Supercompanies with Greg Shove

Julia Stiglitz, co-founder and former CEO of Uplimit, joins Greg Shove to explain how her company pivoted twice in the AI era — ending in an acquisition by Handshake — and what she learned building an AI-native team of 30 people along the way.

Uplimit started in 2021 with a thesis that AI could make online learning actually work. A year later, ChatGPT launched and forced a full rethink. Julia and her team discovered that the AI-powered learning platform they were building mattered more than the content on it — eventually arriving at the “AI Skills Instructor,” a system that generates personalized coaching experiences instead of static courses. That product became the foundation for Handshake’s new education vertical, built for a platform with 1,600 universities, 25 million students and alumni, and a million employers.

In this episode, Julia and Greg discuss:

• Why Uplimit pivoted away from content creation entirely, and how hard it is to let go of a product you’ve already invested time and energy building
• The “work slop” problem — when AI makes individual output faster but pushes editing and review burden upstream to leadership
• Redesigning product workflows so designers prototype in code instead of Figma, and what that does to team dynamics
• Why workflow and role changes are harder to drive than individual AI adoption, especially in larger organizations
• The “barbell” hiring model — pairing AI-native early-career hires with experienced judgment — and why she believes the “AI is killing entry-level jobs” narrative is overstated
• Why discipline and focused vision matter more than ever now that building and discarding prototypes costs almost nothing
• Why enduring skills — agency, ownership, curiosity — will separate the top 1% of workers from everyone AI brings to “upper middle”

If you’re navigating a pivot, rethinking how your team builds, or trying to figure out whether AI adoption is changing your org for better or worse, this conversation is a candid look at what that process actually requires.

Janie Lee, who leads product at Abridge, joins Greg Shove to explain why “quality versus speed” is a false trade-off in AI-native product development — and what it actually takes to build trustworthy AI in the highest-stakes, most regulated industry there is.

Janie built her product career at Box, Opendoor, and Loom before joining Abridge, a healthcare AI company live at over 300 health systems that turns the patient-clinician conversation into clinical notes, billing codes, and proactive care recommendations. She describes healthcare AI as “hard mode on hard mode” — where a 5% error rate isn’t just a bad outcome, it’s a patient safety risk.

In this episode, Janie and Greg discuss:

• Why the quality-versus-speed trade-off has become a false choice, and how smaller, AI-leveraged teams are shipping at what used to take 15-20 people
• How Abridge balances deep interoperability with Epic and other EHRs instead of trying to bypass them
• Hiring for taste and judgment through live, unscripted problem-solving exercises instead of traditional interviews
• Why experience and “AI-native” are no longer a trade-off — both are now table stakes
• Setting inference budgets per clinical encounter to balance innovation with cost efficiency
• Fighting “AI work sloth” with a simple standard: would you put your name on this?
• Why headcount growth no longer scales linearly with business growth, and how Abridge rethinks hiring from first principles
• Advice for product leaders and CEOs on building AI-native teams in the next 30 days

If you’re building AI products in a regulated, high-stakes industry — or trying to figure out how much to trust AI with real consequences — this conversation is a grounded look at what that actually requires.

Garima Kapoor, co-founder and co-CEO of MinIO, joins Greg Shove to explain how a 200-person, decade-old infrastructure company mandated AI use across every function — and what it’s learned about measuring the return.

MinIO builds the object storage and memory layer that enterprise AI runs on, founded on the bet that most enterprise data would live outside the public cloud. The company raised $100 million in a 2022 round it has never had to touch, is cash-flow positive, and has grown from 40 to over 200 people, with most of that hiring in go-to-market roles over the last two years.

In this episode, Garima and Greg discuss:

• Why MinIO mandated AI use company-wide — “either you use AI, or this isn’t the place for you” — and what happened when they rolled it out
• The claim that some engineers are 100x more productive with AI, and why that number isn’t uniform across the team
• Cutting time-to-market for complex backend infrastructure from roughly 12 months to about one month
• Managing an inference spend in the millions of dollars annually, and why MinIO is already holding executives accountable for token budgets, not just headcount budgets
• Why she thinks there’s no single “primary” AI model — using Claude and Codex for different workloads depending on the task
• Building internal AI tools via MCP servers to connect sales, calendar, and CRM data for faster onboarding and management
• Hiring and interviewing in an AI-saturated world — screening for curiosity and clarity of thinking over raw AI fluency
• Running MinIO as co-CEO, with a spouse among her co-founding team, and how she thinks about AI’s role in her own kids’ education

If you’re an executive trying to figure out how to measure AI’s return on investment — not just adopt the tools, but hold teams and budgets accountable to them — this conversation is a real-world look at how one profitable, fast-scaling company is doing it.

Elia Wallen, founder and CEO of Engine, joins Greg Shove to explain how a 1,000-person, 11-year-old corporate travel company is retrofitting itself for AI in public — and posting results most AI-native startups would envy.

Elia dropped out of college after one semester, built a corporate housing company into the third-largest in the country, then spun off an internal booking tool into Engine — now valued at $2.1 billion with 33,000 customers, over a million travelers, and profitable. Unlike many AI transformation stories, Engine isn't a startup unburdened by legacy systems — it's an incumbent with real headcount and process debt, actively rebuilding itself.

