building ai underwriting to finance all human ingenuity starting with farmers. ex-affirm, ex-granular. family and good quests
San Francisco, CA
Joined December 2012
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super excited to share that we were accepted into @StartX fall batch 2026. will be sharing more in the coming weeks about what we’re building at Rizal. onwards!
i don’t see how data center regulation doesn’t benefit the hyperscalers and lead to consolidation (and maybe even a push to space and regional countries like neoclouds in mexico). sad part is that trades people and rural communities are positioned to benefit handsomely from a build out that is currently larger than all of the railroads combined and still growing. think about all the schools and public works projects that could be funded that are now in jeopardy because of the over politicization and misunderstanding of one of america’s most important infrastructure build-outs since the interstate system. i’m for fair guardrails but from what i’ve seen the market (and capital markets facing these projects) has been going a decent job self-policing in recent months. reminds me of what @bgurley has shared at @theallinpod about it being a feature and not bug having dc and sf situated so far apart geographically
agree…services/outcomes and diffusion into enterprises are where we’re stacking our chips @guilleflorvs
Sequoia Capital just hosted AI Ascent and gathered 150 of the top AI minds like Demis Hassabis, Andrej Karpathy, Greg Brockman, Boris Cherny of Anthropic 🤯
And Sequoia just gave one of the clearest frameworks for where AI is going.
The core idea:
- AI is not just another software wave.
- Cloud expanded the software market.
- AI expands the services market.
That means the next giant AI companies may not sell tools, dashboards, or copilots.
They may sell the work itself.
The full breakdown:
1/12. AI is the biggest platform shift yet
Cloud took software from roughly $350B to $650B.
AI opens something much larger:
A $10T+ services market that machines can now start to address.
2/12. This wave is moving faster than cloud or mobile
Every technology wave creates billion-dollar companies.
But AI companies are reaching massive scale much faster.
What took decades in cloud may take years in AI.
3/12. This is a computation revolution, not just a communication revolution
The internet changed how information was distributed.
AI changes how information is processed.
That is a much deeper shift.
4/12. We are moving from faster horses to cars
The first AI apps made workers 10–40% more productive.
Useful, but not transformational.
The next generation creates 10–40x improvements.
5/12. Sequoia’s practical definition of AGI is simple
Forget the philosophical debate.
If a system can be dispatched to do a job, recover from failure, and keep going until the task is complete…
For business purposes, that is what matters.
6/12. The race is now for the services economy
Software companies used to sell systems of record.
AI companies can now sell outcomes.
Not “here is software to help your lawyer.”
7/12. The best moat is not the model
In AI, the underlying technology changes too fast.
Today’s breakthrough becomes tomorrow’s baseline.
So the moat is not just technical capability.
The moat is customer intimacy.
8/12. Founders should wrap around customers, not model releases
Most technical founders want to chase the latest model.
But the model layer keeps shifting.
9/12. Affordance matters more than people think
Raw model power is not enough.
A terminal, API, or blank chat box may be powerful, but most users don’t know what to do with it.
10/12. The diffusion gap is the opportunity
Frontier models improve every week.
Enterprises absorb change over months or years.
That gap is massive.
11/12. 2026 is the year of agents
2022–2024 was chat.
2024–2025 was reasoning.
2026 is agents.
12/12. Services is the new software
AI is moving from helping humans do cognitive work…
to replacing entire categories of cognitive work.
Actual work, done end-to-end.
Full breakdown here: Sequoia AI Ascent 2026: The future of AI
theaiopportunities.com/p/seq…
love this
running a company nowadays feels kinda dystopian
my team is 50+ people in 12 countries, but i talk to my agents more than my human teammates
- voice note to grokbot
- mattpocock/skills /grill-me so the agent interrogates the idea before anything gets built
- spec drops into linear through the linear mcp
- exa + firecrawl agent reads every competitor site, pricing page and changelog
- ideabrowser.com to find ideas and trends
- apify pulls g2, reddit and app store reviews
- prompt: "find the 10 complaints that show up most and quote the users"
- context7 so the agent reads the latest docs before it touches any api
the design
- claude code + paper mcp
- 4 versions of every screen before a single line of code
- pick pieces from 2, it merges them. diverge then converge
- frontend-design skill from anthropic/skills + our own design system skill
- cleanshot screenshots of any ui i like go straight in with "make it feel like this"
the build
- pingdotgg/t3code as the control surface for every agent, from my phone too
- stablyai/orca runs a fleet of agents in parallel, each in its own git worktree
- graphify maps the whole codebase into a knowledge graph so agents navigate instead of guess
- headroom compresses tool outputs before they hit the model, so tokens go 20-95% further
- lsp plugins so the agent sees types and errors like an ide does
- security guidance hook flags risky edits before they land
- supabase mcp for the database, stripe for billing
- github mcp opens the pr, vercel spins up a preview
- loops run overnight, i check what shipped over coffee
the user testing
- lightpanda as the headless browser, way lighter than chromium so you can run hundreds at once
- agents click every flow on the preview like a first-time user
- one persona is impatient, one is confused, one is on a phone
- playwright + chrome devtools mcp for regression tests
the bugs
- sentry mcp catches the error
- agent reads the stack trace and opens a fix pr in under 10 minutes
- human just approves
the data
- posthog mcp cron at 7am on yesterday's funnel
- any step that drops more than 10%, the agent watches 20 session replays and tells me why
- stripe mcp pulls every cancellation reason into one doc every monday
the agents' tools
- orgo so every agent gets its own computer
- agentmail so every agent gets its own inbox
- agentphone so i can call them from the car
- composio connect-apps for every other connector
- akitaonrails/ai-memory, a git-backed markdown wiki so they remember what we decided last week
the skills
- wshobson/agents so skills work across claude code, codex and opencode
- my writing voice
- our design system
- how we write specs
- skill creator so the agents write new skills themselves
some helpful tricks
- one agent per platform. the x agent never touches email
- cheap model for triage, opus for anything that ships
- every output gets scored 1-10 by an llm judge. under 8 goes back
- every agent writes a 5-line log at night so i can read the whole company in 2 minutes
- every agent mistake becomes a new skill, so it only happens once
the team
- humans get version 5, not my voice note which they are happy about lol
- their job is creative feedback, relationships and telling the agents what they got wrong
a human pm reviews the funnel once a week. this reviews it every morning
running a company is now voice notes and inference
the future is coming fast
Imagine if everyone had the tools to shape intelligence itself. That day is today.
