Tisson Mathew

CEO @ Skypoint | Replacing Administrative Labor with AI Agents | Healthcare & Senior Living | HITRUST r2 | Deloitte Fast 500 | Inc. 5000

[Paul Graham @paulg]

If big companies can't make a net return on their LLM token costs, that doesn't mean it's impossible to. In fact this is exactly what you'd expect to happen with a new technology. Incumbents can't use it well, and are replaced by upstarts who can.

[Alek Dimitriev @tensor_rotator]

In case you're wondering, yes we're feeling the AGI.

[Codez @0xCodez]

https://t.co/RSly7N44jl

[Tisson Mathew @mathewtisson]

@JayaGup10 @AdvaitRaykar Lot of slide slop and YC BS

[Jaya Gupta @JayaGup10]

Enterprise exec: “if I see one more startup try to sell me their transformation with slides generated by Claude, I’m calling up McKinsey”

DONT USE CLAUDE SLIDES TO FUNDRAISE CAPITAL OR SELL A TRANSFORMATION!

[Mike Vernal @mvernal]

https://t.co/n7pVVA3uXu

[Thariq @trq212]

been asking others at Anthropic how they stay in the loop with Claude and fully understand the work being done

this is one of my favorites from Suzanne:

[Tom Blomfield @t_blom]

Imagine replacing 90% of your employees with a team of geniuses who have no idea how your company operates.

Total chaos. Nothing works.

That’s what AI feels like today.

The missing piece is extracting all the domain knowledge from people’s heads and providing that as structured context to the models.

[Tisson Mathew @mathewtisson]

@t_blom Start with https://t.co/4UbscAwxuq

[Paul Graham @paulg]

The only thing worse than having the CEO knee-deep in building stuff with AI is not having the CEO knee-deep in building stuff with AI.

[Garry Tan @garrytan]

This is the actual bottleneck. The models are smart enough already. What is missing is the company-specific context locked in senior people heads. Whoever cracks knowledge extraction at the company level unlocks the rest.

As you work on this, please consider using GBrain as your OSS retrieval layer

https://t.co/0F5uDQzPHu

[Marc Andreessen 🇺🇸 @pmarca]

We can just say “consultant”, a word can’t hurt us.

[Aaron Epstein @aaron_epstein]

The biggest hack I’ve seen for founders to close deals faster: just show up.

Get on a plane, fly to their office, meet in person, bond with the whole team.

Instantly replaces weeks of zoom calls.

[yoni rechtman @yrechtman]

https://t.co/Wry5c5Kj5m

[Tisson Mathew @mathewtisson]

@chamath Fat skills, thin harness! GStack and Gbrain good examples .. don't need a factory per se.

[Aaron Levie @levie]

What’s happened is that we went from AI chat tools that were relatively cheap and had small context windows, to AI agents that have giant context windows, the ability to keep track of longer running work, and models that cost an order of magnitude more on inference because they’re that much better.

This has compounded far faster than most realized (unless you were paying close attention at the middle or end of last year, which many here were), and the dollars flowing in now are much more real.

What follows is a continued march of AI capability that will continue to be used by anyone with a frontier use-case (like coding, sciences, finance, consulting) and then a peeling off of tasks to lower cost models that are capable enough for the job. Whereas we thought the cost of AI might converge on a single low price per token before, it’s clear the stratification is only widening based on the task you need performed.

This will be yet another component that has to be figured out for broad AI diffusion. Enterprises will need to put in programs, new finance teams, and technology solutions to manage this all. The labs and platforms that can ensure customers can price optimize for the task at hand will be in the best position.

[Andrej Karpathy @karpathy]

Personal update: I've joined Anthropic. I think the next few years at the frontier of LLMs will be especially formative. I am very excited to join the team here and get back to R&D. I remain deeply passionate about education and plan to resume my work on it in time.

[Thariq @trq212]

a prompt I've been using a lot recently:

implement <SPEC> and while you do, keep a running implementation-notes.html file (or markdown) with decisions you had to make weren't in the spec, things you had to change, tradeoffs you had to make or anything else I should know

[Tisson Mathew @mathewtisson]

@BrendanFoody Fat skills thin harness - build apps to swap out the models

[Petra Donka @petradonka]

https://t.co/kI2B1isN4N