AI in the Real World with Foundation Capital

Joanne Chen
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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.

  1. 5 小時前

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

    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 like00:40 - Intro to Maximor01:18 - Maximor’s design decision to stay ERP-agnostic03:13 - Treating finance as a decomposable problem and building for verifiability by design04:05 - Adversarial agents explained06:24 - Why "doing the work" is the easy part in finance06:56 - How work patterns are changing with AI-powered finance ops08:13 - Lessons from watching OpenAI/Sam Altman up close at Microsoft11:07 - Founder lessons, including the shift to full-stack ownership13:05 - The promise of continual learning15:15 - Maximor's 5-10 year vision as a "company brain"

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

  2. 8月27日

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

    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

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

  3. 7月29日

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

    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

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

  4. 6月18日

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

    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

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

  5. 5月19日

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

    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

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

  6. 2025/08/11

    What robots can (& can't) do in 2025: Ken Goldberg, UC Berkeley & Ambi Robotics

    Welcome to AI in the Real World! In this episode, Foundation Capital Partner Joanne Chen sits down with Ken Goldberg, professor of engineering at UC Berkeley and co-founder of Ambi Robotics, a company applying AI-enabled robotics to transform the logistics industry. Ken has spent more than four decades working at the intersection of robotics and AI, focusing on one of the most persistent challenges in the field: how machines perceive and manipulate the physical world. Ken shares why tasks that seem trivial to humans, like picking up a glass or folding laundry, remain profoundly difficult for robots, and why the physical world introduces a level of uncertainty that can't be fully simulated. Our conversation also covers: What it would take to reach a ChatGPT moment in robotics Why simulation data is not enough without real-world grounding And why the next decade of robotics depends on combining cutting-edge models with good old-fashioned engineering Chapters: 00:00 Cold open: Why robotics still needs good old-fashioned engineering 03:46 Hype cycles and winters in robotics 05:08 Why folding laundry is still hard for robots 10:38 What robots are good at today 15:00 Automation and the rise of warehouse robotics 19:39 Can LLMs and generative AI work for robotics? 26:52 The limits of simulation data and the sim-to-real gap 29:44 Why humanoids are still far from practical 36:34 What founders need to know about robotics timelines 37:08 Why robots need grounding and exploration 39:00 Combining the power of LLMs with traditional engineering 40:42 Why Ken is optimistic about the future of robotics

    What robots can (& can't) do in 2025: Ken Goldberg, UC Berkeley & Ambi Robotics

  7. 2025/04/02

    The promise of State Space Models: Karan Goel, Co-founder & CEO, Cartesia

    Welcome to AI in the Real World! In this episode, Foundation Capital Partner Jaya Gupta sits down with Karan Goel, co-founder and CEO of Cartesia, a company pioneering the use of state space models (SSMs) for AI applications. Karan shared his journey from pursuing a PhD at Stanford to co-founding Cartesia with Albert Gu - taking their academic research on SSMs and turning it into a product that is initially focused on voice applications. Karan explained how SSMs differ fundamentally from transformers, offering a more efficient, memory-based architecture that processes information sequentially rather than in batches. This approach enables more human-like AI systems that can remember and adapt to new information in real-time. The conversation also covers: How SSMs offer significant efficiency advantages for applications like voice agents and on-device AI Why architecture innovation remains crucial alongside advances in data and training techniques The challenges and opportunities in the voice AI space His perspective on the open-weights vs. closed-model debate in AI Advice for researchers who want to become founders Chapters: 00:05 Cold open 01:47 Karan's founder journey 03:45 Transitioning from academia to entrepreneurship 05:17 The art of recruiting 09:22 Karan's vision for Cartesia 13:36 Innovation in AI architectures 16:33 Advantages of State Space Models (SSMs) 19:34 The false dichotomy: models vs. applications 21:17 Best practices for voice AI 25:10 Thoughts on the broader AI landscape 31:37 Open source vs. closed models 35:00 Karan's thoughts on fundraising 41:27 Advice for founders building AI startups 46:03 Upcoming trends that get Karan excited

    The promise of State Space Models: Karan Goel, Co-founder & CEO, Cartesia

  8. 2025/02/11

    Building the next generation of AI models: Rohan Taori, Researcher at Anthropic & Alpaca co-creator

    Welcome to AI in the Real World! In this episode, Foundation Capital Partner Jaya Gupta sits down with Rohan Taori, a researcher on Anthropic's multimodal pre-training team. Within the AI community, Rohan is best known for co-creating Alpaca, a project that demonstrated how fine-tuning Meta's LLaMA model could achieve ChatGPT-level performance for under $600. Rohan shares his journey from early work in computer vision at UC Berkeley to his Ph.D. at Stanford, where he explored methods for making AI more accessible. He explains the technical breakthroughs behind Alpaca, including self-instruct, a method that uses a stronger language model (OpenAI's text-davinci-003) to generate synthetic data that is then used to fine-tune a weaker model (Llama). This approach, which underpins Alpaca and its follow-up projects AlpacaFarm and AlpacaEval, illustrates how small-scale post-training can significantly enhance model performance. The conversation also covers: The promise and challenges of synthetic data for training AI models What it will take to build foundation models that are 100x better The future of multimodal AI and why it matters Why better evals are critical to the next wave of AI advances Chapters: 00:00 Cold open 1:40 Rohan's journey into AI research 04:50 Transitioning from vision research to LLMs 06:18 The story behind Alpaca 08:55 How Alpaca works 10:45 The AI community's reception of Alpaca 12:26 The evolution of Alpaca related projects 14:22 The role of synthetic data 19:38 Challenges in multimodal AI 24:31 Future of foundation models 30:00 Importance of data in AI 34:48 Staying up to date with the latest AI research 36:12 Advice for founders

    Building the next generation of AI models: Rohan Taori, Researcher at Anthropic & Alpaca co-creator
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簡介

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.

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