AI in the Real World with Foundation Capital

In co-founding Fulcrum, Arjun was inspired by an idea he learned from his entrepreneurial father: that it’s worth the pain and the effort to create something completely new. He discusses how that ambition shapes what Fulcrum chooses to build, and what it passes on. With AI making so many customer problems easier to solve, he has to resist letting the loudest request dictate the product roadmap. Some automation tools could bring in revenue today but become redundant as AI improves over the next few years.

Arjun also talks candidly about how insurance earned its reputation for being slow to adopt technology due to years of overhyped tools that didn't deliver and left the industry burned. In the future, Arjun imagines insurance systems will be connected directly to a business's core systems, so that adding new vehicles to a fleet or selling a building updates coverage automatically. Brokers will focus on relationships and advice, not on paperwork.

He closes with lessons learned about leading a team. He shares what it means to create a culture of "extreme ownership" while scaling from five people to forty in a few months, and how having fun is a core part of his leadership values.

0:00 – Cold open: Arjun on the hidden rigor of insurance and the pain tolerance behind building a company

0:56 – Introductions

1:08 – What Fulcrum does and why a $2T "commodity" market is still so inefficient

3:01 – Arjun’s journey from McKinsey to building the "Ferrari of insurance"

3:39 – What Arjun learned from his entrepreneurial father

8:09 – How AI changes insurance

10:04 – How Fulcrum grew fast in a traditionally conservative industry

13:47 – Building something that lasts beyond workflow automation

16:49 – The future of insurance: automating placement and ongoing coverage maintenance

22:01 – The founder failure Arjun is most afraid of repeating

26:35 – Arjun’s core values as a leader

27:01 – Fulcrum’s company culture: extreme ownership and having fun while scaling

Arjun Mangla: https://www.linkedin.com/in/arjunmangla

Fulcrum: https://www.withfulcrum.com/

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"

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.

Seel: https://www.seel.com

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.

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

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:

Links: 

Exponential View - https://www.exponentialview.co

The great org: A human’s guide - https://foundationcapital.com/ideas/the-great-reorg

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.

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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:

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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:

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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:

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Welcome to AI in the Real World! In this episode, Foundation Capital Partner Sid Trivedi speaks to Chris "Tito" Sestito, the co-founder and CEO of HiddenLayer.

HiddenLayer is a cybersecurity platform purpose-built for AI systems. Tito's journey into AI security began at Cylance, where he led the response to one of the first real-world AI attacks. This experience led him to start HiddenLayer, which recently raised a $50M Series A.

In this conversation, Tito shares his insights into the evolving AI threat landscape, from shadow AI to the growing arsenal of strategies that bad actors are using to target generative models. He explains why data poisoning is one of the most urgent threats facing humanity, and why safeguarding AI will define the future of cybersecurity.

Learn more about HiddenLayer here: https://hiddenlayer.com

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