Founders in Arms

Stephen Balaban is co-founder and, as of a recent leadership change, CTO of Lambda, the AI cloud infrastructure company he and his twin brother started in 2012. It took five pivots — augmented reality, a facial-recognition contact book, a camera embedded in a baseball cap, the AI image app Dreamscope, then finally workstations and servers — before Lambda found a business that made money in 2017. Along the way, Stephen kept the company alive by consulting on the side, including projects with Airbus and the creators of South Park.
What you'll learn:
Why it took five pivots and 14 years for Lambda to find product-market fit
How side consulting work — for Airbus and even South Park's creators — funded the company through its leanest years
Why mainline Silicon Valley VCs kept passing on Lambda even while it was profitable, and why that didn't change after ChatGPT
How a Series D round pulled largely from Taiwanese manufacturers and family offices instead of traditional venture capital
What pushed Stephen to step down as CEO and bring in Michel Combes, a former CEO of Sprint and SoftBank International
Why Stephen thinks the disinformation around data centers — noise, water use — doesn't hold up

Chapters:
(01:10) Meet Stephen Balaban, co-founder and CTO of Lambda
(02:40) Palo Alto in 2012 and Lambda's earliest days
(03:42) Building Heads Up, a facial-recognition contact book for iOS
(05:06) The ImageNet moment and training neural nets on NVIDIA GPUs
(06:14) Five pivots: from augmented reality to Dreamscope
(08:55) Funding the company through consulting, including work with Airbus and South Park's creators
(12:13) Writing down the goal to IPO back in a 2012 notebook
(14:28) Raising a first $600K, including Austin Russell's $20K check at a $400K valuation
(24:29) Lambda's climb from $3M in revenue to a $1B run rate
(26:57) Why Silicon Valley VCs kept saying no — even after ChatGPT
(34:04) A Series D built largely on Taiwanese manufacturers and family offices
(37:13) Immad on Mercury Books, Mercury's new bookkeeping product
(44:12) Stepping down from CEO to CTO and bringing in Michel Combes
(52:34) Debunking data center disinformation, from water use to noise

What if the company students use to find their first internship became one of the most important players in training AI?
Garrett Lord, co-founder and CEO of Handshake, joins Immad Akhund and Raj Suri to break down Handshake's unlikely pivot. Handshake started as a way to help college students — regardless of where they went to school — find internships and jobs, and grew into a $200M+ ARR business used by most students in America. But over the last 18 months, Garrett has built a second business inside Handshake: using the company's network of 30 million students and alumni to help AI labs train their models on real, high-quality, professional-domain data — from oil and gas to finance to scientific research. That business alone has gone from zero to nearly $2 billion in revenue in about a year.
The conversation goes deep on how this actually works: recruiting domain experts, building task environments that function like video games, scoring model performance against expert-validated tasks, and why 70% of the money spent training a model today goes toward reinforcement learning rather than pre-training. Garrett, Immad, and Raj also cover the open-weight vs. frontier model debate, why China may already be ahead on robotics, and what jobs might look like in a world where AI models can eventually learn continuously, on the job.
The episode closes with a genuinely open-ended debate between Garrett and Immad about what humans will actually do for a living, and for meaning, if knowledge work is mostly automated — and how disruptive that transition might be along the way.
What you'll learn:
How Handshake used its network of 30 million students and alumni to build a second, multi-billion-dollar business training AI models
Why 70% of AI training spend now goes toward reinforcement learning, not pre-training
How AI labs identify gaps in their models and commission the specific data needed to close them
Why data, not algorithms, may be the real long-term moat for AI companies
Why computer use — AI navigating real software and websites — has recently gotten dramatically better
Why China may already be ahead of the U.S. in deploying real-world robotics
How enterprises like Mercury are likely to use a mix of frontier and open-source models going forward
What Handshake learned scaling a data business from zero to nearly $2B in under two years
Garret and Immad's differing views on what human work and meaning look like if knowledge work becomes automated

Timestamps:
(00:19) Introduction and Handshake's origin story
(01:39) Handshake's new business: training AI models on real-world data
(02:30) How Handshake's 30M-person network became a moat
(04:24) Inside the "video game" environments used to train agents
(05:00) Why 70% of AI training spend now goes to reinforcement learning
(08:18) How AI labs commission specific data from Handshake
(12:10) Why computer use has finally gotten good
(14:11) What models are still bad at, and why
(17:00) The "8 people can agree" test for what AI can be trained to do
(18:23) China's 2 million working robots, and why the US is behind
(22:12) Open-weight vs. frontier models, and how enterprises will use both
(31:15) Scaling from zero to $2B: what broke along the way
(34:02) Handshake's "Olympic pace" culture value
(39:41) Why continuous learning is the next frontier for AI
(42:07) Bill Gates' essay on AI, job loss, and taxing tokens
(45:23) Immad and Garret debate what jobs and meaning look like in an AI-driven future

This week, Immad and Raj sat down for a wide-ranging catch-up on the biggest stories in tech right now — from record-breaking acquisitions to what they're each giving AI access to in their own lives.
The conversation kicks off with the OpenRouter-Stripe acquisition and Cursor's $60B deal, and what both say about investing in "obvious" ideas when the underlying trend is right. From there, Immad and Raj get into the economics of secondary markets (including Immad's own purchases of SpaceX and Anthropic shares pre-IPO), why staying private longer might be bad for retail investors, and the case for making it easier for smaller companies to go public.
They also dig into consumer AI hardware — why simple, single-purpose devices like Pocket are breaking through where more complicated products haven't — and trade notes on what they've each connected their own AI assistants to, from email and calendars to health results and scheduled tasks.
What you'll learn:
Why "obvious" ideas can still be some of the best investments, if the trend is right
What's driving the OpenRouter-Stripe and Cursor acquisitions, and why they matter for developer tools
How Immad and Raj think about the risks and opportunities in secondary markets
Why Immad believes deep secondary liquidity could be bad for retail investors and the broader economy
What's made simple, single-purpose AI hardware devices succeed where more ambitious ones have struggled
How Immad and Raj are using AI assistants in their own lives, from productivity to personal health
Why "PMF doesn't exist anymore" in consumer products, according to a recent conversation Raj had with Character AI's CEO
What it will take for AI to handle more complex, multi-step tasks like buying insurance
How Anthropic and OpenAI's revenue numbers compare going into the back half of the year

