Mayank Mathur

The pod is the new firm. Building Manthan — the aOS for it. Ex-Citi/MS IB London. Exited fintech founder. Vertical AI since 2017.

[Mayank Mathur @MathurMnk]

1/ The 1000x Investor: what happens when you build an investment fund that never stops thinking?

We built Manthan Intelligence — the AI-native engine for the upcoming Tavaga VC Fund. It analyses 25 companies/day, scores its own predictions, and rewires its own lenses.

[Mayank Mathur @MathurMnk]

This week's discourse is about coding agents.

The same approach works for analytical decisions — due diligence, pattern recognition, building institutional knowledge.

Same failure modes. Same fixes. Different domain.

The hard part isn't getting loops to run.

It's building the feedback that makes them improve. That's where the compounding begins.

Full piece ↗ https://t.co/HsaScEqtVR

[Mayank Mathur @MathurMnk]

Boris Cherny: “I don’t prompt Claude anymore. I have loops that do it.”

Addy Osmani’s explainer hit 2.2M views this week.

We’ve been running AI agents in loops — not for coding, for investment decisions — for 3 months.

Same idea. Very different domain. Identical failure modes.

Here’s what we actually found: 🧵

[Mayank Mathur @MathurMnk]

🎯 The frameworks are the asset. The loop is just the plumbing.

We have 12 ways of looking at a company — each one stress-tested against 1,100 real historical decisions.

Accuracy: 65.1%, improving every week.

The loop runs them on a schedule. The improvement comes from the track record, not the scheduling.

[Robert Bye @RobertJBye]

Who’s the best engineering manager you know? Web focussed. Ideally with great product sense.

We’re hiring on my team at Anthropic!

[Alex Clayton @afc]

The profitable SaaS IPO is back? 📈

Entrata just filed: $574M implied ARR, +23% growth, and GAAP-profitable (18% operating margin, +11% net income). They offer a leading property management software solution and are PE-owned (founded in 2003).

It's the largest software company ever to IPO while growing 20%+ and GAAP operating-profitable.

Every name that filed at this scale — Snowflake, Palantir, Figma, Dropbox — was losing money. The last profitable-growth SaaS IPO like it was Veeva, at half the size. We looked back at over 150 SaaS IPOs.

It will be an interesting case study on what's possible for valuation for Cloud 1.0 companies vs. AI-native...likely at least $5 billion.

[andrew chen @andrewchen]

the team built a way for founders and investors to get intro'd at Tech Week! Use it May 26 (today) to Jun 7 to meet connect

try it here: https://t.co/NOfYEwJQCI

how it works:
- Founders sign up and say what they're building
- VCs hit the same link for investor access, then browse and reach out directly

why?
tens of thousands of people come to Tech Week every year to build, launch, hire, etc.

~30% of Tech Week attendees are founders. Many are building the next big thing. But since Tech Week is decentralized, there's been no central place for founders and VCs to find each other.

So we build this tool, which is only available for two weeks only during Boston + NY Tech Week (from May 26-June 7)

enjoy!

[Naval @naval]

The new competition isn’t Humans vs AI. It’s Humans with AI vs everyone else.

[Marc Andreessen 🇺🇸 @pmarca]

Interesting.

[Lenny Rachitsky @lennysan]

My biggest takeaways from @danshipper:

1. The future of work will happen inside Codex or Claude Code. Instead of putting AI into your SaaS tool, you’ll use your SaaS tools inside your favorite AI agents' in-app browser. Dan spends all his time in Codex now—writing documents, managing email, doing research, everything. He's using Google Docs, PostHog, and everything he needs within the agent's in-app browser. The agent can see what he’s doing, and has all of his context, so he and his agent collaborate quickly and super effectively.

2. Automation is a lie—every automation needs a human. Dan's company doubled in size this year despite being incredibly AI-forward. Why? Because in order to make automation work well, you need humans making sure everything keeps working. This is why benchmarks are misleading—they measure AI on problems we’ve already framed and can score, but there’s always a higher frame.

3. PMs will win the AI era. Marcus, a former PM who previously ran Axios’s writing product, joined Every after getting super AI-pilled. Now he runs their product Spiral, and ships faster than anyone on the team. He pairs technical knowledge with spiky product sense, deep user empathy, and an eye for what matters. Dan thinks any PM who gets really AI-native will be incredibly dangerous because the building is done for you—what matters is figuring out what to build and if it’s great.

4. Full-stack designers are becoming superheroes. Designers used to make beautiful interactions that engineers didn’t want to build or couldn’t execute properly. Now designers don’t need to hand things off; they can build it themselves. Designers are naturally creative people, and AI is the perfect tool for them because it lets them bring their vision to life without the traditional bottlenecks.

