Our internal data shows Claude is accelerating AI development—a possible path to recursive self-improvement, or AI autonomously building a more capable successor. It’s happening faster than we thought, and the implications deserve greater attention. anthropic.com/institute/recu…

To get good animations from an AI you need to get good at telling it what you want: - "stagger this list of items" - "make this animation direction-aware" - "spacial consistency", "crossfade", "layout animation", I made a motion vocabulary for this: animations.dev/vocabulary

🆕The Friction is Your Judgment youtube.com/watch?v=_Zcw_sVF… @mitsuhiko (creator of Flask) and Cristina Poncela Cubeiro (AI-native Engineer) break down what goes WRONG when you "ship without friction" — listing many areas of agentic coding where you are enticed to turn your brain OFF when you most need to turn it ON.

6/ Give Claude a way to verify its work Finally, make sure Claude has a way to verify its work. This has always been a way to 2-3x what you get out of Claude, and with 4.7 it's more important than ever. Verification looks different depending on the task. For backend work, make sure Claude knows how to start up your server/service to test it end to end; for frontend work, use the Claude Chromium extension to give Claude a way to control your browser; for desktop apps, use computer use. Personally, many of my prompts these days look like "Claude do blah blah /go". /go is a skill that has Claude 1. Test itself end to end using bash, browser, or computer use 2. Run the /simplify skill 3. Put up a PR For long running work, verification is important because that way when you come back to a task, you know the code works.

Every day for the next long while, I'm going to tear down a new public software company and highlight the AI risks/opportunities around it- products launched to date, top startups, key quotes from earnings calls, etc. Day eighteen: Snowflake $SNOW Peak share price: $392.15 (Nov 19, 2021) Share price today: $121.11 (-69%) EV today: $39.8bn ARR today: $5.1bn ( 30% Y/y) NRR: 125% EV/ARR: 7.8x GAAP Operating Margin: -25% (!!) EV/Run-rate GAAP EBIT: N/A Headcount: 9060 ( 16% Y/y) What Snowflake does: Snowflake is the leading cloud data warehouse focused on helping companies store, manage and query tabular business data using SQL. A significant share of the world's largest enterprises have opted to pool their critical data onto/around Snowflake to create a data warehouse of record to power everything from observability to analytics to data applications. The key innovation powering Snowflake's rise was the separation of compute and storage as concepts, allowing users to apply elastic compute against fixed storage, reducing analytical queries that used to take hours to seconds. Like others in the space, Snowflake has expanded into other adjacent areas like python, ETL, BI, etc. AI bear case: The AI bear case for Snowflake revolves around differences in human vs. agent preferences for accessing data and the continued march of infrastructure that prices to one paradigm becoming obsolete as the world advances. In particular, while Snowflake's query engine works very well at human speeds (loading a dashboard, running a complex SQL query) upstarts like @ClickHouseDB and @motherduck argue that agents have very different preferences and prefer lightning fast queries that would be very expensive on Snowflake. In short, the bear case on Snowflake is that analytical queries will be run by agents in the future, and Snowflake's platform has an architectural innovator's dilemma in serving those use cases. AI bull case: The reality is, thousands of the world's largest companies have invested huge effort in standardizing/centralizing on Snowflake. The battle to be the system of record for aggregated tabular business data is already over at these companies- it will be Snowflake for the foreseeable future. The implication is that agents are actually a huge tailwind for Snowflake- they will need to access business data to operate, to derive insights, to understand context, etc. and Snowflake's business model has the clear advantage of letting it monetize those queries as if they were coming from a human. AI traction: It is hard for Snowflake to know exactly what share of its revenue comes from AI-driven queries, but it did say this on the Q4 call: "This quarter, we delivered the largest sequential increase in accounts using AI, bringing the total to more than 9,100 accounts." Beyond that, net retention ticked up last year to 125%, very impressive at this scale. Adjacent AI-native startup summary: Databricks, albeit not AI-native, is the juggernaut to watch here, with a reported 15,000 employees up 34% Y/y. Clickhouse - 536 employees, 86% y/y Motherduck - 133 employees, 46% Y/y Management Quotes: "And in just 3 months, Snowflake Intelligence has scaled from a nascent offering to an essential capability for over 2,500 accounts, almost doubling quarter-over-quarter." "Our deepened partnership with Anthropic is already helping customers like Intercom see significant impact." "And Matt, just to emphasize that point, just in fourth quarter, we saw a lot of benefit with AI that we had a small reduction in force and about 200 people in the company were impacted. So if you look at our fourth quarter net adds on a headcount basis, we only added 37 people. So AI has really changed the framework for investing in growth. It's no longer tied to headcount." "So we will be launching features like a per user cap on top of Snowflake Intelligence, so they can feel like there is a clear upper limit to how much they can get charged with an agent. We think models like this that are consumption-based with clear user caps and account caps offer the best of both worlds, which is consumption pricing with price predictability." "Yes. Super quickly, like partners, customers and our internal field are all incredibly excited about the results we're seeing with Cortex Code. The original value prop of Snowflake, which is change what's possible in terms of ease of use, it's just gone like 10x with Cortex Code. We showcased a number of instances where people are building pipelines faster, transformation faster, insights faster. And I think we're only at the beginning of what is possible." Commentary: Though the balance of evidence (and certainly my customer work) suggests that Snowflake should be a beneficiary of AI, it is certainly striking that the business impact seems to have been muted thus far. All of the ingredients are there- consumption-based pricing, AI lowering the barriers for humans to ask questions of data (aka AI-generated SQL), and data as a key foundational layer to agents. My suspicion is that some of this disappointment to-date may come from Snowflake's lack of alignment to the use-case where AI is working the best today (i.e. code). Analytical queries may simply be slower/harder to get right- but it certainly seems likely that in a future where agents accelerate the amount of knowledge work done in the enterprise, Snowflake's core business should see a meaningful tailwind. Once that question is answered, the burning question will be whether agent adoption presages an architectural shift towards data warehouses with a more AI-native architecture. My gut is that this will happen at some scale but won't create a wholesale shift and lead to a data warehouse replacement cycle. It will certainly be interesting to watch, though!

Today is a big day! We're launching a ~ new ~ version of Claude Code in the desktop app. It's been redesigned from the ground up for parallel work and is a lot faster. It's been my main way to use Claude Code for the last few weeks.