In this episode, Elia and Greg discuss:

• How Engine's engineering teams shrank from 7 to 3 people while shipping 5–10x faster
• Why over half of customer support cases now resolve with no human involved
• Running "applied AI teams" embedded across marketing, sales, and finance
• Why Elia believes the future favors fewer management layers and more deep specialists
• Hiring for resilience and testing every candidate — including sales reps — on AI fluency
• Acquiring a 30-year-old travel management company instead of banking AI-driven margin
• Managing inference spend at the corporate level before it becomes unmanageable
• What a thousand-person company with 420 open jobs says about where AI transformation actually lands

If you're leading an incumbent business trying to figure out how much to change, how fast, and who to hire for it, this conversation is a real-time look at what that retrofit actually looks like.

Most AI agents are quietly bankrupt — Ilan Zerbib explains why giving them real spending power, not just API keys, is the missing piece of the AI economy.

Ilan Zerbib, founder and CEO of Sapiom, joins Greg Shove to explain why AI agents need their own payment infrastructure — and why most agentic products today are quietly bankrupt.

Ilan built his first company, Earny, running billions of automated bots to recover consumer refunds — a preview of the infrastructure problems agents face today. After Earny's acquisition and five years leading payments engineering at Shopify (Shop Pay, $100B+ in GPV), he founded Sapiom to solve a core problem: the internet's payment and API infrastructure was built to block automation, not enable it.

In this conversation, Ilan and Greg cover:

• Why agents need spending power, not just API keys, to complete real-world tasks
• The difference between the consumer agent economy and the much larger machine-to-machine economy (agents paying agents for APIs and services)
• How Sapiom's governance layer lets businesses give agents autonomy within controlled spending limits
• Why most AI agents today are "bankrupt" — and how cost, model routing, and failure rates determine margin
• Building as a solo founder: raising $51M across three rounds in 12 months, and running a 30-person team without traditional engineering hierarchy
• Why full company automation (80-90%) doesn't mean smaller teams — it means far more ambitious scope
• What it takes to build category-defining, super companies in the AI era

If you're building AI agents, thinking about agentic infrastructure, or trying to figure out how machines will transact in the emerging AI economy, this episode is a blueprint from someone building the rails.

"Human in the loop" is supposed to be the safe, responsible way to use AI. Turns out it might be the worst.

Theoretical neuroscientist and seven-time founder Dr. Vivienne Ming joins host Greg Shove on Supercompanies to break down what happens to human intelligence when AI can answer almost anything.

In an experiment with UC Berkeley students using AI to forecast real-world events, Vivienne uncovered three distinct ways people work with AI: automators who just accept whatever the AI tells them, validators stuck in a "human in the loop" pattern that performs worse than either humans or AI alone, and a rare group she calls cyborgs - people who blend with AI so effectively you can't tell who made the decision, and who came close to matching expert-level forecasters.

The traits that predicted who became a "cyborg" - curiosity, intellectual humility, and perspective-taking - raise bigger questions about how we raise kids and design schools in the AI era, which Vivienne explores in her new book, "Robot-Proof."

Supercompanies is back with all new episodes!

Watch the Video:

https://www.youtube.com/@SupercompaniesPod

Before Fanvue hit a $200 million run rate, Joel Morris and his co-founders were five months from running out of cash.

Fanvue co-CEO Joel Morris joins Supercompanies host Greg Shove to explain how a teenage YouTuber who uploaded for 600 days before getting any traction built a creator monetization platform now running at a $200 million revenue run rate with 130 employees.

Morris shares how Fanvue came within five months of running out of cash, the mindset that got him and his co-founders through it, and how one investor meeting and a later $22 million raise turned things around. He also talks about how Fanvue runs internally on Claude, why AI fluency is now a hiring pillar, and the company's push toward $1 billion in revenue.

Mentioned in this episode:

Ryan Holiday, Daily Stoic: https://dailystoic.com

Connect with Joel Morris:

LinkedIn: https://uk.linkedin.com/in/joel-morris-jmx

X: https://x.com/JoelXMorris

YouTube: https://www.youtube.com/channel/UCYz27J7fd8lK_FKKTal_I-g

Fanvue: https://fanvue.com

Subscribe to Supercompanies on YouTube: https://www.youtube.com/@SupercompaniesPod

Sign up for the Supercompanies newsletter: https://www.supercompanies.ai/

Why does AI adoption hit a wall inside large organizations? Brice Challamel joins to unpack what he's learned while leading people-first transformation at orgs like Moderna, and now at OpenAI as Head of AI Strategy and Adoption.

Brice shares his five components for supporting transformation, and what playfulness has to do with making AI stick. Plus: why Brice thinks the AI layoff narrative is wrong, and his case for a Nintendo as a super organization.

Watch this episode on YouTube here: https://youtu.be/fA80GvnAxCk

Links:

Brice's LinkedIn

Power of Why newsletter

AI is in the workplace. As companies adopt new workflows, new conversations are happening and new concerns are arising. How do AI leaders meet the moment and work with their teams to create clarity?

Taylor Malmsheimer, Section co-founder and COO, joins to share what she's learned speaking with hundreds of execs about their AI adoption efforts. Taylor walks Greg through the main types of questions that arise, and how leaders can empower their managers and employees to navigate these transformational moments.

10 Tough Questions Deck

Supercompanies.ai

How does a legacy tech veteran pivot to AI? Sid Sheth joins to share how Claude Code changed everything at d-Matrix almost overnight, what happens when your engineers are generating more code than they can validate, and why Sid appointed his co-founder as company AI czar. Plus: why Sid tells every new grad to ditch the generalist playbook, and how he's using AI in his personal life.

Links:

Sid's LinkedIn

d-Matrix