Max Altschuler

GP @GTMfund - early stage fund w/ 350+ GTM Exec LPs - backing @Armada_ai, @ollama @Get_Writer, @Owner, @TrustVanta, @paid_ai,
and more.

[Max Altschuler @HackItMax]

https://t.co/2zJImY6CC5

[Max Altschuler @HackItMax]

Last month we closed two new investments, and July has seen some amazing deal flow. It's been 5 years and I've never really felt a summer slowdown at @gtmfund.

[Max Altschuler @HackItMax]

Honored to lead the Seed round for Proaction and support the incredible team!

Trucking, transport, heavy machinery, the fleets that move everything. This is the backbone of the physical economy. It's how goods get where they're going and how a huge amount of value gets created in this country.

But for decades, fleet operators have been stuck with a bad choice: expensive service providers or fragmented, outdated software. Neither one actually gives them control.

Proaction is building the third option. An AI-native platform that handles maintenance, claims, registrations, rentals, and more, all with lower costs and far greater visibility.

They're rebuilding the entire category from the ground up with AI, and unlocking value that simply wasn't possible before.

[Max Altschuler @HackItMax]

@Husk_Talent_Ben @jasondemers5 We did indeed!

[Max Altschuler @HackItMax]

Won the 2nd flight at the Montecito Club member/guest today. Fun battle w/ @jasondemers5

[Max Altschuler @HackItMax]

@Husk_Talent_Ben @jasondemers5 T3rd in the shootout :-(

[Aaron Levie @levie]

Hosted a dinner last night with a group of IT leaders of large enterprises around agent adoption in the enterprise. Some quick notes:

* Change management remains one of the biggest topics for driving workflow transformation. Still most processes need to be upgraded to modern operating models to work with agents, which is a mix of technology, data, and human process change. Lots of emphasis on getting data (structured and unstructured) into a setup that can work with agents properly.

* IT teams are finding increasing success embedding full engineers into the business functions (essentially internal FDE) that go and implement agents into the internal workflows. There’s so much technical work to be done to make agents successful, that they can accelerate months or quarters of failed experiments by having someone technical in the workflow early.

* Consensus that the tech function is becoming more important than ever. It’s clear that the business could only expect automation to affect a minority of the business before (e.g. ERP) but now it can impact all of knowledge work. This means IT is becoming a more central role to the workflows across the company.

* Workflows are cross functional, and getting agents to work cross functionally is a complicated data modeling and permissions issue. Single users don’t have access to this. Which means you need to have agentic systems take on their own roles and have their own privileges, which is non-trivial given agents can’t keep things secure on their own.

* Huge variance in budgets between coding work and the rest of knowledge work. Some companies had a $1,000 a month budget for developers, and others had much higher amounts (like $5,000) that were merely triggers to notify the team vs. block them. Far smaller budgets for non-coding work at the moment.

* More companies are building their own multimodel systems for routing workloads by task to frontier and lower cost models. Lots of energy around open weights models, but still more in experimentation instead of at scale usage (some companies can’t due to perceived Chinese issue).

* Clear sense that all enterprise software must be headless in the future. Relief that they don’t need to train employees on hundreds of different apps. However, clear frustration with the traditional vendors that don’t play extremely nice (technically or cost wise) with agents in a headless fashion. Huge warning for existing software vendors.

* Mythos or mythos level-models are finding more and more sophsiticated security risks. The chaining together of vulnerabilities is what’s novel right now, and companies are coming up with long backlogs of what they need to go patch quickly.

Even more discussed, but just a few of the hottest topics.

[Bret Taylor @btaylor]

I deeply resent that AI has forced me to eliminate em dashes from my writing for fear of signaling slop

[The Vigilant Fox 🦊 @VigilantFox]

Tom Brady reveals the brutally honest talk he had with a Michigan sports psychologist that turned him from a benchwarmer into the GOAT.

Brady's transformation started with one change: the way he thought.

"I sit on the bench my first year, and I really had—I would call it—a lot of self-defeating attitudes and behaviors. I always had an excuse. 'Coach doesn't want me in there.'"

"I had a sports psychologist. His name was Greg Harden. I would go into his office every Tuesday, and he would say, 'Tom, I like you. You work hard, but you have a shitty attitude.'"