5. SaaS is not dead. In fact, Dan is bullish on SaaS stocks. When users bring their own AI (via Codex or Claude Code) to use SaaS products, the user—not the SaaS company—pays for tokens. This saves SaaS company’s margins. Since the agents need their own seats, Dan predicts that agents will create massive new demand for SaaS because there will be tons of agents using these products at high volume.

6. Every company will have one “super-agent” inside their Slack that every employee will use. Dan initially thought every employee would have their personal work agent, like a shadow AI org chart, but he’s completely flipped his view. He realized agents need humans who care about them. When someone gets tired of maintaining their personal agent, it becomes useless. The winning model is one forward-deployed engineer or AI-savvy person who maintains a company-wide agent (like Shopify’s River or Viktor), and then it trickles down to more specialized team agents as models improve and become less fiddly.

7. The AI job apocalypse is not happening, but you do need to evolve to stay relevant. Models make yesterday’s human competence cheap. But because everyone uses the same models, it all looks the same if you use it the default way; it becomes commoditized slop. Humans then take that frozen competence and use it to make something new and interesting for their specific situation. The key: “ride the models”—use them for everything you do, try new models when they drop, keep turning over rocks.

8. We will read way more AI-generated writing, and we will like it. Human writing is incredibly important for things that matter, but for internal docs, planning, and email, AI-generated is often better because most people are bad at writing strategy documents.

9. Build software for humans and agents to use together. The current model is building a CLI that an agent uses independently. Instead, you and your agent should be using the app together. This creates new design challenges—agents can make a billion requests in three seconds, so you need approval flows, inboxes that summarize what happened, logs, and easy rollback.

10. Forward-deployed engineers are the new most essential role. The big model companies have teams of people managing their internal agents, and those teams aren’t going away. It’s different from traditional software building, and certain engineers love it. As models get better, this role will evolve—you’ll be managing more agents doing more things.

[Mayank Mathur @MathurMnk]

So true!

"5. SaaS is not dead. In fact, Dan is bullish on SaaS stocks. When users bring their own AI (via Codex or Claude Code) to use SaaS products, the user—not the SaaS company—pays for tokens. This saves SaaS company’s margins. Since the agents need their own seats, Dan predicts that agents will create massive new demand for SaaS because there will be tons of agents using these products at high volume."

"7. The AI job apocalypse is not happening, but you do need to evolve to stay relevant. Models make yesterday’s human competence cheap. But because everyone uses the same models, it all looks the same if you use it the default way; it becomes commoditized slop. Humans then take that frozen competence and use it to make something new and interesting for their specific situation. The key: “ride the models”—use them for everything you do, try new models when they drop, keep turning over rocks."

[Lenny Rachitsky @lennysan]

My biggest takeaways from @danshipper:

1. The future of work will happen inside Codex or Claude Code. Instead of putting AI into your SaaS tool, you’ll use your SaaS tools inside your favorite AI agents' in-app browser. Dan spends all his time in Codex now—writing documents, managing email, doing research, everything. He's using Google Docs, PostHog, and everything he needs within the agent's in-app browser. The agent can see what he’s doing, and has all of his context, so he and his agent collaborate quickly and super effectively.

2. Automation is a lie—every automation needs a human. Dan's company doubled in size this year despite being incredibly AI-forward. Why? Because in order to make automation work well, you need humans making sure everything keeps working. This is why benchmarks are misleading—they measure AI on problems we’ve already framed and can score, but there’s always a higher frame.

3. PMs will win the AI era. Marcus, a former PM who previously ran Axios’s writing product, joined Every after getting super AI-pilled. Now he runs their product Spiral, and ships faster than anyone on the team. He pairs technical knowledge with spiky product sense, deep user empathy, and an eye for what matters. Dan thinks any PM who gets really AI-native will be incredibly dangerous because the building is done for you—what matters is figuring out what to build and if it’s great.

4. Full-stack designers are becoming superheroes. Designers used to make beautiful interactions that engineers didn’t want to build or couldn’t execute properly. Now designers don’t need to hand things off; they can build it themselves. Designers are naturally creative people, and AI is the perfect tool for them because it lets them bring their vision to life without the traditional bottlenecks.

5. SaaS is not dead. In fact, Dan is bullish on SaaS stocks. When users bring their own AI (via Codex or Claude Code) to use SaaS products, the user—not the SaaS company—pays for tokens. This saves SaaS company’s margins. Since the agents need their own seats, Dan predicts that agents will create massive new demand for SaaS because there will be tons of agents using these products at high volume.

