CCO @Anthropicai. Enterprise AI adoption at scale is what I think about most. Previously commercial leadership at ServiceNow, Salesforce, Microsoft.

New York
Joined September 2008
Paul Smith retweeted
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel. - Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all. - Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract. - Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code. - And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while. The funnel is driven by a combination of factors: 1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel. 2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel. 3. Access. Free-tier, paid-tier, internal. So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
Judging by my tl there is a growing gap in understanding of AI capability. The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code. But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along. So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions. TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
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Love this.
hung out with my very ai-pilled friend @danshipper to chat about how he built the Every agent on Claude Managed Agents. some cool tips in here on how they create benchmarks (can an agent edit copy as well as their team’s human editor?) to pull off continuous improvement
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Haiku 4.5 came out two months after I joined Anthropic. It scored 15.7% on the test for operating a computer. Haiku 5.5 is out today, less than a year on. It scores 72.4% and costs around 75% less to run. anthropic.com/claude-haiku-5…
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I mostly talk to Claude out loud, with whatever voice tool you prefer. You give more context when you speak, and you get better results.
I am surprised that people are surprised this is how I prompt Claude. Talk to Claude the way you would a coworker. There's no secret to prompting. There's no need to be overly scaffolded or prescriptive for most tasks -- give Claude a goal, and it will figure it out. Back in the Sonnet 3.5 days, your prompt mattered a lot. Nowadays, it's much more important to communicate to the model: 1. What you want it to do 2. How much effort you want it to spend 3. How it should verify that it did the right thing
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We're committing $100 million to train 10,000 engineers at our customers and partners. Every exec team I meet wants to go faster. And that often comes down to the best AI + the best talent to apply it. And that talent can be scarce. anthropic.com/news/claude-fr…
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Thanks @petergyang I kind of agree.
Opus 5.5 is: - Smart - Fast - Fun to talk to - Good at writing - Friendly on rate limits - Constantly surprising at just how much it is capable of
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Paul Smith retweeted
This was done by a fleet of 950 agents working 21 hours and using 210 million tokens. Long running agentic tasks can unlock new discoveries.
Claude has discovered a previously unknown enzyme system hidden in the DNA of bacteriophages. Beside the enzyme’s gene sits a long array of repeating DNA—a structure that looks somewhat similar to CRISPR. We don’t yet understand what this system does, but only a handful of known systems share its features, and all of them are able to cut, copy, and paste DNA. Historically, the discovery of such programmable systems has helped revolutionize medicine. CRISPR, for instance, is now the foundation of genetic medicines. But it will take much more work to learn what this system does, and whether it can be put to similar use. Read more: anthropic.com/news/claude-di…
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Thanks Aaron. Really good to see Box testing it on the messy stuff enterprises actually deal with. Better answers on far fewer tokens is what every customer I sit with is asking for.
At Box, we've been testing Opus 5.5 on a variety of complex enterprise knowledge work tasks dealing with unstructured data with the Box Agent. Overall, we saw frontier capability levels, with major performance improvements over Opus 5. 63% fewer tokens used, 42% less verbosity, and 30% faster vs. Opus 5. And the model itself is cheaper, so this is a major win for any agentic computer use, coding, analytics, or data work that enterprises will be doing. Here are some examples of the task wins and performance gains across a variety of industry tests that we performed: • Financial services - due diligence (+39% task accuracy): A year of transaction records, with the job of finding every miscalculation in an acquisition target's pricing tool. Opus 5.5 scored a perfect result on every attempt in half the words Opus 5 used, consuming 82% fewer tokens overall. • Technology - cloud cost analysis (+65% task accuracy): Work out what a company should actually change about its cloud spend. Opus 5.5 picked the right basis for the retention calculation and kept the source data's unit conventions straight all the way through, so the number at the end actually holds up. It took half the time Opus 5 took, with 70% fewer tokens. • Consumer products - client account analysis (+17% task accuracy): Set the onboarding targets for a client account, reading across the signed contract, a satisfaction tracker and a team metrics sheet. The contract never states a senior/junior split, so Opus 5.5 derived it from the 18-person roster and showed the rule it used; several clients had a perfect 10 on individual survey questions, so it averaged each client's responses instead of crowning the single 10. It finished this one in half the time, on 78% fewer tokens. • Clinical diagnostics - data analysis (+15% task accuracy): Malaria rapid-test performance across a dry and a wet season: build the patient records out of two clinical PDFs, compute positive test rates by season and gender, and test whether parasite counts really differ between test-positive and test-negative patients. Opus 5.5 caught that the two groups' standard deviations differed more than 100-fold, re-ran it the right way, and found the dry-season difference didn't hold up after all. This accuracy gain came with a final answer that was half the length of Opus 5's, and also needed 78% fewer tokens end to end. Customers will be able to build AI Agents with Opus 5.5 shortly in the Box AI Studio.
