Y Combinator

When Rolls-Royce pulled out as @boomsupersonic's engine partner, it looked like a potentially fatal setback. Instead, it pushed Boom to build its own engine, a decision that ultimately helped unlock boomless supersonic flight and opened up an entirely new business: gas turbines for the massive power demands of AI data centers.

Boom founder and CEO @bscholl returns to The Social Radars with @jesslivingston and @cjoneslevy to tell the story of how that crisis became one of the company’s biggest opportunities.

They talk about why building the engine in-house changed the airplane itself, how Boom realized the same technology could generate power for data centers, and why the new turbine business could help fund the development of its supersonic airliner.

00:00 — Rolls-Royce Walks Away
02:39 — The Breakup Goes Public
07:39 — Deciding to Build the Engine In-House
09:01 — The Brian Chesky Advice That Changed Everything
10:25 — Why Building the Engine Was the Right Call
11:06 — Unlocking Boomless Supersonic Flight
13:35 — The Problem of Funding a Supersonic Airliner
14:37 — The AI Power Shortage Creates an Opportunity
16:23 — Turning a Jet Engine Into a Gas Turbine
18:26 — Why Hard Tech Is So Difficult to Fund
21:20 — From Paycheck to Paycheck to a New Business
22:26 — Why America Stopped Building Power
25:56 — The Aerospace Industry Stopped Innovating
27:21 — Designing the Engine and Airplane Together
32:55 — Rethinking the Passenger Experience
39:03 — Rebuilding America’s Industrial Base

Replit was building toward the AI coding era years before it had a name. In 2024, the company shipped an agent that could take a request in plain English, work through errors, provision its own infrastructure, and keep going until the software actually worked. The model companies even reached out to ask how Replit had pulled it off.

Co-founder and CEO Amjad Masad had been thinking about making programming radically more accessible since 2008, and Replit’s original pitch deck predicted AI-generated code before the Transformer paper was published. Today, that same technology is moving into banks, manufacturers, and other large companies, where Amjad says teams are replacing software projects once scoped at $10 million with $250,000 of Replit.

On How to Build the Future, @paulg sits down with @amasad to talk about why @Replit was so well positioned when coding agents arrived, what comes after vibe coding, and his bigger vision for the “self-driving company,” where AI agents increasingly build and operate the software a company needs.

00:00 - "You guys basically invented vibe coding"
02:43 - Watching users copy-paste from ChatGPT
05:27 - The origins of Replit
08:00 - People underestimate their ability to predict the future
10:27 - Grieving the craft
12:52 - A culture of obsessing over users
15:13 - Why enterprises pick Replit
17:28 - The agent was just another user
20:17 - It barely worked, and users loved it
23:13 - $10 million projects now for $250,000
25:45 - Sandboxing for humans
28:14 - "The self-driving company"
30:34 - The way AI flows into companies
33:28 - What Steve Jobs knew before the iPad
35:59 - Killing the sacred cow
38:25 - The rest of the world is two years behind
41:15 - The compute brought on AI
43:46 - The person who assumes dramatic change

Congrats to @getreflex (W23) on their $14M Series A! They build the platform companies use to create custom software around their own operations.

Reflex started as an open source framework for building web apps in pure Python. Now the same platform takes a business problem all the way to a working app, with AI writing the software and Reflex engineers putting it into production.

The open source framework has now passed 29,000 GitHub stars, powers internal tools at ~30% of the Fortune 500, and has been used to build more than 1 million apps.

reflex.dev/blog/series-a-…

Tractian (W21) is building physical AI for asset-heavy industries, helping factories and industrial operators monitor critical equipment, predict failures, and prevent costly downtime. Their platform combines proprietary sensors, software, and AI to monitor hundreds of thousands of assets in real time.

They’ve now crossed $100 million in ARR, growing 90% year over year, and recently raised a $130M Series D at a $1.4B valuation.

In this fireside, co-founder and CEO @igmarinelli joins YC’s @harjtaggar to share how Tractian grew from a problem he first saw through his father’s work as a plant manager into a platform used by some of the world’s largest industrial companies.

They dig into how @Tractian won its first enterprise customers, why AI is changing the economics of industrial maintenance, and how the company is moving from monitoring machines toward a future where factories can increasingly operate themselves.

00:05 — What Tractian Does
02:20 — How Tractian Monitors Machines
05:38 — Why the MVP Had to Be “Awesome”
09:13 — The Problem Igor Grew Up With
11:07 — 88 Factory Visits in Year One
15:25 — Winning the First Customer
18:15 — Selling Without Social Proof
20:24 — How to Structure a Paid Pilot
22:14 — How AI Changed Tractian
25:55 — Why Smaller Models Can Win
31:09 — AI and the Industrial Workforce
35:04 — Robotics in the Factory
38:18 — From Monitoring to Autopilot
41:32 — Fundraising at $1.4B
46:58 — The Future of Industrial Work

Weave (W25) helps engineering teams understand whether their AI coding tools are actually making them more productive. Its models measure the work humans and agents produce and compare that output with AI spending.

The company recently raised a $13.5M Series A led by Standard Capital and launched Weave Router, which selects models for different coding tasks.

In this fireside, @WorkWeave co-founders @adambcohen93 and @AndrewChurchiII join YC's @snowmaker to share how the company evolved alongside the rapid rise of AI coding, from measuring the impact of tools like Cursor to helping teams build software factories that can work autonomously.

They explain how the best engineering teams are learning to measure the real output of their agents, spread the habits of their strongest AI users, and optimize for a future where much more software is written without a human at the keyboard.

00:00 — Optimizing AI Coding With Weave
04:02 — Getting More From Your AI Budget
08:39 — Measuring Work Beyond Lines of Code
12:30 — From AI Adoption to Optimization
16:47 — Measuring AI ROI and Coaching Engineers
20:16 — Building Software Factories
25:38 — The Future of Model Routing