Question 1

How can startups use large language models to improve their products?

Accepted Answer

_Simplify interfaces and gain user insights through language_

As a startup founder, think about which interface will be changed by language input. Whenever users needed to use their mouse or learn complex clicking and dragging, you suddenly have the ability to let them do it via language. This simplifies the interface dramatically. Additionally, language prompting gives you more feedback. Before, you'd create an interface and see how users clicked buttons and moved around without really knowing their intentions. Now with language interfaces, you actually see what they're searching for and asking for. This gives you valuable input for building your product.

— [AI as an interface](https://www.lutzfinger.com/transcript/2023-04-24-ai-as-an-interface/) · _The Edge_

Question 2

How do you decide if AI is the right fit for your product problem?

Accepted Answer

_Not every problem needs AI; start with whether complexity demands it._

We created an AI fit analysis with portfolio companies. It starts with questions around precision and output quality, focusing on data quality. Then ask: Do you benefit from modeling complex decisions versus just a few? If it's a linear problem, a rule engine works. But if you have a complex decision space or nonlinear problems like search engines or recommendation engines, then it's time to deploy AI to find tiny clues. This doesn't always mean large language models. Sometimes simple regression analysis is good enough and more precise.

— [Building AI products: 5 lessons from our founders' workshop](https://www.lutzfinger.com/transcript/2024-08-01-building-ai-products-5-lessons-from-our-founders-workshop/) · _The Edge_

Question 3

How does Neuralens measure brain health non-invasively compared to current methods?

Accepted Answer

_Replacing skull drilling with light-based sensors for brain monitoring._

Currently, doctors literally drill a burr hole through your skull with a household drill and insert sensors through brain tissue to measure oxygen saturation, blood flow, and intracranial pressure. This invasive surgery is only done on 200,000 to 300,000 patients yearly, though over 10 million neurocritical care patients could benefit. Neuralens uses advanced methods of measuring how light interferes with brain tissue to quantify blood flow, blood volume, and intracranial pressure non-invasively. We can stick sensors on the head or shine a laser from 30 centimeters away to make these measurements.

— [Founder Coaching: Tips for your AI Journey](https://www.lutzfinger.com/transcript/2024-07-26-founder-coaching-tips-for-your-ai-journey/) · _The Edge_

Question 4

How does your company Gooder AI address the gap between technical metrics and business value?

Accepted Answer

_*Translating predictive model performance into profit and savings estimates*_

Gooder addresses one particular place where there's a need for a new category of software: the metrics. It's a business user console for understanding potential value and making decisions about deployment. Typically, data scientists present technical metrics like area under the curve, precision, or recall, but very rarely go to stakeholders and say if you deploy this model, it has potential value of this much profit or savings. We provide visualization where both dimensions are business relevant, like the decision threshold and business metrics like savings or profit, so stakeholders can see exactly what their options are.

— [The Value Translation Gap: AI's Deployment Problem](https://www.lutzfinger.com/transcript/2024-11-28-the-value-translation-gap-ais-deployment-problem/) · _The Edge_

Question 5

How does Zenith OS reduce costs in neuropsychiatry treatment?

Accepted Answer

_AI cuts neuropsychiatry costs by reducing required therapist headcount per session._

Neuropsychiatry treatments like ketamine and psilocybin for PTSD, depression, and anxiety are 30 to 40 percent more effective than SSRIs but remain inaccessible due to high human capital costs. Currently, two therapists must be present during treatment because patients are in a vulnerable state and there are sexual harassment concerns. Each therapist charges $100 to $150 per hour. Zenith OS uses AI to augment administrative tasks and help with patient recommendations, allowing clinics to operate with one therapist instead of two, significantly reducing costs and expanding access to these groundbreaking treatments.

— [Founder Coaching: Tips for your AI Journey](https://www.lutzfinger.com/transcript/2024-07-26-founder-coaching-tips-for-your-ai-journey/) · _The Edge_

Question 6

Should founders focus on building new AI models or developing applications with existing technology?

Accepted Answer

_Applications beat innovation as VCs shift from research to practical tools._

Take the existing capabilities and apply them. Do we really need more innovation, or is it fine to say large language models are very good, you can chain them, combine them with traditional machine learning techniques, and build an application around it? I want more practicality, more tooling around it. The VC world actually sees that the appetite to go on risky, more researchy projects has gone down. VCs want more applications. Founders should focus on taking existing capabilities and applying them rather than pursuing foundational model innovation.

— [AI Gold Rush: Is the Market Overheating?](https://www.lutzfinger.com/transcript/2024-08-08-ai-gold-rush-is-the-market-overheating/) · _The Edge_

Question 7

What advantage do existing platforms like Google, Meta, and TikTok have in the bot space?

Accepted Answer

_Existing user access matters, but cross platform integration creates opportunity_

Companies with existing access to users and data will likely have the majority chunk of the market. While we are still figuring out style, entertainment value, and answer quality, TikTok, Google, Meta, X, and Snap are all putting out bots to monetize their existing access. They already have the right information and the right tools. However, there is an opportunity for cross platform bots that operate across different systems, like accessing both SAP and HubSpot, or working across Snap and LinkedIn, following users regardless of platform.

— [The future of AI bots: winning with personalization, conviction, and cross-platform integration](https://www.lutzfinger.com/transcript/2024-08-22-the-future-of-ai-bots-winning-with-personalization-conviction-and-cross-platform/) · _The Edge_

Question 8

What advice do you have for someone considering building a fintech or launching in Africa?