"'How about you start worrying about what you can control and stop talking about the other quarterbacks, stop talking about the coaches not putting you in.'"

"If they give you three reps, you do the best with the three you get. Quit bitching about you only getting three or you going in there with the backup receivers. No one cares.'"

"'You treat practice like it's a game. If you throw a touchdown in the two-minute in practice, you celebrate like it's the game.'"

Brady never approached it that way before. Then "sure enough" everything changed.

"Mmy energy started getting way better. I was bringing juice; I had the right attitude."

"Then all of a sudden, I'm bringing the juice, man. Every day, boom."

"That [mindshift] really helped me get better."

The road to 7 Super Bowl rings didn't start on the practice field; it started in that very office…

[Max Altschuler @HackItMax]

For the AI winners - building a company is starting slower, but scaling is getting faster.

It's an interesting insight from @chetanp on the @GTMnow_ podcast.

This has historically been true: getting to your first dollar is fast, and the grind is scaling from 1M to 100M ARR.
But it’s flipping. Going 1 to 100 has never been faster.

Companies are no longer releasing a wedge product, but a fully baked platform. The hard last-mile work has to happen before you launch.

Look at Legora, who spent over a year embedded inside a law firm before they had something to sell. So building lengthens and scaling compresses.

This changes two things:

1. If you're building, the quiet stretch before launch isn't you moving slow. That's the job now. Do the research, earn the insight, then let the scaling curve rip.

2. If you're investing, you're underwriting founder depth, not early traction. And once they clear that first million, the value inflection between Seed and Series A compresses fast.

In the AI era, GTM doesn't happen after PMF; it's how you get PMF in the first place.

[Manny Medina @medinism]

White-boarding live with founders and execs building some of the fastest-growing AI companies on the most important topic right now. Can't wait!

Next Tuesday, I'm back in SF bringing together leaders to tackle one of the most critical challenges every company building and selling agents is facing: AI monetization.

Thrilled to be joined by Simon from Plain and Sudhee from Battery, along with pricing experts from Simon-Kucher, for this closed-door workshop.

We'll spend the session working through the toughest monetization scenarios folks in the room are actually navigating and building practical models live.

It's invite-only.

Shoot me a DM or apply in the comments if you think you should be there.

[sarah guo @saranormous]

energy is local. stopping datacenter builds, as NY just did (rather than designing win-win requirements and incentives for local communities and builders) is going to lead to some geos having decrepit infrastructure, and some geos benefiting from grid upgrade, investment and economic revitalization.

the state needs to “plan” faster. the last time the bureaucracy said they’d study energy it led to a 30-year abject failure to build any (clean) nuclear capacity. a standard nuclear EIS takes 4 years to even get a decision

[Eric Glyman @eglyman]

a lot of valley companies inherited Google's approach to performance: don't make cuts, pay everyone generously, call it culture.

tolerating B and C work isn't kindness to your A players. it's how you lose them, and how the whole company falls short of what it could be.

[Chris Pisarski @chrispisarski]

"we could have executed more big bets if we had had a single person owning & iterating on them. Former successful founder profile with drive and speed"

+1

seems obvious but hiring former founders for early sales/GTM is very underrated

not only because of the speed (which is needed in an earlier stage, just taking more calls & closing deals faster)

but also because they are incredible at making bets and iterating very quickly

we had our first $1M month because of a bet started by an ex-YC founder turned AE (using the @crustdata MCP to power internal recruiting use cases)

he recorded personalized looms for hundreds of our customers, explained the use case and managed to expand accounts

also hired someone to build out the Claude skills for us and (with the help of our growth team) got us enough traction to justify us spending more time and resources on this new ICP

you need to hire quicker but also look out especially for entrepreneurial/ex-founder talent, they can change the trajectory of your company

[litquidity @litcapital]

The irony of using a data center to generate this image and post it on X is outstanding

[TBPN @tbpn]

Hedge funds are trying and failing to hire prediction market sharps.

"We are just getting crushed by these sharps," said Susquehanna's Jeff Yass.

@iscoe tells the story of a guy who's made 7 figures trading Rotten Tomatoes betting markets, and rejected an offer from SIG:

"He said, 'Not only am I just making a killing, but I can do things that a big institutional fund can't do.'"

