Megan Maloney

sending an email without AI is the new handwritten letter

I fear that AI has decimated the traditional email inbox as we know it. Too many personalized emails slip through spam filter. Hope someone builds a better mousetrap. This one is cooked in its current form.

A map of SF investors by stage. Curated from recommendations by SF founders, investors, and operators. Talent-first / pre-idea / pre-company >Entrepreneurs First >1517 Fund >Bloomberg Beta >Human Capital Pre-seed / pre-product / first believers >Afore Capital >Abstract Ventures >Amity Ventures >Cambrian Ventures >Caffeinated Capital >Conviction >Cowboy Ventures >Forum Ventures >Hustle Fund >Pear VC >Precursor Ventures >SV Angel >Uncork Capital >Unusual VC Seed-first / early-stage generalists >Accel >Acme Capital >Acrew Capital >Battery Ventures >Benchmark >Bessemer Venture Partners >Blumberg Capital >Bullpen Capital >Costanoa Ventures >Craft Ventures >CRV >Defy VC >Felicis >First Round >Floodgate >Forerunner Ventures >Foundation Capital >Greylock Partners >Headline >Initialized Capital >Kindred Ventures >Menlo Ventures >Redpoint Ventures >Spark Capital >Susa Ventures >True Ventures >Upfront Ventures Multi-stage / large platform firms >8VC >a16z >Bain Capital Ventures >Base10 Partners >Coatue >Francisco Partners >General Catalyst >Google Ventures >Greenoaks >Greycroft >Index Ventures >Khosla Ventures >Kleiner Perkins >Lightspeed >NEA >Norwest Venture Partners >S32 >Sapphire Ventures >Sequoia Sector-focused / thesis-driven >Abad Capital >Atomic VC >Better Tomorrow Ventures >Builders VC >Commerce Ventures >Delphi Ventures >Fifty Years >Gradient Ventures >GSV Ventures >Infinity Ventures >Lux Capital >NFX >Obvious Ventures >Patron >Ribbit Capital Broad early-stage >Draper Associates >Founders Fund >Renegade Partners >SignalFire Platform / accelerator-style >Plug and Play >Y Combinator

The token cost to build a production feature is now lower than the meeting cost to discuss building that feature. Let me rephrase. It is literally cheaper to build the thing and see if it works than to have a 30 minute planning meeting about whether you should build it. It’s wild when you think about it. This completely inverts how you should run a software organization. The planning layer becomes the bottleneck because the building layer is essentially free. The cost of code has dropped to essentially 0. The rational response is to eliminate planning for anything that can be tested empirically. Don’t debate whether a feature will work. Just build it in 2 hours, measure it with a group of customers, and then decide to kill or keep it. I saw a startup operating this way and their build velocity is up 20x. Decision quality is up because every decision is informed by a real prototype, not a slide deck and an expensive meeting. We went from “move fast and break things” to “move fast and build everything.” The planning industrial complex is dead. Thank god.

The case for emerging managers in a nutshell

Excited for this one

If there’s BC and AD, then there must be BAI and AAI because that’s the bifurcation in my notes. Before AI and After AI.

"The top-20 per cent of consumers account for around 65 per cent of sales of products like cookies and ice cream. If those 'super users' end up on GLP-1 drugs, you get a non-linear reduction in sales."

Ok Claude, tell me how to capture the President of Venezuela without any of our guys dying. Make no mistakes.

He is right, and @rabois is himself a barrel.

