What is Content A Proxy For?
From @sonyatweetybird
"In the coding domain, using AI is not only accepted, its embraced...using AI to produce content, though, there is an allergic reaction to it...I'm curious what you think about work slop (ex. slide decks entirely written by ai)..." -
From @levie
"On acceptability...other than top engineers we hang out with, who have deep taste in code (take that group aside) for most of the world code is a utility, you are just trying to automate something, you are just trying to put an interface up that someone presses a button and moves to the next step"
"..at the end of the day, we'll use the term slop for a while because there's taste involved with code (ex. handling bug vulnerabilities before shipping)...but if you can tell an agent go generate my back end and front end system, that is not only acceptable its preferable, because that was the thing that was blocking us from moving forward so we need to go do that..."
"at least the way society functions, the way the world works, and our brains work at the moment...there's still this association which is I'm trying to decide if i can trust that preson to go and execute on that thing, deliver that result, or understand that topic... when you see work slop you're like i'm losing my ability to know for a fact how much of the process was them versus how much was the AI, how much do I care about that...it might be a thing tha tas a society we have to keep cranking through and end up on the other side"
"...obviously you don't care when you see somebody's financial model... you did not personally compute all of that, but you're showing it to me and we're talking about it, so why can't the same exist for a strategy deck...right now we're going through this evolution of what is the person's role, what is the content a proxy for? how much that person knows? how much they can go and execute on? we're in a messy period where we have to figure that out"
Well.. @levie and I filmed this episode of Training Data a week or two ago, when the “current thing” was Doug Leone’s novacaine root canals instead of pacing the frontier… Simpler times!
But Aaron’s advice on reinventing yourself and your company for AI is timeless.
Aaron founded @Box 20 years ago. It sits on hundreds of billions of enterprise files, and he's bet the company on agents that can read every one of them. He's also one of the most wired-in people in AI, on every cap table and, by his own admission, 95% Twitter-educated. He’s the rare CEO who can straddle both the internet AND has the ear of CIOs.
His core argument: (1) the gap between what a model can do and what an enterprise workflow actually needs is vast, and closing it is a lot of software; (2) diffusion of AI outside of coding will take far longer than Silicon Valley thinks, and that slowness is exactly where the applied layer's value comes from.
The conversation covers:
— why application companies are the hottest neolabs, and why the LLM-wrapper thesis is finally working
— the fox-guarding-the-henhouse problem with letting model providers route your tokens
— work slop, and why we accept AI-written code but flinch at AI-written decks
— how Box built its agentic harness and why it beats raw API access on accuracy and latency
— the open-weights paradox: closed labs and open models both growing exponentially at once
— what continual learning has to solve before it works for a lawyer with five matters and a Chinese wall
— why 90% of enterprise tokens in five years will come from tasks no human kicked off
— the mandate for founders right now: whoever gets it to the customer wins
0:00 – Introduction
1:55 – Are application companies the hottest neolabs?
6:56 – Will the labs move up the stack?
12:34 – Box and betting the company on AI
16:50 – Hero use cases: reading a million contracts and long-running agents
18:42 – Work slop: why AI code is embraced but AI content isn't
24:08 – Building Box's agentic harness and the evals that matter
27:23 – The state of the model race
29:25 – Open-weight model adoption in the enterprise
32:34 – Memory, continual learning, and what belongs in the weights
37:29 – Box Labs and systems of record in a world of agents
44:55 – Will chat be the dominant UI for enterprise AI?
48:00 – Why coding diffused fast and the rest of knowledge work hasn't
54:31 – Staying wired in, making a company AI-first, and what it takes to win
Figma CEO Dylan Field: AI Slop Is Creating a Trust Problem at Work
“I'm already hearing from so many like, ‘Ugh, I got another deck made by AI today. And I don't even know if the person who made it, like, I don't know if they read it. Do they even know what's in their deck?’
And at Figma, we actually made it so that, it was just a simple thing, but we said, ‘Hey, if you are using AI in your writing of any kind, just put at the top ‘Written with AI.’
And it makes it so that we can all trust that when we see something, we know that it's been cared for enough that it's worth your time to process.
And I think that we're gonna see some bifurcation here around trust, and one way to get trust is to do something with your hands.”
$FIG
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Standout takes require standout fits.
@JensenHuang has the black leather jacket
@levie has the black bomber jacket
@alexandr_wang has the retro animal graphic t shirt
@CompleteSkeptic has the neon pink fur coat
I can't believe really think this
Have you used Opus 5.5? Astra?
Maybe these people are on the free or cheap plans and haven't used top frontier models and simply do not know how great they are?
