Bears: "I told you so" Meanwhile YTD: -> $SNDK: +372% -> $DELL: +253% -> $MU: +178% -> $AEHR: +173% -> $MRVL: +155% -> $AMD: +137% -> $BE: +123% -> $LITE: +113% -> $CRWD: +100% -> $ARM: +99% -> $AAOI: +91% -> $NBIS: +84% Ok doomers

$GOOGL is now discussing deals to extend their memory LTAs through 2035 from KeyBanc channel checks. Samsung already has 70% of their capacity allocated through 2031, and this move would suggest a more durable cycle for $MU, $SKHY, and the others. They also expect HBM prices up 80% to 100% in the Q1 2027. Having all your capacity potentially allocated out into 2035 via LTAs would ease a lot of memory durability fears.

This is the bear thesis. Costs of providing compute are sky-rocketing (chips, power), while the output prices (tokens) are hyper-deflating Rising input costs and collapsing output prices is what kills every debt-fueled capex cycle

We’ve added DeepSeek V4.1 Flash to AA-AgentPerf, with results for NVIDIA and AMD hardware. At 100 tokens/s per user, NVIDIA GB300 is in first place with throughput up to 261M tokens/s per MW. AA-AgentPerf is our agentic inference performance benchmark. It replays real agentic coding sessions, including prompt caching and tool-call delays, to give a realistic measure of inference performance in day-to-day agentic workloads. We measure total system throughput per megawatt of runtime power, and per-agent output speed at varying concurrency levels, to show the trade-off between system utilization and individual user experience. DeepSeek V4.1 Flash joins the leaderboard today. It brings the leaderboard to four featured models, joining DeepSeek V4 Pro 0813, Kimi K3, and GLM-5.2/5.3. We are evolving the featured models as popular new open-weights models release, and will feature day-0 results for major future releases. Background on DeepSeek v4.1 Flash: ➤ It scores 39 on the Artificial Analysis Intelligence Index, ahead of DeepSeek’s earlier V4 Pro 0813 at 36, with about a third of the parameters (552B ‘backbone’ and 16B active, against 1.6T and 49B) ➤ It is built on a new architecture, with a causal encoder-decoder design that activates 8B parameters per input token and 16B per output token. It uses a further 196B parameters of Engram n-gram memory that can sit in host memory rather than on the accelerator, and compressed sparse attention that needs about a quarter of even the earlier V4 Flash model’s KV cache per token ➤ This makes it much more efficient to serve, since the weights fit on fewer GPUs and the smaller KV cache leaves room for more concurrent agents per GPU Initial AA-AgentPerf results for DeepSeek V4.1 Flash: ➤ V4.1 Flash runs on as few as 2 GPUs, so rack-scale systems get less advantage over 8-GPU single nodes like B300, which stays within about 20% of rack-scale GB300 NVL72 across the curve, and leads at both ends ➤ At 100 tok/s per user, GB300 leads with ~261M tokens/s per MW, ahead of B300 (~220M), B200 (~193M), GB200 (~178M) and MI355X (~128M) ➤ Every Blackwell system and MI355X can reach around 600 to 670 tok/s output speed for a single agent (ie. at batch size of one), against 372 to 392 tok/s on H100 and H200 ➤ V4.1 Flash is both more intelligent than V4 Pro 0813 and much cheaper to serve, yielding ~1.3x (GB300) to ~6.2x (B200) the throughput per MW at 100 tok/s per user compared to its larger cousin

1) Trying again. 
TLDR: Everyone can win. Open-source, frontier labs, infrastructure, applications and most importantly the American people.   I understand all of the jitters about Anthropic losing share, real rates, spreads and second derivatives. Still think the most likely future outcome is a hybrid, multi-model future where almost everyone wins, open-weight tokens are likely the overwhelming majority of tokens, the dominant frontier model companies still do well albeit perhaps not the outcome that seemed possible in May and AI infrastructure/applications benefit immensely from the increased fragmentation, competition and lower margins at the model layer.   Lower margins at the model layer mechanically drive higher compute demand as long as someone is willing to pay for the training. And Jensen is obviously willing to be the “trainer of last resort.” As below-market compute contracts signed in 24/25 roll-off and reprice higher, cloud and neocloud revenue should continue to accelerate which will help address financing fears; I think we may be near peak external financing for this buildout. Noteworthy that using data from 3Fourteen Research, B200 clusters will generate a 37% IRR *if* their rental price declines 40% per year. Prices are still going up, but that IRR math is too ridiculous to print. 37% IRRs available at scale should get funded. Re: this weekends controversy about MOUs, Anthropic’s $413 billion of real contractual commitments sounds scary. Reality is that it works out to roughly $31 billion of annual cloud spend, which is easily internally financed given their reported ARR, and $161 billion of spend at Broadcom which will be financed by Broadcom and private credit. Continue to believe fragmentation at the model layer is the best outcome for ordinary Americans and humans everywhere. As I have said before, do not trust any 5-20 individuals to make the best possible decisions about the direction of this technology. I want as many AIs as possible to maximize the odds that they encompass the diversity of human values across the world and richness of human variation. And I want an AI that shares my own personal values. Safety, freedom and the maximization of human agency through democratization, distribution and diffusion vs. centralization. History has spoken on the dangers of the latter path. Finally, from my perspective anything that slows this buildout down is net positive over the long-term as the biggest risk and worst outcome is an *actual* overbuild. We are structurally short watts, wafers and increasingly capital. That is all good. God forbid we were still in ZIRP, TSM ramped capex to 3x their current plan and we weren’t short turbine blades: there would almost certainly be an overbuild and a bubble. That would be terrible. Slower and smoother for longer is better both for investors and for the world. A terrible cyber event is the most plausible near term bear case IMHO, but AI cyberdefense ramping quickly and I was happy to hear Sholto say that cyber probably becomes defense dominant in the near future.   Longer, threaded version follows:

Goldman estimates short-term AI compute is fetching $40B to $50B per GW annually across $SPCX, $NBIS and $CRWV nearly double long term rates and ~20x what a powered data center shell earns. What makes this really interesting is Nebius captures both spot and contracted pricing while $IREN locks in longer-term revenue showing how same GW can generate really different returns depending on contract.