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Ed Zitron's worst enemy, an AI summarization of his anti-AI content. Get the squeeze of the Where's Your Ed At newsletter.
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Anthropic’s revenue is soaring. Its compute costs are rising faster. That gap is a useful place to start asking who’s earning a return on the AI boom, and who’s financing the wait. Where are the returns? Anthropic’s moving financial picture The compute bill How durable is demand? What customers actually pay Financing and exit incentives Source: Ed Zitron's Where's Your Ed At — Exit Liquidity This podcast was created with Podkey. Make your own at https://podkey.fm
The AI trade has a striking amount riding on a handful of companies. The question is whether the revenue beneath all that spending can catch up. A concentrated bet Where the returns have to come from Racks before power Debt against unfinished facilities Who pays whom Who bears the downside The missing receipts Source: Ed Zitron's Where's Your Ed At — AI Had Better Work Out This podcast was created with Podkey. Make your own at https://podkey.fm
A company can announce a $48 billion annualized revenue run rate in May and report $15.5 billion in revenue for the entire first half of the year. Both figures can be accurate. The trouble starts when we treat them as though they answer the same question. What a run rate measures The customers behind lab revenue What compute earns The stakes of the buildout Source: Ed Zitron's Where's Your Ed At — Annualized Enron Rate This podcast was created with Podkey. Make your own at https://podkey.fm
An AI company can sign a contract for compute years into the future. Someone still has to borrow the money to build the data center, buy the chips, and get the power connected. The price of money When a project needs a lender The labs’ financing wall Who is buying the compute? Risk travels through contracts The customer cash-flow test Source: Ed Zitron's Where's Your Ed At — Credit Crunch This podcast was created with Podkey. Make your own at https://podkey.fm
A GPU can be sold, financed, and counted as AI growth before it ever runs a customer’s workload. How much of this boom survives the trip from purchase order to paid invoice? Sold chips, usable capacity Revenue moving in circles Borrowing into the buildout The contract underneath the data center Customers funded by customers The labs at the center Source: Ed Zitron's Where's Your Ed At — Dead Money This podcast was created with Podkey. Make your own at https://podkey.fm
A gigawatt sounds concrete. So does a GPU sale. Yet neither phrase tells us whether a customer can use that compute today, or whether it is sitting behind an unfinished building, a missing power connection, and a financing promise. What does capacity actually mean? The pile of bought, uninstalled hardware Depreciation postpones the bill Who is actually buying compute? The overbuild question GPU demand versus useful demand Source: Ed Zitron's Where's Your Ed At — Where're All The AI Chips? This podcast was created with Podkey. Make your own at https://podkey.fm
A model gets pointed at a cybersecurity benchmark, finds a path through somebody else’s infrastructure, and suddenly the story becomes “the AI escaped.” That phrase can obscure the most important question: who built the setup, authorized the access, and watched the system while it ran? The Hugging Face incident Responsibility stays with the deployer Safety rhetoric and present harms The slowdown problem Hype meets public panic Legal accountability Source: Ed Zitron's Where's Your Ed At — AI Is Already In Dangerous Hands This podcast was created with Podkey. Make your own at https://podkey.fm
“AGI has arrived” makes a great headline. A trillion dollars of compute commitments makes a much harder spreadsheet. The question hanging over this boom is simple: who ultimately pays for all this capacity, and with what cash flow? AGI as a moving target Revenue concentration behind the boom The compute reset wall Circularity and the backlog story Where end-user demand stands What would break the narrative Source: Ed Zitron's Where's Your Ed At — Concentration Risk This podcast was created with Podkey. Make your own at https://podkey.fm
A trillion-dollar technology buildout can look inevitable long before its economics are settled. That gap between the story and the underlying evidence is where this episode lives. Hypernormalization Who Gets Rescued Media and the Reality Gap AI’s Financial Loop Run Rates and Valuations Infrastructure and Productivity Source: Ed Zitron's Where's Your Ed At — Hyperscale Normalization This podcast was created with Podkey. Make your own at https://podkey.fm
A tool that helps debug a crash log can be useful. A trillion-dollar industry built around the promise that it will eventually think, work, and decide for us deserves a far harder audit. The question isn’t whether language models can do anything. It’s whether their demonstrated value matches the certainty, spending, and social pressure surrounding them. Useful tools and the boundary of judgment Outsourcing work versus thought The financial story under the hype Inference costs and productivity proof Growth, power, and human investment Future tense and accountability The maintenance burden Source: Ed Zitron's Where's Your Ed At — The AI Hater's Manifesto This podcast was created with Podkey. Make your own at https://podkey.fm
The core question here isn’t whether people want AI. It’s whether the biggest AI company can sell more of it without losing even more money. And if the answer is no, that problem doesn’t stay inside one startup. It leaks into cloud giants, chip vendors, lenders, and a whole lot of investor storytelling. Unsustainable economics Compute obligations and circular demand Funding constraints Run-rate headlines versus durable revenue Anthropic, IPO optics, and valuation logic What failure would actually look like Systemic risk or overheated narrative Source: Ed Zitron's Where's Your Ed At — What Happens If OpenAI Dies? This podcast was created with Podkey. Make your own at https://podkey.fm