Timestamps:
(00:47) Introduction
(01:21) OpenRouter's acquisition by Stripe
(02:18) Cursor's $60B deal and the case for "obvious" ideas
(06:03) Why big exits justify high seed valuations
(08:47) AI adoption is still low — why Immad is bullish on the next 5-10 years
(12:56) Buying into SpaceX and Anthropic pre-IPO
(15:17) The case against deep secondary markets
(18:21) Why Pocket is winning in consumer AI hardware
(20:02) Talking to Matic's robot vacuum
(22:07) An idea for family video, and why photo frames haven't solved it
(24:14) What Character AI's CEO said about PMF at a recent Tribe event
(28:28) What Immad and Raj have given their AI assistants access to
(33:08) Scheduled AI tasks, and why AI still can't do the last mile
(35:59) Anthropic and OpenAI's latest revenue numbers
(38:06) The debate over housing density and California's building laws

Mo Al Adham is the founder and CEO of Frec, a brokerage platform he describes as "brokerage 2.0" — building on core trading primitives to offer more sophisticated strategies like direct indexing, long-short direct indexing, and options overlays. Before Frec, Mo co-founded Twitvid, an early video-for-Twitter startup, and later spent five years at Twitter. He founded Frec in 2021 and launched the product in October 2023.
What you'll learn:
Why the $1-30M wealth segment — about 10 million US households — controls 40% of all investable wealth in the country, and why it's the fastest-growing segment
How direct indexing creates "tax alpha" by harvesting capital losses, and why that's a deferral of taxes rather than an elimination of them
How a step-up in cost basis at death effectively forgives the deferred tax bill
The concrete numbers: how much a $100k investment can harvest in losses via a classic direct index versus a long-short direct index
Why long-term, sophisticated investors have proven far less fee-sensitive than the market assumes
Mo's path from Twitvid — an early video app built on top of Twitter — to five years working inside Twitter itself
How a frustrating experience with a wealth manager who charged 1% fees for little added value planted the idea for Frec
Why Mo's six months of "top-down" market research largely failed, and why a "bottoms-up" approach — starting from what he actually cared about — led him to Frec
Why Frec had to resequence its roadmap when rising interest rates undercut its original plan to lead with a cheap line-of-credit product
Immad's framework for company OKRs (which he calls "COR") and why he insists on including non-measurable results
Why Frec has deliberately stayed out of banking, unlike some robo-advisor competitors
Mo and Immad's picks for financial products that should already be obsolete

Chapters:
(00:00) The $1-30M wealth segment and why it holds 40% of US investable wealth
(01:03) Introducing Mo Al Adham and Frec, "brokerage 2.0"
(02:07) Targeting sophisticated investors vs. democratizing access
(03:12) Why long-term investors are stickier and less fee-sensitive than assumed
(07:08) Tax alpha explained: deferral vs. elimination
(09:20) How direct indexing lowers cost basis through loss harvesting
(12:45) Long-short direct index and portfolio tilts
(14:16) Mo's first startup, Twitvid, and getting outpaced by Twitter
(16:29) The wealth manager experience that inspired Frec
(19:49) Vetting the idea: six months of top-down research that failed
(22:18) Switching to a bottoms-up approach and finding conviction
(30:39) Immad's approach to OKRs, called "COR"
(36:24) Frec's pivot from lending to investing as rates rose
(52:16) Rapid fire: AI in fintech, obsolete products, and more

Guillermo Rauch is the co-founder and CEO of Vercel, the company behind Next.js, and previously created the widely-used Socket.io library. In this special episode, recorded live in front of an audience, Guillermo joins Immad Akhund and Raj Suri for an open Q&A covering pivots, ethics, investors, and the future of work in the age of AI.
What you'll learn:
The difference between a "lowercase p" pivot (refining focus) and an "uppercase P" pivot (starting over) — and how to know which one you need
How to build an ethical framework for operating in an industry full of shortcuts and noise
How to extract real signal from investors without letting them drive your roadmap
Real pivot stories from Presto (restaurant tablets to voice AI), Lyft (carpooling to peer-to-peer ride-hailing), and Mercury's early product-market-fit signal
Why blaming distribution is often easier than blaming the product — and why that's a trap
How founders can get their teams to think about prioritization the way they do
How Mercury created early demand by deliberately recruiting a broad, vocal set of seed investors
What "the future of work" looks like when your team's job shifts from producing outcomes directly to building the systems that produce them
How growing up outside Silicon Valley shaped each panelist's belief that they could build something from scratch

Chapters:
(0:00) Lowercase p vs. uppercase P pivots
(1:05) Q&A begins
(1:23) Building an ethical framework in Silicon Valley
(4:38) Balancing customer signal vs. investor advice
(9:53) Pivot stories: Presto, Lyft, and Mercury's obvious PMF moment
(15:34) Why founders blame distribution instead of the product
(16:08) Getting your team to think about prioritization like you do
(18:21) How Mercury created early demand with 60 seed investors
(19:48) The future of work: agents, harnesses, and factories of output
(24:25) Growing up outside the Valley: mentors and self-belief
(28:04) Closing