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No Priors Ep. 10 | With Copilot's Chief Architect and founder of Minion.AI Alex Graveley

Everyone talks about the future impact of AI, but there’s already an AI product that has revolutionized a profession. Alex Graveley was the principal engineer and Chief Architect behind Github Copilot, a sort of pair-programmer that auto-completes your code as you type. It has rapidly become a product that developers won’t live without, and the most leaned-upon analogy for every new AI startup – Copilot for Finance, Sales, Marketing, Support, Writing, Decision-Making. Alex is a longtime hacker and tinkerer, open source contributor, repeat founder, and creator of products that millions of people use, such as Dropbox Paper. He has a new project in stealth, Minion AI. In this episode, we talk about the uncertain process of shipping Copilot, how code improves chain of thought for LLMs, how they improved product, performance, how people are using it, AI agents that can do work for us, stress testing society's resilience to waves of new technology, and his new startup named Minion. 00:00 - Introduction 01:50 - How Alex got started in technology 02:28 - Alex’s earlier projects with Hack Pad and Dropbox Paper 07:32 - Why Alex always wanted to make bots that did stuff for people 11:56 - How Alex started working at Github and Copilot 27:11 - What is Minion AI 30:30 - What’s possible on the horizon of AI

Sarah GuohostAlex GraveleyguestElad Gilhost
Apr 24, 202341mWatch on YouTube ↗

At a glance

WHAT IT’S REALLY ABOUT

From Linux Hacker To Copilot Architect: Building The Age Of Agents

  1. Alex Graveley traces his path from teenage Linux and open source contributor to building Hackpad, Dropbox Paper, and ultimately serving as chief architect of GitHub Copilot.
  2. He explains lessons from ops-heavy ‘assistant’ startups like Magic, early crypto work, and the messy human behaviors that make automation hard, especially before modern language models.
  3. Graveley details Copilot’s origin with a weak early code model, the experimentation that led to the ghost-text UX, and the ruthless focus on speed that drove massive adoption and retention.
  4. He closes by discussing his new startup Minion, the coming era of AI agents that act for users in the real world, and the societal challenges around identity, abuse, and building the right talent and intuition for this new wave.

IDEAS WORTH REMEMBERING

5 ideas

Open source and real user problems can seed impactful products.

Hackpad emerged from a Burning Man warehouse struggling to coordinate projects; by forking Etherpad and solving a real organizational need, it grew into a YC company and ultimately an acquisition, showing how niche coordination problems can become mainstream tools.

Ops-heavy ‘assistant’ startups exposed the limits of pre-LLM automation.

Magic and similar services relied on human operators, revealing how messy, context-dependent, and unpredictable human requests are in text form—and why earlier ML approaches (like basic seq2seq) couldn’t yet handle the variability of real-world tasks.

AI abuse and human bad actors are nearer-term threats than runaway AGI.

Graveley expects years of discomfort as AIs are weaponized for fraud, scams, and social engineering, arguing that policy, identity systems, and enforcement need to catch up to mitigate harm rather than focusing solely on AI itself going rogue.

Product success with AI hinges on UX and extreme latency optimization.

Copilot only took off after shifting from clunky UIs to Gmail-style ghost text and optimizing multi-line completions; data showed every extra 10 ms of latency reduced completions by about 1%, and regions far from Texas under-used the product until additional GPU regions were deployed.

Weak early models can still be viable if they scale and are framed right.

The initial code model from OpenAI solved under 10% of test cases, yet systematic evaluation, better prompting, more data (including version histories and diffs), and improved UX pushed success rates above 60% on in-the-wild tests, enabling a mass-market product.

WORDS WORTH SAVING

5 quotes

The honest answer is, I think we're gonna go through a many-year period of extreme discomfort, where AIs pretend to be things, or confuse people, or extract money from your grandparents.

— Alex Graveley

I'm not really scared about AIs killing us... I'm more worried about bad people using new technology to hurt us.

— Alex Graveley

It turns out code is pretty special, right? You can run it. So if an AI generates some code and it runs, you know something about that code that you wouldn't know necessarily with text.

— Alex Graveley

Our retention rate was 50%. Months later it was still above 50% by weekly cohort, which is insane.

— Alex Graveley

The goal, right, is straight out of sci‑fi: you wanna make a thing where you say, 'Hey, computer, file my taxes,' and it does the right thing.

— Alex Graveley

Alex Graveley’s early background in Linux, open source, and startups (Hackpad, Dropbox Paper)Lessons from early operations-heavy assistant products like Magic and related startupsCrypto work (hCaptcha, MobileCoin) and the intersection of crypto, identity, and AI abuseSecurity, fraud, and societal risks from AI agents versus classic existential AI riskThe origin, technical evolution, and productization of GitHub Copilot with OpenAIUX, latency, and usage metrics that determined Copilot’s success and business modelMinion.ai and the future of AI agents that perform real-world tasks for users

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