Feeding proprietary insights to the LLMs. Testing power laws. Applying the bitter lesson to AI RevOps. Rippin creative.
San Diego and San Francisco
Joined June 2008
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Generative UI for conversational agents.
Combining @aisdk with Chat SDK and 𝚓𝚜𝚘𝚗-𝚛𝚎𝚗𝚍𝚎𝚛, you can go straight from LLM to UI on platforms like Slack and Teams.
I believe this will become the dominant Human-Agent Interaction (HAI) paradigm:
vercel.com/changelog/json-re…
Good list
Google just exposed a huge SEO mistake 🤯
Exposed internal estimates for 23 crawling, indexing & search processes.
Some key numbers:
- Link signal processing: Up to 3 weeks
- Indexing after crawling: ~90 minutes
- Core update recovery: 3–6 months
- Existing page recrawls: ~30 days
- New URL discovery: ~20 hours
- Site migrations: 1–3 months
- Title changes: 1–2 days
Simply put, Google just gave marketers a better way to diagnose SEO problems before throwing more resources.
In case you’ve updated 20 pages but haven’t seen rankings move. Here’s what I’d do before rewriting anything:
1. Open Google Search Console. Use URL Inspection to check when Google last crawled your priority pages.
2. Compare crawl dates with your content update dates. If Google hasn't revisited a page, don't judge the rewrite by its rankings yet.
3. Once Google has recrawled it, compare impressions, queries & conversions against your baseline. If visibility improved but conversions didn't, investigate intent & the offer before creating more content.
Btw, I'd also maintain a simple change log: URL, update date, last crawl, indexing status & performance.
That way, you're not guessing whether an SEO change worked.
You're building a record of what Google has seen, what actually improved & where your next investment belongs.
ChatGPT is already at 20% of Google’s Search volume
I was really surprised by the scoop that @Kantrowitz had today about ChatGPT having 1/5 of the search query volume of Google. IMO.... amazing how it was easy to finally break Google's monopoly on "finding information" by offering anything slightly better. businessinsider.com/chatgpt-…
Cool new data source + partnership
Announcing: Public compensation benchmarks for the most in-demand tech roles, now available for free on LennysJobs.com 💥
Pick a role and location, and get detailed base salary and new-hire equity benchmarks, from entry-level IC to Sr. Director. Data updated monthly.
To make this happen, I’ve partnered with @pavecomp, home to the world’s largest real-time compensation dataset, which pulls directly from the HR, payroll, and equity systems of 9,000+ companies (e.g. OpenAI, Databricks, Ramp, Cursor).
This is the first time Pave has opened up this data to everyone, and I’m excited to help more people get the comp they deserve.
Find it here: lennysjobs.com/comp
Love this PostHog series
Most "Tool X vs. tool Y" comparison content is bad, because it's dishonest. Why write about why your competitors are better than you?
Because it converts. We've driven 700k pageviews and 25k+ sign ups from content about competitors.
Here's how we write ours, and what we've learned about SEO/AEO along the way:
High stakes for Google to make “the transition”
Joe Robison retweeted
unpopular opinion: ai generated designs are not slop. or even remotely close to slop.
ai raises the design floor so dramatically now that dismissing it as such is really no diff than when mathematicians got irked when ai solved decades old math problems.
if you’ve studied interaction design to any extent you obviously know that so so much of it is patterns, conventions, & adapting to established behaviors. geee, imagine what new technology is amazing at this?
you should maximize its utilization.
Joe Robison retweeted
The beginning of Spellbook was also an oddly meditative and spiritual pursuit: Our hypothesis was that if we could access raw reality directly, and disconnect from the sort of "false reality" of popular stories, then we would find alpha in the world.
In some ways the company was a higher level experiment: if we can come to see raw reality with crystal clarity, then alpha should follow. Alpha exists in the gap between popular stories, and raw reality.
We saw business as competitive philosophy. Your score is a direct measure of how good you are at uncovering truth before others.
This continues to provide a great deal of meaning for me: investing and business are a gym that ultimately enable you to build radically clear thinking.
It worked: we raised money for a legaltech company long before it was fashionable (pre-Atrium), and were the first in the world to commercially launch and LLM-based tool for lawyers.
We were making a hard bet that legal AI was needed, based on raw first principles thinking, long before any story had taken hold.
The challenge, though, is that the "raw reality" view that uncovers alpha, is a completely different from the story-telling muscle that excites customers and your team.
The hardest part of business is that you must simultaneously be aware of stories as a pollutant, while still crafting the inspiring stories that ultimately drive most people to action.
Investors/hedge funds have the luxury of only needing to be good at seeing raw reality. They don't need to market their bets. Sales and marketing has the luxury of being able to focus on the stories.
