How 3 CEOs Use AI to Run $10B in Companies | This Week in AI
Episode
30 min
Read time
2 min
Topics
Productivity, Investing, Startups
AI-Generated Summary
Key Takeaways
- ✓Large Tabular Models vs. LLMs: Enterprises running fraud detection, demand forecasting, and ETA prediction still rely on pre-LLM machine learning because LLMs handle unstructured data poorly. Fundamental's Nexus model uses a non-autoregressive architecture without positional encoding, meaning column order doesn't affect output — critical for deterministic predictions across billions of structured database rows.
- ✓Photonic Interconnects — 3x Training Speed: Lightmatter's optical fiber chips pack 16 wavelengths of light per fiber, delivering 1.6 terabits per second — equivalent to 1,600 homes' internet bandwidth simultaneously. Replacing copper with photonics allows GPU clusters separated by up to one kilometer to operate as a unified brain, cutting AI model training time by a factor of three.
- ✓AI Video Cost Economics: Generating one eight-second AI video clip currently costs $1–$2, making a personalized one-hour film approximately $700 — economically unsustainable at $15/month subscription pricing. Synthesia's thesis is that infrastructure cost reductions over the next few years will bring real-time, interactive, personalized video within standard consumer subscription price points.
- ✓Focus Beats Breadth in AI Product Strategy: Anthropic's rapid revenue growth — widely discussed at a Lightspeed founder retreat — is attributed to eliminating voice and video features entirely, concentrating exclusively on code generation and B2B enterprise sales with no freemium tier. OpenAI's discontinuation of Sora reflects the same lesson applied belatedly under competitive pressure.
- ✓Hyperscaler Custom Silicon Strategy: Google ($180B capex), Amazon ($200B+), and Meta are all building proprietary AI chips — Trainium, Inferentia, and MTIA respectively — because at that spending scale, custom silicon development is a rounding error. Founders building on NVIDIA's CUDA should explore abstraction layers enabling AMD and custom chip compatibility to avoid single-vendor hardware dependency.
What It Covers
Three CEOs — Jeremy Frankel (Fundamental, $255M Series A unicorn), Victor Ripparbelli (Synthesia, $4B valuation, 100M+ ARR), and Nick Harris (Lightmatter) — discuss large tabular models, AI video evolution, and photonic interconnects reshaping how enterprises process data and run AI infrastructure at scale.
Key Questions Answered
- •Large Tabular Models vs. LLMs: Enterprises running fraud detection, demand forecasting, and ETA prediction still rely on pre-LLM machine learning because LLMs handle unstructured data poorly. Fundamental's Nexus model uses a non-autoregressive architecture without positional encoding, meaning column order doesn't affect output — critical for deterministic predictions across billions of structured database rows.
- •Photonic Interconnects — 3x Training Speed: Lightmatter's optical fiber chips pack 16 wavelengths of light per fiber, delivering 1.6 terabits per second — equivalent to 1,600 homes' internet bandwidth simultaneously. Replacing copper with photonics allows GPU clusters separated by up to one kilometer to operate as a unified brain, cutting AI model training time by a factor of three.
- •AI Video Cost Economics: Generating one eight-second AI video clip currently costs $1–$2, making a personalized one-hour film approximately $700 — economically unsustainable at $15/month subscription pricing. Synthesia's thesis is that infrastructure cost reductions over the next few years will bring real-time, interactive, personalized video within standard consumer subscription price points.
- •Focus Beats Breadth in AI Product Strategy: Anthropic's rapid revenue growth — widely discussed at a Lightspeed founder retreat — is attributed to eliminating voice and video features entirely, concentrating exclusively on code generation and B2B enterprise sales with no freemium tier. OpenAI's discontinuation of Sora reflects the same lesson applied belatedly under competitive pressure.
- •Hyperscaler Custom Silicon Strategy: Google ($180B capex), Amazon ($200B+), and Meta are all building proprietary AI chips — Trainium, Inferentia, and MTIA respectively — because at that spending scale, custom silicon development is a rounding error. Founders building on NVIDIA's CUDA should explore abstraction layers enabling AMD and custom chip compatibility to avoid single-vendor hardware dependency.