Today we launch the Trajectory platform - the easiest and most intuitive tool to post-train models of your own. The foundation for continual learning.
With just a few agent prompts, you get access to a full creative suite to train, eval, and deploy trillion parameter models.
On top of that, we believe in a future where you don’t just shape your model once, it also keeps learning on its own from the world around it.
We’re releasing two research previews.
1. Continual grading scales your taste, so your model learns what good looks like from far more than you could ever grade by hand.
2. GS1, a new algorithm that lets models learn directly from what they do in production.
We’ve now raised 60M, with a new round led by @Sequoia at a 300M valuation to democratize the most powerful technology of today - training models - so that the lawyers, doctors, and engineers of the world can own their own intelligence.
You know what good looks like, now go build it. Own your own value function with Trajectory.
well said. with great power comes great responsibility. seriously, for your team with you in the tenches
let’s go! @bengusberg
Daniel Tebes retweeted
Weekend hack: HackerTube. I was annoyed with YouTube's recommendations so I had Claude filter down 43M comments over 20 years of HN to 6,454 videos that keep being recommended. *Significantly* better viewing experience, ~2 hours with Opus 5.5:
hackertube.org/
- Excellent recs without any logins (state is kept in local browser session)
- Thumbnail is a representative frame from middle of the video instead of the super duper A/B test hyper-optimized user uploads
- Underlying data from the comment analysis as one zip file, point your Claude at it and ask for your own version
- HackerTube Longs ✊
A bit more on the process:
I use Claude Code (desktop app for Mac), Opus 5.5 on Medium effort as my default. I only talk to Claude and have it do everything, the only "manual work" I helped with in tandem was in the browser (reminiscent of MenuGen): 1) I had to create some kind of API key from Google Cloud UI to query YouTube for video metadata, 2) I booted up a VPS box for serving and 3) I bought the domain name. Once the box was up I gave Claude the IP and told it update, secure, harden, deploy and optimize the code, and it did the whole thing.
Many awesome projects that would have normally taken weeks/months even can now be few-shotted for breakfast. What else are we not thinking of.
when your toddler knows how to blow his nose into a tissue and pretends to so he can save his snot for his mouth when you’re not looking 🤦
yup we’ve been building with graphs for 6+ months now, it’s the norm and pretty incredible what you can build in a few runs.
graphs do require a strong acceptance criteria, agent ledger and qa process…without these things it’s garbage in and out
This is f*cking insane. Anthropic Research Lead:
"Prompting is basically over
99% of our engineers already run swarms of 300+ self-improving agents. Now everyone is shifting to graphs"
In just 20 minutes, she explains how graph engineering turns separate agents into a system that improves itself
One Agent → Loops → Plan Mode → Graphs → Self-Improving System
Prompting was the old workflow. The graph around the model is the new one
Worth more than most $1,000 agent engineering courses
Bookmark and watch it today
Then read the article below ↓
agreed this is our playbook and it’s working @gregisenberg
more wild shit
today, we introduce a little mischief to the world.
for four years, we’ve been brewing real brain cells on electrodes to understand how they compute. now, we’ve turned those discoveries into a video model anyone can use.
from 0.5¢ a second, mischief 1 is at least 5x faster than its base and the cheapest image-to-video model on @fal and @OpenRouter. soon to be on @awscloud marketplace.
it’s not a delusion anymore. biocompute is real, commercial and this is just the beginning.
happy halloween.
well said @levie and I even see it among sf startups and my founder friends, those of us that bet long on AI, building with agents instead of hiring eng teams and taking a copilot approach have entirely different startups today. Tbf building entirely with agent teams at the time (around sonnet 4.5 release) while flying the plane in a highly regulated industry like fintech introduces a ton of challenges (black box design, probabilistic bugs etc) and pain that only the best engineering minds can figure out…. we’re super lucky to have @hichilo_ @luislascurain edson and @AlvaroCreixell at @rizalaihq
It’s almost incomprehensible how wide the distance is between someone who’s working at the frontier with AI and basically everyone else right now.
The crazy thing is these lines are not converging, but only growing farther apart. If the curve of AI progress had leveled off, it would certainly have given most people and companies time to catch up or implement their workflows in a standardized way. The technology would have neutralized competitive advantage because everyone would be using it the same way.
But what’s happened is the opposite. The curve of AI progress is basically going vertical right now, which means if you had codified your processes 3-6 months ago you’re likely already behind again. Now imagine most companies that kicked into gear a year ago and thought that they were done. Even further behind now.
I’ve never seen a technology in my life and career that improves at this rate, which consequently means you need to upgrade your expectations at a similar rate. Our brains weren’t wired for this, and our organizational change management processes certainly weren’t either.
With any form of recursive self improvement, things just get wilder. It’s a time when you will need to be constantly reevaluating the things that didn’t work just a couple months ago to see if they now can be solved. AI native companies certainly have a strong advantage due to this right now, so now it comes down to can the bigger companies keep up. Wild times.
