AI in the Real World with Foundation Capital

The founders building with AI and the researchers behind it. We explore how AI is reshaping real products and real businesses, what it takes to make it work, and the learnings along the way.

AI in the Real World with Foundation Capital is a technology podcast hosted by Joanne Chen, with 15 episodes on record and a Required Pod Score of 80. PitchCentric scores this show on Booking Probability, Listen Score, and live audience signals refreshed every 24 hours.

Joanne Chen hosts AI in the Real World with Foundation Capital, a technology show with 15 episodes published.

Our AI reads these to draft pitches. Use them as grounding for a pitch that cites a real guest and a specific topic.

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  Episode #15

  How AI agents are automating the CFO's office | Ajay Krishna, Co-Founder and CTO, Maximor

  Sep 9, 202617 min

  What does it take to make AI agents you can actually trust with your books? Ajay Krishna Amudan, co-founder and CTO of Maximor, joins Foundation Capital partner Jaya to talk about building an autonomous finance platform for the office of the CFO. Maximor’s agents handle revenue recognition, month-end close, reconciliations, AR, AP, and billing ops in ways that finance teams can actually audit. Instead of fighting to become the next system of record, Maximor chose to become ERP-agnostic, working across whatever ERPs a customer already has, even if they have different data models. Ajay and Jaya dig into the technical decisions behind creating trustworthy AI for finance: an "adversarial agent architecture" where agents audit each other like a human auditor would, with built-in verifiability. They talk about when post-training actually improves quality (versus just cost), along with how roles are shifting: finance teams are becoming custodians of policies and judgments as agents take on more of the day-to-day work. The discussion closes Ajay's hardest founder lesson and his vision for the future, where Maximor becomes every company's central decision-making brain. What we covered: 00:00 - Cold open, Ajay on what a fully re-architected finance function looks like 00:40 - Intro to Maximor 01:18 - Maximor’s design decision to stay ERP-agnostic 03:13 - Treating finance as a decomposable problem and building for verifiability by design 04:05 - Adversarial agents explained 06:24 - Why "doing the work" is the easy part in finance 06:56 - How work patterns are changing with AI-powered finance ops 08:13 - Lessons from watching OpenAI/Sam Altman up close at Microsoft 11:07 - Founder lessons, including the shift to full-stack ownership 13:05 - The promise of continual learning 15:15 - Maximor's 5-10 year vision as a "company brain"

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  Episode #14

  AI, agents, and the future of e-commerce | Zack Peng, Co-Founder and CEO, Seel

  Aug 27, 202629 min

  Seel started with a simple but powerful insight: if you could predict returns and refunds, you could offer shoppers the option to make otherwise final-sale purchases refundable. That initial financial product became a wedge into a bigger problem: after realizing that more than 70% of the support requests Seel received had nothing to do with returns or refunds, the startup began taking on more of the work that happens post-purchase. Zack explains how today AI handles tasks human teams structurally can’t do in real time, like updating an address across disconnected systems or issuing instant refunds before a return even ships back. Zack and Rodolfo also dig into why “insurance-like” options perform so well at checkout and what it actually takes to build agentic systems that operate at scale. They close with a candid look at Zack’s own founder journey, including a moment where the company deliberately walked back a million dollars in revenue to bet on a completely different path. What we covered: 00:00 – Cold open: Zack on the psychology of hitting product-market fit 00:48 – Intro: Rodolfo introduces Zack 01:31 – What Seel does 03:26 – The origin insight: returns/refunds are 10-20% of GMV 07:24 – How Seel transformed after noticing 70% of support tickets had nothing to do with returns 11:08 – How agents enable new experiences not previously possible with human support reps 18:23 – The evolution of Seel's four Agencies 23:00 – Zack’s vision of a one-person billion-dollar brand 24:20 – The future of agentic shopping 27:54 – Seel’s two-time zero-to-one story Links:  Seel: https://www.seel.com

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  Episode #13

  Maximizing intelligence per watt | Avanika Narayan & Jon Saad-Falcon, Stanford AI CS Ph.D.s

  Jul 29, 202626 min

  The next phase of AI adoption will depend on making intelligence more efficient, not just more capable. In this episode, Jaya is joined by Stanford Ph.D. researchers Jon Saad-Falcon and Avanika Narayan to discuss how local and hybrid AI can help bring these tools to billions more people. Much of today’s LLM inference runs through frontier models in centralized data centers. But Jon and Avanika argue that many tasks can already be handled by smaller, open-weight models, including models running on local hardware. Their concept of “intelligence per watt” measures how much task performance a given model-and-hardware configuration delivers relative to the power it consumes. They also discuss OpenJarvis and Minions, two projects that explore how AI systems can use local models when possible and turn to cloud models when more capability is needed. From there, the conversation turns to what this shift could mean for OpenAI, Anthropic, and other frontier labs. As open-weight models improve and inference becomes cheaper and more distributed, Jon and Avanika argue that low switching costs and limited lock-in could put pressure on the labs’ margins. They also explain why enterprises should be careful about handing proprietary data and decision-making processes over to model providers. They close with advice for Ph.D. students and academic researchers: rather than trying to reproduce the frontier labs’ work with fewer resources, choose problems where original ideas matter more than scale. What we covered: 00:00 – Cold open 00:52 – Welcome and guest introductions 02:03 – Why intelligence efficiency matters 03:22 – Defining intelligence per watt 05:46 – Overview of OpenJARVIS and Minions Projects 08:00 – Industry adoption of intelligence per watt metrics and hybrid execution strategies 09:10 – Why local capabilities will shift demand away from centralized cloud infrastructure toward fragmented, specialized hardware 12:35 – Technical drivers for local AI 12:56 – How tokenmaxxing complements efficiency research 14:36 – Why proprietary AI providers face revenue risks as open-weight models compress in size and rival frontier capabilities 17:15 – The real moats in AI today 18:23 – Advice for enterprise CIOs 22:15 – Hot takes 23:48 – The importance of US public policy and aligning public benefit with AI advancements 24:45 – Advice for PhD students and researchers 25:56 – Closing thoughts Links: Intelligence per Watt: Measuring Intelligence Efficiency of Local AI - https://arxiv.org/abs/2511.07885 OpenJarvis: Personal AI, On Personal Devices - https://arxiv.org/abs/2605.17172 Minions: Cost-efficient Collaboration Between On-device and Cloud Language Models - https://arxiv.org/abs/2502.15964