Notable Moment
Victor Ripparbelli described Synthesia's upcoming private beta product: a real-time interactive video system where salespeople role-play with AI customers, receive live objection coaching, and watch diagrams of client tech stacks drawn dynamically — representing a fundamental shift from broadcast video toward interactive, game-like media formats.
Episode Transcript
Hey. It's Oliver from This Week in AI, the brand new podcast from the team at Twist. We're dropping a sneak peek right here in your feed to show you what we've been building. If you enjoy it, join the community at this week in a i.ai, or find us on Spotify, Apple Podcasts, or YouTube. I was talking to a friend of mine. She's an accountant, and she told me accounting is never gonna be replaced by automation. I'm like, what are you talking about? It's the first time we're really automating cognition as opposed to just automating the physical part of a job. 70% of them think they'll have a decrease in job opportunities. Only 30% of Americans are worried in the same poll about themselves, so they all think it's happening to somebody else. I do think that humans will wanna play status games. I think we'll find other jobs. I think we'll probably be doing less numerical and logical jobs. It feels like something very big is coming. The world doesn't appreciate that it's happening because most people are not very good at asking questions. You can taste the singularity at this point. I can't even imagine the end of this year is gonna be shocking. Thanks to our friends at PayPal, the exclusive sponsor for This Week in AI. Try the payment and growth platform that's trusted by millions of customers worldwide, PayPal Open. Start growing today at paypal open dot com. Alright, everybody. Welcome back to not This Week in Startups, not All In. This is a new roundtable I'm doing. It's called This Week in AI. It's in the name, folks. Every week, three amazing CEOs, just like on All In or the the, VC roundtable we do over at Twist. Three amazing CEOs who are actually building the future and me, an investor in the space and an entrepreneur, talk about the week's issues and sometimes the bigger picture issues. You can find out more about the podcast This Week in a I dot a I. Or if you want to see the YouTube channel, This Week in a i.ai/ youtube. And this is our seventh episode. It's 03/31/2026. Three amazing guests, with us today. Jeremy Frankel is here. He's the CEO and cofounder of Fundamental. They're building large tabular models for enterprises. They emerged from Stealth as a unicorn just, sixteen months after founding 255,000,000 series a led by Oak with participation from Valor Battery Salesforce. And more welcome to the program, Jeremy Frankel. So, Jeremy, explain what your company is doing and, how it's going so far. Things are going great. So what we are doing is we built a foundation model for tabular data. So what does that mean? So when you, you know, when people think about, you know, the AI boom or the AI revolution, everyone is thinking about, you know, LLMs and, you know, for good reason. Right? Like, you know, ChargeGPT, like, you know, create …
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Books, tools, and gear mentioned in this episode
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Gear
by NVIDIA
“Founders building on NVIDIA's CUDA should explore abstraction layers enabling AMD and custom chip compatibility to avoid single-vendor hardware dependency.”
by Amazon
“Google ($180B capex), Amazon ($200B+), and Meta are all building proprietary AI chips — Trainium, Inferentia, and MTIA respectively”
Products
company
“Anthropic's rapid revenue growth — widely discussed at a Lightspeed founder retreat — is attributed to eliminating voice and video features entirely, concentrating exclusively on code generation and B2B enterprise sales with no freemium tier.”
“OpenAI's discontinuation of Sora reflects the same lesson applied belatedly under competitive pressure.”
“Victor Ripparbelli (Synthesia, $4B valuation, 100M+ ARR)”
“Google ($180B capex), Amazon ($200B+), and Meta are all building proprietary AI chips — Trainium, Inferentia, and MTIA respectively”
“Google ($180B capex), Amazon ($200B+), and Meta are all building proprietary AI chips — Trainium, Inferentia, and MTIA respectively”
“Jeremy Frankel (Fundamental, $255M Series A unicorn)”
“Founders building on NVIDIA's CUDA should explore abstraction layers enabling AMD and custom chip compatibility to avoid single-vendor hardware dependency.”
“Google ($180B capex), Amazon ($200B+), and Meta are all building proprietary AI chips — Trainium, Inferentia, and MTIA respectively”
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