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Claude Opus 5.5 is out today. I've been using it for a couple of weeks and it's my favourite model yet for capability, tone and quality of work. We spent a lot of time with customers on cost per task, and it's 40% cheaper to run than Opus 5. It also comes with strong safeguards for cyber and bio, and it earned top marks on our alignment tests.
Introducing Claude Opus 5.5, the first model in our new Claude 5.5 family. It performs at the level of Claude Fable 5.1 for most tasks, and costs 40% less to run than Opus 5.
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It’s been a minute but back attending a Dreamforce keynote this last week and @Benioff on great form - and joking aside it’s a great alumni network you have unleashed Marc!
Claude just unblocked a deal stuck in Salesforce for 11 days. Live at #DF26. No slides. Real pipeline. In seconds, it: • Scanned the Slack thread • Checked the security review • Cross-referenced pricing history • Executed the next step under standard seller permissions That’s Claudeforce. Not AI bolted onto CRM. Claude executing inside the system the business already trusts. Trust first. Now it acts. ❤️💪
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Paul Smith retweeted
Sihao loves technology: he’s worked on rocket engines and collects chips/wafers as a hobby. He’s also one of the best people I know for thinking about where AI's headed. Very glad to have him on the team. Lots to build!
1/ Some personal news: I'll be joining @AnthropicAI as Head of Frontier Compute Strategy to work with @NotTomBrown on our infrastructure expansion, coalition building, and planning for rapid AI progress.
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Fable 5.1 is out. About 25% cheaper than Fable 5 for typical workloads, up to 45% for agentic work. The piece I care about most is Enterprise Frontier Safeguards. Your data stays on infrastructure you control. Built with more than 100 customers in regulated industries.
We’re introducing Claude Fable 5.1 and Claude Mythos 5.1. They're the world’s most advanced models for coding and knowledge work.
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Paul Smith retweeted
I’ve joined @AnthropicAI! My morning run looked a bit different today.
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Paul Smith retweeted
Cursor has been a trusted partner of Anthropic since Sonnet 3.5. We’ll continue to increase compute to support Claude models in Cursor and are excited for what comes next with them at SpaceX.
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Paul Smith retweeted
this is the most exciting boring news you can get. us nerds on twitter are unimpressed by a CRM MCP, but this isn't for us. this will change the game for enterprise GTM folks out in the world who aren't allowed to vibe code a third party CRM wrapper. enterprise employees want to have the same toys we get, and this is the monster truck of MCP toys
Marc @Benioff + @DarioAmodei. Live on @CNBCFastMoney. Watch as they talk Salesforce + @AnthropicAI, the Claudeforce partnership, and what’s next for enterprise AI.
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Paul Smith retweeted
Salesforce $CRM announces a partnership with Anthropic named Claudeforce
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I was with Marc and the Salesforce team today. We announced a strategic partnership and launched Salesforce in Claude. Your CRM data is now right there inside Claude. No more hunting for it. We're starting with sales teams. I use it every day. Tools for other teams are coming soon.
Claudeforce is here. ⚡️ The #1 AI (Claude) now runs natively on the #1 CRM (Salesforce). Through the new AIforce harness + Headless 360, Claude gets direct, governed access to Data 360, Tableau, Slack, and your entire Salesforce workflow—without ever leaving the chat. What this changes today: • Instant Grounded Intelligence — Ask complex enterprise questions and get real-time, trusted answers • App & Agent Builder — Deploy custom workflows, agents, and secure apps on the fly • Action-Oriented — Trigger live enterprise actions and get work done, not just summaries • Ironclad Governance — Zero Data Retention, full trust boundary, Salesforce-certified by default Probabilistic models alone can’t run a company. Deterministic systems alone can’t reason. Claudeforce fuses Claude’s reasoning with Salesforce’s trusted data and controls. The AI is the interface. This is how every business will run. See it at Dreamforce. #DF26
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Paul Smith retweeted
The shape of the graph is getting very familiar.
Claude Fable 5 scores very well on FrontierMath: Tiers 1–4 (v2), reaching 87% on Tiers 1–3 and 88% on Tier 4. This continues a streak of Anthropic models improving rapidly at math.
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Question from a senior member of the banking community earlier this week: "Should we be the tortoise or the hare when it comes to AI?" My answer: a cautious hare.
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Paul Smith retweeted
I've had access to Fable for a bit. A genuine jump in capability, I could feed it a 15 page design document for a project and it would work for 9+ hours and deliver terrific results. But working with it is weird & weirder is coming Lots of examples: open.substack.com/pub/oneuse…
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