compete with other companies.
No deadline stated Evidence as of
Limitation: Efficiency improvements and competitive motives can coexist.
Source: Make Fun Of Them
Assessment: UnresolvedSaid
Observation
“the fact it plans to lose over a hundred billions of dollars before becoming profitable?”
Our reading: OpenAI plans to lose over $100 billion before becoming profitable.
No horizon Evidence as of
Limitation: Evidence relies on reporting about private projections.
Source: Make Fun Of Them
Assessment: MixedSaid
Forecast
“So what, OpenAI makes $12.7 billion this year, but loses $14 billion, what does that mean to you, exactly?”
Our reading: OpenAI will make about $12.7 billion in revenue in 2025 but lose about $14 billion.
Deadline passed Evidence as of
Limitation: The numerical tolerance and loss interpretation were supplied by the evaluator.
Source: Make Fun Of Them
Assessment: SupportedSaid
Observation
“The most prominent company in the tech industry is an unstable monolith with a vague product that can only make $10 billion a year (revenue, not profit) as the very fabric of its existence is shoved down the throat of every executive in the world at once.”
Our reading: OpenAI is an unstable monolith with a vague product that can only make $10 billion a year in revenue, not profit.
No horizon Evidence as of
Limitation: The figure was company-reported through news outlets, with no audit status supplied.
Source: Make Fun Of Them
Assessment: UnresolvedSaid
Observation
“The reality is far simpler: we have an industry that has spent nearly half a trillion dollars between its capital expenditures and venture capital funding to create another industry with the combined revenue of the fucking smartwatch industry.”
Our reading: Nearly $500 billion in capex and VC has produced a generative AI industry with revenue comparable to the smartwatch industry.
No horizon Evidence as of
Limitation: Several relevant source passages could not be retrieved with read_page.
Source: Make Fun Of Them
Assessment: UnresolvedSaid
Observation
“SoftBank and Microsoft pay all of OpenAI’s bills, and the media does his marketing for him.”
Our reading: SoftBank and Microsoft pay all of OpenAI's bills.
No horizon Evidence as of
Limitation: An investment commitment does not establish funds received or their use for bill payments [e1].
Source: Make Fun Of Them
Assessment: SupportedSaid
Observation
“An aside about software engineering: I actually believe LLMs have some value here.”
Our reading: LLMs have some value in software engineering.
No horizon Evidence as of
Limitation: The current-period evidence is a company estimate.
Source: Sincerity Wins The War
Assessment: UnresolvedSaid
Observation
“Can you replace an accountant? No. A doctor? No. A writer? Not if you want good writing.”
Our reading: Current AI cannot replace an accountant or a doctor, and cannot replace a writer if good writing is wanted.
No horizon Evidence as of
Limitation: The packet contains no accounting evaluation.
Source: Sincerity Wins The War
Assessment: UnresolvedSaid
Observation
“there’s yet to be proof it’s actually happening, or that AI’s taking these jobs and not, say, outsourcing.”
Our reading: There is not yet proof that AI is taking entry-level software engineering jobs, as opposed to, for example, outsourcing.
No horizon Evidence as of
Limitation: The Salesforce evidence is an executive statement reported by news media.
Source: Sincerity Wins The War
Assessment: SupportedSaid
Observation
“Generative AI has sped up transcription and translation, which are useful for quick references but can cause genuine legal risk.”
Our reading: Generative AI has sped up transcription and translation, which are useful for quick reference but can cause genuine legal risk.
No horizon Evidence as of
Limitation: The transcription study measures processing time against an extrapolated human baseline, not a complete corrected workflow.
Source: Sincerity Wins The War
Assessment: UnresolvedSaid
Observation
“The word “powerful” is a marketing term that the media has adopted to describe something it doesn’t understand, along with the word “agent,” which means “autonomous AI that can do things for you” but is used, at this point, to describe any Large Language Model doing anything.”
Our reading: 'Powerful' and 'agent' are marketing terms the media has adopted to describe something it doesn't understand, and 'agent', which means autonomous AI that can do things for you, is now used for any LLM doing anything.
No horizon Evidence as of
Limitation: The evidence is a small set of examples, not a representative corpus.
Source: Sincerity Wins The War
Assessment: UnresolvedSaid
Conditional forecast
“when those LLMs inevitably shit the bed, those people will be hired back.”
Our reading: When the LLMs inevitably fail, the workers whom CEOs fired after buying into the AI hype will be hired back.
No deadline stated Evidence as of
Only if: when those LLMs inevitably fail
Source: Sincerity Wins The War
Assessment: UnresolvedSaid
Causal claim
“The “AI is going to eliminate half of white collar jobs” story is one that’s taken hold because it gets clicks and appeals to a fear that everyone, particularly those in the knowledge economy who have long enjoyed protection from automation, has.”
Our reading: The story that AI will eliminate half of white-collar jobs has taken hold because it gets clicks and appeals to a fear everyone has, especially knowledge-economy workers who have long been protected from automation.
No horizon Evidence as of
Limitation: The headline experiments concern another publisher and an earlier period.
Source: Sincerity Wins The War
Assessment: ContradictedSaid
Observation
“Christopher Mims of the Wall Street Journal had a story from June 2024 that talked about freelance copy editors and concept artists being replaced by generative AI, but I can find no stories about companies replacing employees.”
Our reading: Apart from a June 2024 Wall Street Journal story about freelance copy editors and concept artists being replaced by generative AI, he can find no stories about companies replacing employees with it.
No horizon Evidence as of
Limitation: The result depends on reading the statement as a claim about available reporting.
Source: Sincerity Wins The War