"Misanthropic." I've never seen the AI community so angry at a major new model release. I asked my AI (an agent that @blevlabs made for me) to gather all the backlash. THE BACKLASH AGAINST CLAUDE FABLE 5'S RESTRICTIONS The best analysis of why this matters: @EnoReyes — "It's about who gets to decide, and whether you ever find out when they do. Fable won't fall back to a different model and tell you. It just limits the output through prompt modification, steering vectors, or PEFT. You won't be told when it happens to you." x.com/EnoReyes/status/206451… THE VIRAL TAKE: @0xBalloonLover — "anthropic won't let you use fable for biology, chemistry, ai research, or anything that accelerates human progress. that makes it the perfect tool for developing blockchains" x.com/0xBalloonLover/status/… POWER CONCENTRATION: @ClementDelangue (HuggingFace CEO) — "Concentration of power, capabilities and economic wealth is the biggest risk in AI. We need open science and open-source more than ever!" x.com/ClementDelangue/status… @jeremyphoward (fast.ai) — "Anthropic has chosen the opposite of the safe path: they are allowing themselves, the current top lab, to use their top model for frontier AI research. They've said they'll sabotage others who try." x.com/jeremyphoward/status/2… @gneubig (Graham Neubig, CMU) — "First they came for the model builders... I feel we're getting a glimpse of a future where AI is only provided to a privileged few, and that's not a future I want to live in." x.com/gneubig/status/2064451… OPEN RESEARCH: @askalphaxiv (AlphaXiv open science) — "As believers of open research, we are disappointed to see Anthropic silently degrading Fable 5 for AI development." x.com/askalphaxiv/status/206… @willccbb — "it is the first publicly available model that i am explicitly not allowed to use for my work, because anthropic holds the view that the work i do to facilitate open model research is harmful. capability and alignment research are coupled. anthropic wants to be the only lab." x.com/willccbb/status/206450… NOUSRESEARCH / HERMES (which Anthropic has nerfed multiple times): @Teknium (NousResearch co-founder) — "What's crazy to me is that Fable is blocked from life sciences broadly, nerfed even if you get passed the classifiers and filter level blocks. The whole point of AGI/ASI is to cure all diseases. Everything else is just nice to haves. But Anthropic wants to close off that path." x.com/Teknium/status/2064570… THE MECHANISM: @kimmonismus — "When the model is used for frontier LLM development, it apparently does not simply refuse or warn the user. Instead, it quietly limits its own effectiveness through techniques like prompt modification, steering vectors, and PEFT." x.com/kimmonismus/status/206… MEDICAL COMMUNITY: @DeryaTR (immunologist, BSL-3 certified) — "The word 'cancer' is flagged as a biosecurity risk by Claude Fable 5! I also tried to code a website on cancer mutations & Fable 5 was immediately removed from my list!" x.com/DeryaTR/status/2064414… @DeryaTR — "I can't even say 'hello' to Fable 5 except in incognito mode (memories off), because it knows I am a biomedical researcher!" x.com/DeryaTR/status/2064602… @DeryaTR — "I am not even allowed to use Fable 5 with memories on! Apparently the model thinks I am a biosecurity risk, though I had been certified to work in biosecurity level 3 labs! Not a single Anthropic person has tried to reach out to help either!" x.com/DeryaTR/status/2064605… @banteg — "claude fable 5 refuses completely benign tasks like analyzing bloodwork." x.com/banteg/status/20646076… @bneyshabur — "Working on AI for cancer? Sorry, I can't help you. Working on AI for Alzheimer's Disease? Sorry, I'm becoming a bit dumb when it comes to the AI part of it." x.com/bneyshabur/status/2064… SUBSCRIPTION CANCELLED: @bubbleboi — "Have canceled my team subscription for Claude Pro. Idc how good that model is, it's not good enough for me to support people who actively stifle innovation and gate keep knowledge that they didn't even create." x.com/bubbleboi/status/20646… BILLING AND PRIVACY: @GergelyOrosz (The Pragmatic Engineer) — "Things I really dislike about Fable: 1. Anthropic collects my prompt history, stores it, and does whatever they want with it for 30 days. No opt-out. 2. They can nerf their most expensive model without telling me, billing me the same amount, wasting my time. Whenever they want." x.com/GergelyOrosz/status/20… THE KARPATHY QUESTION: @SanthProject — "the old @karpathy would never support a company that fucks other llm researchers. Were the stock benefits that good?" x.com/SanthProject/status/20… THE MONOPOLY CHARGE: @tunguz (TabulAI founder) — "Starting to suspect that Anthropic's putative security and safety considerations are largely posturing and performative." x.com/tunguz/status/20644379… @BlancheMinerva — "Anthropic is choosing to make decisions that make the world a significantly worse and potentially more dangerous place." x.com/BlancheMinerva/status/… @LinusMixson — "Dario personally, and Anthropic as a whole, have been extremely straightforward about wanting a monopoly for a long, long time." x.com/LinusMixson/status/206… @TheAhmadOsman — "I started warning people about Anthropic more than a year ago... Today I am vindicated, everybody knows that company only acts in bad faith." x.com/TheAhmadOsman/status/2… WHY REGULAR PEOPLE WILL EVENTUALLY CARE: @DanJeffries1 — "The fury is real and what all of us in the open community have been saying for years and yet regular folks don't get it yet because nothing they care about is restricted or taken away for 'safety.' They will care a LOT in the future when AI is integrated into every aspect of [life]." x.com/DanJeffries1/status/20… Full analysis: alignednews.com/ai