This is the part of my job at @a16z that most gets me up in the morning: building an incredible team. We’re hiring for a lot of roles at the moment: Chief of Staff: help me run all our initiatives at the intersection of investing, networks, and media. Senior role with a big impact. Marketings and Comms Leadership: own the narrative for the a16z portfolio and firm. We’re hiring a CMO and a Chief Comms Officer. Head of Podcasts: run, produce, scale our podcasts end-to-end. We’re also looking for a podcast host for new podcasts. Content: define the agenda for tech. Build the most well-read and high-taste tech publication in the industry as an editor, writer, or specialist in growth/operations. Social: build the biggest go-direct channels for the firm and top-tier CEOs, drive content strategy, design and audience growth. Research: help us curate the most important conversations in tech. Networks: Help build digital and IRL community across our extended networks of founders, investors, creators, and the tech ecosystem more broadly. Help run our VIP group chat operation. Help us source and identify amazing future founders at top companies today. College: help us build a network of talented young people on campuses across the country (and the world). Events: own flagship gatherings and summits that create moments people talk about. Video: we want to take flyers on creative directors, content generalists, and aspiring filmmakers in tech. More broadly, we’re interested in hiring anyone doing something exceptional at the intersection of investing and networks or content. People here love the work, the people, and the growth. a16z.fillout.com/new-media-hiri…

Brex hired 900 people in 2021-2022. They took pay cuts for equity. Options struck at $8-10/share. Exit price: ~$5/share. 4 years of work. Zero equity value. This is the 409A trap nobody explains in the offer letter. If you're taking a startup job for equity, ask what happens if they exit below your strike price.

Introducing Scout Program. Real capital. A live leaderboard. And a path to running your own fund. scoutprogram.com This will be the most interesting investing program in the industry when all is said & done.

New RFS's just dropped Reminder: These are just ideas, these are not the ONLY ones we will fund. The best ideas invariably will be from YOU, not us.

So addicted to Claude Code, I stayed up 19 hours yesterday and didn't sleep til 5AM

The sweet spot for present-day AI seems to be projects that were constrained by the rate at which humans could produce text. That's why it works so well for programming. Basically programmers produced valuable text. But there are lots of other projects with this quality.

Language models are unusually good at turning messy reality into structured inputs — extracting action items from complaints, turning transcripts into CRM-ready fields, converting unstructured text into something your systems can use.

🚨 Anthropic CEO Dario Amodei just dropped a massive timeline update at Davos 2026: “I have engineers within Anthropic who say ‘I don’t write any code anymore. I just let the model write the code, I edit it’... - the creator of Claude code recently also said “100% of his contributions to Claude code were written by Claude code” for the month of December Dario then goes onto say: “We might be 6 to 12 months away from when the model is doing most, maybe all of what SWEs do end-to-end.” If the recursive self-improvement loop closes this year, the curve is about to go vertical.

VERTICAL AI CHALLENGE Vertical AI Founders: You've spent 2+ years building your agents, training your model on your customers' data, embedding into workflows, creating a powerful GTM motion, all the best practices. You've beaten back challengers and are the #1 or #2 player in your vertical. I'm sorry, you cannot relax. In fact, you need to massively up your game. Turns out you are facing an existential challenge: long-horizon agents (eg: Claude Code). Agents that are not trained on a specific domain, but can reliably work for hours or days on end in pursuit of a goal, self-correct, and actually do stuff. I'm sure many Vertical AI founders will say: "Oh, we are not worried. We are the system of record for decision traces. We train on enterprise-specific context. That's why these horizontal agents can never catch up with this." You might well be right. But, but, but ... you cannot afford to bury your head in the sand. These long-horizon agents will get better very, very quickly. You need to understand precisely how good they are at the exact jobs you've built your agents on. You cannot wait for someone else to do this. For example, if you're a legal AI company with an agent that automates contract review, you must compare how good your specialized agent is versus a general-purpose long-horizon agent that's simply given the contract and asked to perform the same review. My challenge to you: Assign a strong engineer on your team to focus 100% on using long-horizon agents (with minimal context, other than just the contract in the example above) to compete with your custom-trained agents. Benchmark how the long-horizon agents perform vs your agent. Rinse and repeat it every few months. Like with most other things worth measuring, what matters is the rate of improvement (the "slope" vs the Y-intercept). If the long-horizon agent is 30% as good as your vertical agent on Day 1, but 50% as good on Day 60, and 70% as good on Day 120, you need to reassess your product strategy. AGI is coming for everyone. Long-horizon agents are the closest we have to AGI, and as a Vertical AI company, you need to figure out how you compete and survive. Game on.

2026 is gonna be crazy