Big Tech's $160B paper-profit illusion hinges on Anthropic and OpenAI IPOs
The Q2 earnings beat looked enormous. Alphabet's net income tripled. Amazon's nearly did. New reporting from the Financial Times says more than $160 billion of those profits came from "other income" — mark-ups on the cloud giants' own stakes in AI labs, not from new ads, new shoppers or new AWS customers. The number is real under accounting rules and almost entirely unreal in cash. Whether it stays on the books depends on the next two IPOs.
The mechanics are simple and circular. Alphabet booked just over $99 billion in equity gains in Q2, mostly on SpaceX and what is widely understood to be its Anthropic position; Amazon's $53.4 billion in non-operating income came largely from marking up its Anthropic preferred shares after a new funding round. The FT's framing — that this is the AI economy reflecting value back on itself — is the right one: cloud giants fund model labs, model labs spend heavily on cloud and chips, and later funding rounds let the same giants re-value those bets upward. None of it is fraud. All of it is mark-to-market accounting that loses its comfort the moment a real, public price appears.
That moment is coming. Anthropic filed confidential IPO papers earlier this summer and is now testing what private marks can survive a Nasdaq opening. If Anthropic lists below its $965 billion Series H post-money, the same balance sheets that just booked $99 billion in upward adjustments can be forced to book write-downs. OpenAI's own confidential filing is on the same horizon. Two AI labs going public in the same window won't be judged in isolation — investors will compare revenue quality, compute costs and how much of the profit story was financed by the same handful of partners.
For now the market is willing to live with the illusion. Alphabet's search business is still enormous. AWS still throws off real operating income. Nobody is mistaking these for weak companies. But the cheer over a 298% profit jump is reading the same kind of profit as a retailer selling more goods — and it isn't. We have covered this build-out's cost side — the $1 trillion AI build-out hits a wall cash can't fix — but the $160 billion Q2 phantom is the asset side of the same story: the AI trade is now large enough that its valuation flips show up in Big Tech's headline earnings.
A hobbyist kernel pack called R9V just made AMD's R9700 a serious local-AI card
For most of the last two years, if you wanted to run a frontier-sized open model on consumer hardware, you bought an Nvidia card and waited for a CUDA-native inference engine. AMD's RDNA4 desktop parts had the memory bandwidth; they didn't have the kernel work. A new project called R9V, dropped this weekend on r/LocalLLaMA, changes that math in a single Reddit post: on two R9700s running the Qwen3.8-Flash-Next IQ4_XS quant with tensor parallelism and SSD n-gram streaming, the author reports 78 tokens per second on a 256-token generation — roughly a 3× bump over the public vLLM-Radiance baseline — and 1,512 tokens per second on an 8,192-token prompt prefill, which the post flags as a ~30× improvement.
The package is open source (engine and kernels on GitHub, quant packages on Hugging Face) and applies vLLM-Radiance to RDNA4 in the way Ninfer and DS4 have done for Nvidia: hand-tuned kernels that exploit the architecture's quirks instead of fighting them. A separate build for a dense 30B model called Muse Glimmer hits 59.65 tok/s on a single R9700 — about 1.4× the open llama.cpp ROCm baseline at the same quant. The author says single-card Qwen3.8-27B and an IQ3 single-card Qwen3.8-Flash-Next path are next.
The headline matters because it is the first time the RDNA4 consumer card has been put on a credible footing against an Nvidia 5090 in the local-AI race. The 5090 still wins on raw single-stream speed; the dual-R9700 path now beats it on cost-per-token for someone who already owns the cards. That is a small market — the people who can afford two R9700s plus 128 GB of DDR5 — but it is the same shape of niche that pulled CUDA onto every ML workstation a decade ago, and it is exactly what AMD has needed to make its consumer GPUs a real second option.
What to watch: Anthropic's confidential IPO timing, and whether the next private mark survives its first public tape; OpenAI's filing following on the same window; the first RDNA4 single-card Qwen3.8-27B benchmark.
Do you think the AI bubble is now big enough that Big Tech should mark these stakes at a discount to private round price, not premium? Tell us in the comments.
Sources: R9V project (GitHub)