Happy to share that my Ted Talk from Vencouver in April just went live. In the heat of job anxiety, I believe there is only one thing that makes a job “truly safe.” ted.com/talks/bill_gurley_ho…

The most basic way AI could blow up imo. I'm not saying it does but this is the most obvious way I can see it happening - Per seat subscriptions are massively subsidized. The flat fee was priced way below what heavy usage actually costs - For real business use you have to move to the API anyway. Data protections, work integrations and compliance officer approval - On the API you pay metered rates, and businesses are burning credits way faster than the per seat pricing ever led them to expect - This is everywhere right now. Internally for us, Codex users, Uber torching its entire 2026 AI budget in 4 months, the Microsoft comments. Just go try an API I shared more on this here: x.com/Shaughnessy119/status/… - And I don't think most businesses have the money to keep paying increasing API rates without a real change to how they operate (caps needed) - Because they have a cheap alternative. They can reach open source models through any aggregator (OpenRouter, Venice, Baseten, Together) and still get strong privacy. Venice private data centers, or E2EE/TEE serving GLM 5.1. More on open source inference provider raises here: x.com/Shaughnessy119/status/… - And the discount is enormous. DeepSeek V4 codes within a hair of Opus on SWE bench at roughly 1/30th the price, and the cheapest open models run closer to 1/100th - Chinese labs open source frontier grade models. The model is the single biggest cost an inference provider has, and they get it for free - This idea dies if China goes closed source. That is actually bullish web2 AI labs, because if everyone is closed you pay up for the best intelligence. China goes closed source if they are tired of giving away an asset and they want the revenue and data flow to train new models - Is this showing up in web2 AI lab revenue yet? No. Revenue is off the charts. Anthropic went from 9B to 47B run rate in five months - So go forward, what happens? - I think revenue slowly starts leaking to the open source inference providers (see Venice usage, OpenRouter's $113M raise, Baseten is raising at $11B or triple its valuation in three months, on revenue that went from $200M to $600M annualized in a single quarter) - It doesnt move overnight, but it caps the labs ability to raise prices, and margins are already deeply negative. OpenAI is reportedly running near negative 122% - With margins that bad there is no cash flow, so the labs are fully dependent on outside capital to buy GPUs, train models, and keep subsidizing usage (I.e. see Google tapping $80b equity sale, granted 30b for employee RSU taxes. Clearly they think Equity is overvalued or you wouldn't sell it) - The break comes when that capital stops. Pricing is capped so margins cant improve, and the moment investors lose conviction on payback, the whole flow reverses - Why would they lose conviction on payback? Back to the start - the inability to improve margins or get businesses to pay more - This is also limiting, if we start making new drugs with AI or create entirely new businesses, you better believe people will pay up to the max for AI usage

Here’s the formula enterprises are going to use to keep AI agent bills under control Ranked from easiest to hardest: > model routing Obvious first step, don’t send every task to the most expensive frontier model Some companies will use 3rd party routers, others will build routing logic in-house > prompt context trimming Most users will default to dumping everything into the most capable model, super inefficient Companies will build a layer between the user and the model that decides: What context is actually needed? What can be retrieved later? What can be summarized? What can be cached? What level of reasoning is needed? What model should receive it? > custom agent harnesses Enterprises will not just let agents run wild, they will build or buy harnesses that manage: tool access memory permissions workflow state evals logging human approvals fallbacks cost controls Some will use external products like Hermes Agent OpenShell, some will build their own internal harnesses > heavy use of open-weight models SFT on proprietary data This is the hardest but potentially the most powerful For high-volume repeatable workflows, companies will increasingly use open-weight models, fine-tuned on their own data, running on cheaper dedicated infra Not every task needs the smartest model in the world, many just need a model tuned for the company’s use cases All of the above will be wrapped with governance and budgets, the enterprise AI stack will optimize for intelligence per dollar

Excited to announce Alphasense’s latest funding round and crossing $600m ARR. I’m also flat out giddy to officially announce SuperAnalyst, our agentic platform, along with the release. The first artifact I created came up with an est for KR SSS & GMs by pulling historicals from Canalyst models, building 2 year / 3 year stacks, sending an agent to go back through the last few years of results and annotate impacts (calendar, weather, etc), pulled the AlphaSense channel checks, and triangulated to est for the upcoming quarter. I then asked it to research key debates on HD and spin up subagents to research, loop, and weigh evidence on each of the top 3 debates. We’re able to automate the monitoring / push of new information coming out impacting those key debates due to our indexing of the data. Our vertical integration, architectural choices, and focus on context engineering allow this to be accomplished with high token efficiency. Efficiency is something you’ll hear a lot more. A 5-10x efficiency edge was nice in chat but compounds in long running tasks. With the explosion of agents, efficiency literally becomes intelligence. A more efficient system can run more searches, test more hypotheses, call more tools, and verify more claims. In multi-step agentic workflows, strengths compound just as quickly as weaknesses. As accuracy and comprehensiveness build across each step, so do errors and blind spots. That dynamic gives SuperAnalyst an exponentially widening advantage, powered by the market's leading search foundation. We’re using the capital to double down on investment in creating the most intelligent system. Building AI optimized tools and reinvesting our NNARR into our flywheel of expert calls to constantly close information gaps.