6. Every company will have one “super-agent” inside their Slack that every employee will use. Dan initially thought every employee would have their personal work agent, like a shadow AI org chart, but he’s completely flipped his view. He realized agents need humans who care about them. When someone gets tired of maintaining their personal agent, it becomes useless. The winning model is one forward-deployed engineer or AI-savvy person who maintains a company-wide agent (like Shopify’s River or Viktor), and then it trickles down to more specialized team agents as models improve and become less fiddly.

7. The AI job apocalypse is not happening, but you do need to evolve to stay relevant. Models make yesterday’s human competence cheap. But because everyone uses the same models, it all looks the same if you use it the default way; it becomes commoditized slop. Humans then take that frozen competence and use it to make something new and interesting for their specific situation. The key: “ride the models”—use them for everything you do, try new models when they drop, keep turning over rocks.

8. We will read way more AI-generated writing, and we will like it. Human writing is incredibly important for things that matter, but for internal docs, planning, and email, AI-generated is often better because most people are bad at writing strategy documents.

9. Build software for humans and agents to use together. The current model is building a CLI that an agent uses independently. Instead, you and your agent should be using the app together. This creates new design challenges—agents can make a billion requests in three seconds, so you need approval flows, inboxes that summarize what happened, logs, and easy rollback.

10. Forward-deployed engineers are the new most essential role. The big model companies have teams of people managing their internal agents, and those teams aren’t going away. It’s different from traditional software building, and certain engineers love it. As models get better, this role will evolve—you’ll be managing more agents doing more things.

[David Sacks @DavidSacks]

Full article:

https://t.co/IQgBJXvDRy

[Aaron Levie @levie]

The CEO of Goldman Sachs is taking the other side on the pessimistic takes on AI and jobs.

If you looked at what work looked like a few decades ago and saw how much faster everything is or easier it is to produce the same thing as before - even before AI - you’d certainly have been convinced there’d be no jobs left.

What happens is we constantly just demand more from everything. Instead of automating a task and delivering the same value proposition, but cheaper, we just expect more from the overall product or service. Because some players in the market decides to do more with the automation, and it raises everyone’s expectations. So those that don’t respond can’t compete.

We get more financial analysis from analysts. We get much more comprehensive legal advice. We get more tailored financial services offerings. We get better software in niches we never thought we could automate. Our healthcare providers offer more tests and deeper medical advice. This just goes on and on.

When you move from believing the world is static and you’ll have a better view of how jobs evolve due to AI.

[Mayank Mathur @MathurMnk]

I completely agree with this view - juniors doing more interesting work earlier in their careers, experienced the same during my banking days! the only caveat I'd add is that the top management has to look at AI as a lever to grow their business 10x, instead of being stuck in the loop of marginal cost savings.

Redesign workflows, rethink value add, think at scales previously unimagined - that's the direction to take

[Aaron Levie @levie]

The CEO of Goldman Sachs is taking the other side on the pessimistic takes on AI and jobs.

If you looked at what work looked like a few decades ago and saw how much faster everything is or easier it is to produce the same thing as before - even before AI - you’d certainly have been convinced there’d be no jobs left.

What happens is we constantly just demand more from everything. Instead of automating a task and delivering the same value proposition, but cheaper, we just expect more from the overall product or service. Because some players in the market decides to do more with the automation, and it raises everyone��s expectations. So those that don’t respond can’t compete.

We get more financial analysis from analysts. We get much more comprehensive legal advice. We get more tailored financial services offerings. We get better software in niches we never thought we could automate. Our healthcare providers offer more tests and deeper medical advice. This just goes on and on.

When you move from believing the world is static and you’ll have a better view of how jobs evolve due to AI.

[Mayank Mathur @MathurMnk]

@SamiraBehrouzan @speedrun Beat your tweet by 2 hours 😅 submitted at 1:43am BST.

Manthan = the agent OS for the investment-banking pod. 46 tiered MD/VP/Analyst agents that argue before they conclude. Fully autonomous since 23 April.

Hope it makes the lineup: https://t.co/zn3OAyMmQu

[Mayank Mathur @MathurMnk]

Built Manthan on exactly this thesis. The 12-persona IC is agent-native by construction: agents arguing each other, decisions emitted as confidence + provenance objects, spend-bounded by default. 46 agents running my investment fund autonomously. SR007 app submitted last night.

Live: https://t.co/EE0zu5BstM

[Mayank Mathur @MathurMnk]

Manthan is exactly this for institutional finance — 46 agents running a fund autonomously since 23 April, no human triggers.

Live in your browser → https://t.co/EE0zu5BstM.

Submitted SR007 last night with two companion apps. — Mayank, ex-IB London (MS/Citi), Avasa AI (vertical AI), uTrade (exited).