There are very few people who can simultaneously wield the power of stories, while also piercing through the sea of stories, extracting alpha from the raw reality that eludes others.
The ultimate magic is being able to do both.
Realizing how incredibly autistic our early board meetings were for Spellbook:
1. I declared the meeting a “story free zone” because I was worried that stories were too psychologically persuasive, and that they would pull us away from hard data-driven reality
2. Meetings would largely consist of scrolling through like 40 slides of charts sifting for signal. I called this “soaking in the data”
3. In the search for PMF I wanted us to think like a hedge fund making many bets. While we knew the problem we were solving I didn’t want our team to get too tied to any single narrative because I thought it would reduce agility.
4. I never allowed us to think about us as “a company”, but instead as a SWAT team searching for a company to build
5. This approach enabled us to launch over 100 different landing pages with different angles on the way to PMF
Honestly, I think it’s a really good approach pre-PMF. Too many companies get attached to a singular story too early and quit when it doesn’t work out. Startups are 3D chess. There is always a move. Slack was a game company.
However, at Series B/Series C scale you really need stories to align both your team and customers, and you can think much more longterm after PMF.
But I still believe stories are a dangerous dark magic. You can craft an incredibly persuasive story for almost any position. They’re more of a marketing/comms tool than a reasoning/understanding reality tool.
Joe Robison retweeted
What will soon become clear is that the Frontier Labs are no longer the optimal place to allocate capital. The best index portfolio for The Great Compression is instead as follows:
1) bets on pure "capabilities", super-intelligent general learning algorithms, a God Model, etc: Core Automation, Oaklab, Standard Intelligence, SSI, Flappy.
2) bets on compute/infrastructure: nvidia, spacex, etc.
3) bets on economic version control / pole position: Jai & Co (and a couple others in stealth), HoldCos, Bending Spoons, Palantir, Formation Bio, OpenEvidence etc.
The "labs" no longer give you the most convex upside in any of the salient areas. That's what the market is starting to see.
1) is mostly a hedge, if somehow there's a way for a true God Model to be born, I think it happens inside these research organizations focused exclusively on radically novel learning paradigms (not turning the economy into code).
inference / meter based businesses are poorly positioned for The Great Compression™️
once humanity reorients and the new meta (intangible economy) arrives, we will have the tools / frameworks to stop allocating tokens into the abyss
inference businesses make the underlying assumption that we’ve reached the frontier of compression in the economy - but from a systems perspective this is obviously not true as we are living inside system designs from the 1800/1900’s (locke, coase, etc.)
humanities task will be to create intangibles (useful compressions) that best describe reality into abstractions that can be hooked up to the inference meter and thus financialized using a new instrument of capital allocation (terming this thesus)
the best humans create compressions that are most inference efficient compared to past ways of modeling reality
ex: development of logarithms by napier unlocking anything bottlenecked by large demand for multiplication
new primitives for capital allocation, political economy, ai deployment, and more telemetry on the economy will arrive soon making this feasible across the whole economy
the long term market structure for meter / inference will look a lot like memory / semis (large booms / busts as we compress and uncompress)
we will be talking in terms of compression cycles soon
Joe Robison retweeted
Anthropic is now recommending that companies REMOVE unnecessary subagents from their AI systems.
This is a pretty significant shift in agent architecture, with their team finding that a single agent equipped with skills can frequently outperform complex multi-agent systems, and one customer seeing roughly 2x faster execution.
The emerging rules of thumb:
→ Skills: Teach the agent HOW to do something, including procedures, expertise, and instructions.
→ Tools / MCP: Give the agent the ability to DO things through APIs, actions, and live data.
→ Subagents: Use for parallel work, isolated context, or tasks a single agent struggles with.
The interesting part is progressive disclosure. Instead of spawning specialized agents or stuffing everything into a giant prompt, one agent discovers and loads skills and tools as needed.
Their advice is to fold existing subagents into the main loop, turn their instructions into skills, expose their actions as tools, and run evals to see if performance holds up.
As models have gotten smarter, improving agent architecture is becoming less about what you can add and more about what you can delete.
claude.com/resources/article…
Joe Robison retweeted
Google's API leak revealed the "matchedScore" attribute, the magic backlink
1) Google sees 100 suspicious links and labels then spam
2) Google sees 100 sus links + one TRUSTED link w/ similar anchor text and trusts *all of them*
Perhaps why big sites get away with spammy links
Joe Robison retweeted
I think you're right about this happening at the most advanced (or earlier adopting) businesses. At the same time, the % of companies who have done the full arc of tokenmaxxing to token questioning to token economics is still very small? And so, there's plenty companies who are just about to discover tokenmaxxing, which makes me think that overall spend will keep going higher.
AI is sparking an intellectual renaissance