"He's a Rotten Tomatoes trader. He trades how a film's going to do on Rotten Tomatoes. He's made 7 figures, easily. He's building models, scraping websites, and he's doing things that SIG, through their corporate policies, maybe wouldn't allow."

"And he asked in the interview, 'Could I do this technique?' And they said, 'Yeah, probably not.'"

"And this guy, he self-describes as a 'dips**t from the Midwest.' He's like, 'I didn't go to an Ivy League school, and I'm able to outcompete Wall Street with a $600 Lenovo laptop.'"

From his appearance on the show last month.

[Deirdre Bosa @dee_bosa]

Benchmark’s @peterfenton says 90%+ of tokens could come from open weight models in the next 18-24 months, pressuring frontier model margins.

Full convo at 12p PT/3p ET on the livestream, plus @AravSrinivas on Perplexity’s new orchestrator model and @jmorgan on why enterprises are moving toward AI models they can download and control

[TechCrunch @TechCrunch]

Popular open source AI developer tool Ollama raises $65M, grows to nearly 9M users https://t.co/Yxn26QGYq4

[Max Altschuler @HackItMax]

Big day! Incredible company from GTMfund I and III. The future is open source!

[ollama @ollama]

Big day for Ollama! When we started, open models and the open source AI ecosystem were in their early days with few believers.

Our belief in open source has never wavered.

With today's fundraising announcement and our 9M+ active builders, we’re ready to scale open models into AI that you can own.

All aboard open models!

🧵

[Max Altschuler @HackItMax]

What we're seeing across the landscape right now and some key points to pay attention to in the interview with @veelarco (Founder/GP of Premise, former Partner at NEA).

A new investor interview on @GTMnow dropped. Before we get into the conversation with Vanessa, @PaulGTM and I talked through what we're seeing across the landscape right now and some key points to pay attention to in the episode.

A few things we covered:

1. Premise is betting the whole firm on technical founders.

Vanessa and her partner anchor on backing deeply technical founders, and we debate whether that still matters in the AI age. The counter-case is interesting: if AI writes more of the code than ever, can a great recruiter just hire the technical team around them? Where it gets hard to argue is talent. The biggest bottleneck in company building right now is hiring elite AI talent, and the frontier labs are handing out multi-million-dollar packages of near-liquid equity. Having technical chops, or a network to pull from, is a real edge on day one.

2. Will OpenAI and Anthropic eat the app layer?

This is the "companies OpenAI won't kill" question. Both are now shipping vertical products directly, legal and cybersecurity plugins, and Ironclad's CEO just left to build legal at OpenAI. But there's a long history here. In the cloud era everyone asked why AWS wouldn't just build Snowflake, and the answer held: big markets with great products leave room. Customers running insurance, customs, or manufacturing aren't waiting on the next model release. They want AI that works, and that last mile stays defensible.

3. Distribution is becoming the moat.

As nearly 100% of new code becomes machine-written, the technical and product moats of the last era erode toward zero. The edge shifts to distribution, and specifically to the differential alpha of adopting new tooling before the rest of your market does. The gap between AI-native go-to-market teams and everyone else is compounding like a snowball.

[Max Altschuler @HackItMax]

Killer ep!

[GTMnow @GTMnow_]

NEW: After nearly a decade at NEA, @veelarco left to start Premise, a new fund backing the "companies OpenAI won't kill."

She breaks down why she specifically backs technical founding teams, how a two-person fund stays conviction-driven instead of drifting to consensus, why distribution is the new moat now that the old GTM playbooks are breaking, what it means to run the fund itself like a startup with CAC/LTV and an R&D budget, and why not everything OpenAI ships actually wins.

She has backed Robinhood and sat on the board through its IPO, along with a run of category-defining consumer companies.

Highlights:
00:41 Investing in "companies OpenAI won't kill"
03:09 The "just a wrapper" trap
03:52 Why she only backs technical founders
08:15 How she sources deals + what Surreal is
14:47 Conviction over consensus in a 2-person IC
19:30 The fund is a startup, the product is the fund
25:07 Distribution is the new moat
27:42 Hiring failed founders as the first GTM hire
29:16 The AWS "cloud wrapper" parallel
32:02 The GTM talent problem nobody's solving