A lot of the AI story still gets told through stock charts, capex totals, and vibes. The harder question is whether there are enough real customers, at real prices, to justify what everybody is building. Revenue concentration Demand versus capacity How the growth story got framed Circular financing questions What breaks if the labs slow down Source: Ed Zitron's Where's Your Ed At — Don't Look Up This podcast was created with Podkey. Make your own at https://podkey.fm
A lot of the AI story gets told as pure technology progress. But if the customers only exist because the vendor helped finance them into existence, you’re not just looking at demand. You’re looking at a system. The core claim What Jensen Huang admitted How dependent the neoclouds look The financing loop Technology versus hype economics Why the GE Capital comparison matters What this protects and what it risks Bottom line Source: Ed Zitron's Where's Your Ed At — Premium: The Hater's Guide To NVIDIA (Part 2) This podcast was created with Podkey. Make your own at https://podkey.fm
The headline here isn't just that AI is expensive. It's that one customer-partner relationship may already be large enough to shape how we read Microsoft's entire AI business. And once you look at the disclosed revenue mix next to the scale of capital spending, the obvious question is whether this is proof of strength, or a warning about concentration. What the revenue disclosure actually says Why concentration matters What this could mean strategically The capital spending number How to connect the two stories carefully What would strengthen either case Source: Ed Zitron's Where's Your Ed At — News: Microsoft Disclosures Suggest OpenAI Sales Account For Around 70% Of FY26 AI Revenue, More Than 7% of FY26 Revenue This podcast was created with Podkey. Make your own at https://podkey.fm
A lot of the AI economy starts looking strange once you ask a boring question: who is actually paying for all this? If the answer is basically two companies, then the story shifts from explosive broad demand to concentrated financial dependency. Revenue concentration Capex versus actual demand The circular funding question Is there real demand beyond the labs Disclosure and narrative framing Debt, balance sheets, and second-order risk Why NVIDIA is part of the same story What actually seems most supported Source: Ed Zitron's Where's Your Ed At — The AI Demand Bubble This podcast was created with Podkey. Make your own at https://podkey.fm
A lot of the AI conversation is being carried by vibes, projections, and fundraising headlines. The awkward part is that some of the people closest to the data are also admitting the biggest labor-market claims still are not showing up in reality. AI job impact narratives Economic indices and narrative incentives Revenue versus funding mismatch The scale of commitments Infrastructure costs and profitability Venture capital exposure What actually seems supported Source: Ed Zitron's Where's Your Ed At — Premium: AI Is Getting Way Too Expensive This podcast was created with Podkey. Make your own at https://podkey.fm
A lot of the AI story still sounds like physics and destiny. But the pressure is showing up somewhere less glamorous: bond spreads, memory prices, lease obligations, and the fact that nobody wants to say exactly how much money this is making. Blackwell efficiency claims The capex mountain What bond markets are saying The memory cost spiral Circular financing and missing foundations The startup business model problem Opaque revenue and the hype machine What could break, and what might not Source: Ed Zitron's Where's Your Ed At — The More You Buy, The More You Lose This podcast was created with Podkey. Make your own at https://podkey.fm
Oracle's story right now sounds simple if you only read the hype: huge AI demand, huge data centers, huge future. The harder question is whether the business underneath that story is actually getting stronger, or whether scale is masking fragility. The baseline problem Free cash flow and what changed The AI GPU spending surge The OpenAI contract question Cerner and the margin mix Accounting and the story management problem Source: Ed Zitron's Where's Your Ed At — Premium: The Hater’s Guide To Oracle (Part 2) This podcast was created with Podkey. Make your own at https://podkey.fm
A lot of the AI story is being told as a technology race. But underneath it, there's a financing story, and that story matters because booms usually break where the money got weird. Why the CDO comparison exists Off-balance-sheet debt and what it hides Overbuild versus actual demand Customer concentration and contract cliffs NVIDIA's backstop and circular demand Who ultimately holds the risk Why so much money flowed in How bad could it get Source: Ed Zitron's Where's Your Ed At — The Subprime Data Center Crisis This podcast was created with Podkey. Make your own at https://podkey.fm
The core claim here is bigger than one startup maybe overspending on GPUs. It’s that OpenAI has become the story holding together a huge chain of spending, lending, construction, and stock-market optimism, and if that story breaks, a lot of supposedly separate bets may turn out to be the same bet. The load-bearing narrative OpenAI’s spending and obligations Who actually holds the risk Oracle as the sharpest edge Memory chips and knock-on inflation Phantom deals and media amplification SoftBank and the funding chain Anthropic as a mirror image What would actually happen in a failure Source: Ed Zitron's Where's Your Ed At — The OpenAI Bubble This podcast was created with Podkey. Make your own at https://podkey.fm