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  Episode #12

  The great reorg is just getting started | Azeem Azhar, Founder of Exponential View

  Jun 18, 202658 min

  In this episode, Joanne is joined by Azeem Azhar, founder of Exponential View, to unpack what it will take for AI to move from individual productivity gains to true org-level transformation. The conversation builds on Joanne and Leo’s recent essay on the great reorg, which argues that AI’s full impact will only arrive when companies redesign how work gets done from first principles. Today, AI is already helping individuals work faster, but many organizations are struggling to translate that into gains at the team or company level. Azeem calls this problem “congestion.” When one part of the company speeds up, the next downstream process becomes the bottleneck. As AI takes on more work inside organizations, it reveals where they are too slow, too rigid, or too dependent on processes built around human constraints. Joanne and Azeem explore what it will take to build AI-native companies, from new human roles like system architects, validators, and accountability owners to new tools designed for agents rather than humans. For founders, Azeem argues that the most important metric may be cycle time: how quickly a company can learn from customers, ship, adapt, and repeat. The org chart is being redrawn, and the companies that thrive will be the ones that learn how to collaborate with agents instead of simply bolting AI to old workflows. What we covered: 00:00 Cold open: Why this moment favors startups 00:28 The great reorg, explained 02:16 AI’s productivity paradox 03:36 Congestion as the new bottleneck 06:30 What congestion looks like in practice 09:16 Bottlenecks across compute, healthcare, and drug discovery 13:44 The new human roles in AI-native organizations 15:33 The growing importance of accountability as agents do more work 22:09 The future of expertise 27:27 Are you managing the agents, or are the agents managing you? 31:28 What remains human in venture capital 33:08 Throwing out old assumptions about process 37:12 Designing for agents, not humans 39:19 Cycle time as a key AI-native metric 44:15 The mental model that stalls AI adoption in large orgs 48:27 Azeem’s advice for early-stage founders 51:19 New startup opportunities in the great reorg 55:08 What changes in the next three years Links: Exponential View - https://www.exponentialview.co The great org: A human’s guide - https://foundationcapital.com/ideas/the-great-reorg

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  Episode #11

  Making healthcare move faster: Trey Holterman, Co-founder & CEO, Tennr

  May 19, 202630 min

  Tennr co-founder and CEO Trey Holterman joins Joanne to talk about what it takes to make AI work in healthcare, and why the answer is not simply "better models." Tennr focuses on patient flow: the process of getting patients from one provider to the next with the right records, insurance information, clinical context, and scheduling in place. It's a problem that sits in the messy middle of healthcare, where delays, denials, missing documentation, and manual workflows are the norm. Today, its platform processes referrals and intake documentation for roughly 10% of Americans each year. Healthcare has long been considered too hard for startups to crack. But advances in LLMs have made it possible to build products that can handle the unstructured, edge-case-heavy nature of healthcare administration. As Trey explains, those advances have created an opening for founders willing to stay close to customers and learn the market directly. Trey also gets into his first-time founder lessons and Tennr's path to scale. In the early days, he and his co-founders spent too much time building in isolation, then swung too far the other way by saying yes to nearly everything customers asked for. At one point, they were approaching $1M in revenue, only to realize they were spreading themselves across too many use cases. The decision to refocus resulted in a short-term hit to their revenue, but became the moment the team locked in and found product-market fit. Along the way, Trey reflects on the discipline of questioning your own assumptions, how Tennr uses AI internally, and what the patient experience could look like if Tennr realizes its mission of making healthcare move faster. Chapters: 00:00 Cold open 00:35 What Tennr does 03:20 Tennr's road to PMF 07:40 Startup advice that is true but hard to follow 11:30 Going against conventional wisdom: why selling to healthcare was worth it 13:20 Why Tennr is not an AI company 16:21 What it takes to get AI to work in healthcare 17:44 How Tennr thinks about building an industry-level context graph 19:30 Why focused product companies beat general-purpose labs 21:10 The importance of fresh perspectives in regulated industries 22:40 Founders Trey looks up to 24:27 How Tennr uses AI internally 27:20 AI's impact on productivity and the future of org charts 28:34 What Tennr could look like in five years

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