Calvin Zeng

partner @paradigm

[Calvin Zeng @calvinzeng]

@dennisihong @jefernandez8 I also worry that one of their moats was the ability to corner the supply chain and strike big deals and that ability will be diminished without Jensen.

[Calvin Zeng @calvinzeng]

@jonbma What do you think the terminal value is of NVDA if CUDA is an increasingly shrinking moat?

[Varun Srinivasan @varunsrin]

some thoughts on writing software in nov 2026:

- it feels like magic
- the first 80% is 100x faster
- rewriting systems is also 100x faster
- opus 5.5 is very good at a lot of things
- "deep flow state" is much easier to reach
- almost anything can be built in typescript and rust
- building secure things is very, very hard

the optimization function is how to maximize agents working on goal with the fewest interruptions needed. some things that help:

- know where to trust vs guide (and keep retesting)
- give them agency like "i'm going to bed, get this done"
- if there is a reward function, ask them to hillclimb
- the socratic method works wonders
- skills and memories can often hinder
- asking agents to review themselves still helps
- cowriting a plan/spec/invariants helps sometimes

managing your own psychology is equally important:

- be a lot more ambitious about what you can achieve
- avoid dopamine traps / false signals of progress
- get good at parallelizing and context switching

there are also some things that don't quite work fluidly yet, but will get unlocked in the very near future:

- software factory or "find and fix the bugs"
- multiplayer agentic workflows
- agents managing other agents for high level outcomes
- organizational memory

[Calvin Zeng @calvinzeng]

This is the clearly the future of software engineering and will require learning an ML skillset, broad adoption of cloud agent infra, and a much more ambitious set of prompts :)

[Matt Huang @matthuang]

Swarm riders and Swarm-native companies

AI-use even among highly AI-native people is power-law distributed.

There seems to be a new class of people — call them “swarm riders” — who are capable of consistently commanding large swarms of agents to productive ends.

This is a new kind of managerial skill, analogous to the skill it takes to manage large organizations of people, but wholly different in nature and of course more technical.

Based on the swarm riders we know (including many at @Tempo and @Paradigm), these people are capable of productive token spend that is 1000x-10000x higher than median software engineers, and 100x higher than top 5% Claude Code users.

The art of swarm riding is different from software engineering and more akin to machine learning, where practitioners grapple with black box systems: defining reward functions and evals, transmuting non-verifiable problems into verifiable problems, running experiments, etc.

Swarm-native companies are a new class of company founded by and populated by swarm riders. These companies are organized around swarms in a way that conventional companies will struggle to replicate, as the cost of internal human coordination is sand in the gears of an effective swarm. Effective swarm-native companies will define the right reward functions and evals, remove functional and bureaucratic barriers, and then let the swarms cook.

We’ve gotten to see a few swarm-native companies up close — reshaping financial rails @Tempo, building a new gaming platform @Spawn, cyber swarms at Cathedral Systems — and it’s clear these teams wield a significant advantage over pre-AI teams.

Some questions I’ve been wondering about:

1) How fast will swarm riding diffuse through the economy? The implications for token spend could be significant.

2) Can swarms be effectively adopted by existing companies or will it generally track the formation of new swarm-native companies?

3) What becomes scarce as swarm capabilities themselves commoditize (through frontier models becoming better at swarming, swarm capabilities built into harnesses, etc)? Cultures and teams that can define reward functions well and otherwise get out of the way?

[Calvin Zeng @calvinzeng]

@mishhman Would love to chat!

[Calvin Zeng @calvinzeng]

RT @alanapalmedo: Fired up to back this team of killers @adamblake @OverReactor1776 and @nickporadek to solve the hardest physical constrai…

[Calvin Zeng @calvinzeng]

@katieruthmishra GC CVF has done a good job of helping close the gap

[Calvin Zeng @calvinzeng]

@TheRohanVarma @embirico @thsottiaux Add me zirp

[Calvin Zeng @calvinzeng]

@Lihong062 Timing matters a lot given GPU debt is 5Y - so both can be right, but slope is what creates very diff NT outcomes

[Calvin Zeng @calvinzeng]

@TechFundies Curious where you stand. Not sure we are anywhere near AGI from a commercialization perspective, but obviously timing matters.

[Matt Huang @matthuang]

Zipline is now one of the largest autonomous robotics companies in the world, operating with scale, safety, and a magical user experience for consumers and merchants (including Walmart, Uber, etc)

Excited for Paradigm to lead this $1.5B round

[Tarek Mansour @mansourtarek_]

Last night, the power to decide what America sees changed hands.

I have spent a ton of time in DC over the last few months. The elites were certain the markets were off. I heard many variants of “no chance markets are right about X” and “I’ve been in politics for a decade. ‘These people’ don’t really know much.”

Last night, ‘these people’ beat the elites.

Prediction market favorites won 479 of the first 487 races. That’s 98%.

The elites live in a bubble. They theorize, write, ideate, and mistake credentials for an understanding of lives they’ve never lived. They don’t see and feel what the everyday man, on the ground, sees and feels.

Knowledge is not exclusive to the elites but scattered across millions of people.

Someone knows their hometown better than any national pundit. Someone has followed a local race passionately for months. Someone has followed a local race dispassionately for months. Someone notices a shift in their community before it makes the headlines. Someone is shaped by an incident at work. Someone is shaped by what’s getting harder or easier for their families.

Each holds a piece of the picture that no expert can see in full.

And for the longest time, that knowledge had no way to reach the rest of the world.

For generations, election night meant turning on the TV and waiting for a handful of people to tell you what happened.

Prediction markets change that.

You don't need a title, a large following, or an editor to publish you. All you need is a perspective and the conviction to back it.

That changes who holds power over information.

Anyone can see the market. Anyone can contribute to it. Anyone can question it. No title gives you the final word. No gatekeepers can filter your view.

Sometimes the signal can be wrong. But anyone can challenge the consensus and improve it.

And when people consolidate their knowledge together, something remarkable happens: millions of individual perspectives become a signal that we all can learn from.

Over the last few months, the people showed up on Kalshi to tell the world what they were going to do.

Last night, the megaphone was passed to them.

[Calvin Zeng @calvinzeng]

@jkhamehl @martin_casado Extremely thoughtful / well researched post!

[Calvin Zeng @calvinzeng]

@abhiadesai @WillManidis Agree

[Calvin Zeng @calvinzeng]

@Alex_Goston Great analysis and unfortunately much of what is needed to answer this question is largely unknowable

[Calvin Zeng @calvinzeng]

@GarrettLord @jessicarwolf Congrats!

[Calvin Zeng @calvinzeng]

@shaunmmaguire @uncovered_alpha Thank you for explaining your reasoning!

[Calvin Zeng @calvinzeng]

@nicholasachow Awesome primer!

[Calvin Zeng @calvinzeng]

@_simonsmith Curious how this would compare vs Grok Bot