Remember when majority of the population thought AI was dumb because they'd used ChatGPT 3 once in 2023 and it hallucinated and then just concluded AI was dumb for years until trying it again in 2025 or so
No hay nada que me diga que esto es cierto.
Hoy se manda muchas cagadas cualquier modelo.
Entonces no se la verdad si se quieren sumar a una movida o yo estoy haciendo algo muy diferente.
No se ni que significa que el software esté muerto. Todo lo contrario si un modelo puede ser un equipo entero de ingenieros debería todos estar haciendo lo que necesitan super personalizable. Lo cual no siempre es bueno.
Lance retweeted
Biggest lesson from Jensen Huang at Nvidia
"Jensen called me at midnight to unpack a challenge, his SLAs are insane, he responds that fast.
The lesson was your job is to fall in love with whatever the job is.
You have to find a way to bend your DNA to love what the job requires." @ShivdevRao
What are your biggest lessons from Jensen @NicolaiTang1 @sama @elonmusk @nbt
I have interviewed 1,000s of the world's best founders over the past decade. Few have impressed me like @ShivdevRao at @AbridgeHQ.
He navigated a brutal 5-year wilderness before exploding into one of the most dominant forces in vertical AI.
Today, Abridge is a $5.3BN powerhouse.
I sat down with Shiv to unpack exactly how he did it and condensed my notes below:
🚀 6 Lessons on Building a $5.3B Vertical AI Juggernaut
1. Survive Long Enough for Market Timing to Catch Up:
Abridge spent 5 years in the "wilderness" before hitting a tidal wave of adoption. When you have an absolute true north thesis, your primary job in the early days is simple: stay standing and don’t die. You must be alive when the sky finally opens up.
2. Pivot the Product, Never the Core Thesis:
Shiv was willing to pivot on features, go-to-market strategies, and business models. But he refused to budge on his core thesis that healthcare is ultimately powered by the spoken human signal. Die on the hill of your thesis; adapt everything else.
3. Target the Concentration of Scale Early:
A massive trap for healthcare and enterprise founders is staying down-market too long for "fast feedback loops". In the US, the vast majority of clinicians are concentrated within large, integrated delivery networks. Time your "YOLO shot" to go up-market the moment the market inflects.
Single biggest advice to founders on when to go up market @bhalligan @dharmesh?
4. Own Your Stack to Protect Your P&L and UX:
While many AI startups rely entirely on frontier systems, 40% of Abridge's model outputs are generated by in-house models. Milliseconds matter in high-stakes enterprise workflows. Building your own models gives you insane performance gains, lower latency, and ultimate control over your P&L.
When should you vs should you not build your own model @matanSF @MaxJunestrand @antonosika?
5. Don't Fight Foundation Models—Counter-Position Instead
If you try to fight the frontier model giants directly, you've already lost. You win by going millions of miles deep into regulated industries with proprietary datasets and workflows they can't easily replicate. Find ways to coexist and leverage their tailwinds.
Reminds me of what @bradlightcap said on his 20VC.
6. Move Toward the "Flat Company" Era:
With the explosion of AI agents and advanced tooling, the traditional management layer is compressing. Shiv’s latest idealistic shift is building a hyper-flat organization: fewer managers, and highly leverageable "Super ICs" who can move in lockstep and cover massive surface area.
(link in comments)
Day Three (just finished):
tickmarks.app/watch
Day Two:
tickmarks.app/watch
Day Two:
tickmarks.app/watch
First run at this. Actually pleasantly surprised with how high I was able to get the production value on the first go-round on my own.
I've committed to at least once a week on Wednesdays but given that I got the process down, I may be able to do more a week.
One thing I noticed I need to figure out because I'm still having trouble:
1. Configuring OBS to capture audio properly
2. Scene configuration to make for easy navigation
Everything else worked really smooth.
Going to try to fix these today and if its easy I'll be back soon for another live stream
Lance retweeted
Don Toliver says he’s “levelling up” on his new album ‘NITROUS’ 🚨
“OCTANE was the gasoline I was using to drive the car all year … Now it’s time to take it to a whole other level and press that button.”
Lance retweeted
What’s happening in the AI-powered reverse engineering and decompilation is absolutely insane. Pretty soon all software will be de facto open-source.
AI is coming for everything and everyone.
First run at this. Actually pleasantly surprised with how high I was able to get the production value on the first go-round on my own.
I've committed to at least once a week on Wednesdays but given that I got the process down, I may be able to do more a week.
One thing I noticed I need to figure out because I'm still having trouble:
1. Configuring OBS to capture audio properly
2. Scene configuration to make for easy navigation
Everything else worked really smooth.
Going to try to fix these today and if its easy I'll be back soon for another live stream
