Accepted Answer

_Hard lessons from exploring Africa and fintech opportunities_

For Africa, most successful businesses are traditional, non-tech businesses like furniture or services. Building something successful requires being hands-on, spending 10 years in the country, and building local relationships. It's really a lifestyle question. For fintech, the space has been extremely hot over the last four or five years. I think you either need to go very close to the rails, like payment rails and banking APIs, or do something very verticalized like payments for healthcare in Europe connecting insurance refunds. Otherwise it's super difficult to create something that works in fintech.

— [AI Products: Why ‘Good Enough’ Beats Perfect](https://www.lutzfinger.com/transcript/2024-11-14-ai-products-why-good-enough-beats-perfect/) · _The Edge_

Question 9

What does it mean for a company to be AI-native versus just using AI?

Accepted Answer

_AI-native means AI is the core product, not just an enhancement_

An AI-native company means the primary product offering or value is driven by AI, not just enhanced by it. Netflix is a channel for videos that uses AI to suggest movies, but you didn't need AI for Netflix. LinkedIn is a social network that uses AI to connect people, but you didn't need AI for LinkedIn. These are not AI-native. Companies like 11 Labs, Midjourney, or Cursor are AI-native because AI is at the heart of what's being built, what's being sold, and the people you hire early on.

— [The Future of AI, with Martin Casado](https://www.lutzfinger.com/transcript/2025-03-20-the-future-of-ai-with-martin-casado/) · _The Keynote on AI_

Question 10

What is Runway at Cornell Tech and how does it support PhD entrepreneurs?

Accepted Answer

_A two-year paid fellowship helping PhDs turn research into companies._

Runway is an entrepreneurship fellowship at Cornell Tech designed to help PhDs commercialize their research. The program accepts five fellows per year and provides a two-year paid fellowship specifically to commercialize technology that you ideally developed during graduate school. It's a structured way to transition academic research into viable commercial products, giving you financial support and time to build your startup while maintaining ties to Cornell Tech's ecosystem and resources.

— [Founder Coaching: Tips for your AI Journey](https://www.lutzfinger.com/transcript/2024-07-26-founder-coaching-tips-for-your-ai-journey/) · _The Edge_

Question 11

What is the minimum viable quality concept when building AI products?

Accepted Answer

_Building with AI means accepting imperfection strategically, not chasing perfection everywhere._

We always ask ourselves how to build a product where AI imperfection is not a problem for the use case you're solving. This is the concept of minimum viable quality, not product, but quality, because AI is not always perfect. Many startups put AI in the product but not actually in the solution. The value creation comes down to whether you have a decision that's different because you use AI. For example, a scale reminding you about your diet is just an interface advantage, not real AI value creation.

— [Building AI products: 5 lessons from our founders' workshop](https://www.lutzfinger.com/transcript/2024-08-01-building-ai-products-5-lessons-from-our-founders-workshop/) · _The Edge_

Question 12

What problems with RAGs still need to be solved before widespread adoption?

Accepted Answer

_The observability and control challenges that remain in RAG systems._

RAGs don't completely stop hallucination yet. The model uses both the trained corpus and new data, but we don't have a very good solution to manage that difference. Who decides when new information should override training data? Is the LLM taking that decision or the RAG? We lack observability and control. For example, I created a document saying I'm Captain America, but ChatGPT rejected it as fake. What if that was actually true breaking news? Amazing startups will help create this observability, solve access control, and manage the difference between what the corpus was trained on and corporate datasets.

— [Are we one step closer to using LLMs in practice?](https://www.lutzfinger.com/transcript/2023-07-21-are-we-one-step-closer-to-using-llms-in-practice/) · _The Edge_

Question 13

Where does Ultimate AI see its competitive defensibility beyond just AI models?

Accepted Answer

_User experience and distribution trump data or model advantages_

I don't think there's a silver bullet or secret IP that guarantees you always win. On the data side, we stream live conversations from big companies in a secured way. We have hundreds of millions of proprietary conversations that competitors starting today don't have. But more important than data moats or technical moats is user experience and distribution. I love products that take 10 minutes to learn but years to master, like Shopify or Excel. If you build something easy to start but with depth, solid brand, and good distribution, that matters much more than some little advantage in the back office models.

— [How Ultimate evolved from hundreds of supervised models to UltimateGPT](https://www.lutzfinger.com/transcript/2023-05-12-how-ultimate-evolved-from-hundreds-of-supervised-models-to-ultimategpt/) · _The Edge_

Question 14

Why are radically lean teams the future?

Accepted Answer

_How AI compresses the team size needed to ship._

> But what hinders many of the large corporates to adapt them? >>Yeah, well, I think there's a lot of components there but in the end, it comes down to the size, and the politics, and the setup they built. Large companies are usually built to mitigate risk and focus on their core business. If there's a new technology that comes in that is not obviously threatening the core business, or opening a huge opportunity for the core business,…

— [Unit 5 Full Transcript](https://www.lutzfinger.com/ecornell/) · _eCornell course_

Question 15

Why are startups not yet widely adopting knowledge graphs with their LLM applications?

Accepted Answer

_Prompt engineering dominates now, but knowledge graphs will become essential at scale._

I am not seeing significant use of knowledge graphs at startups yet. There is heavy reliance on out of the box language models and significant investment in prompt engineering, both at startups and larger companies. The zeitgeist is to mine these wonderful resources rather than going upstream to drive the language models themselves. Knowledge graphs are not simple and are difficult to build, while prompt engineering and rule-based approaches are easier. Results from research literature take time to get absorbed by practitioners. Once startups scale or work on applications where accuracy is crucial, they will have to adopt knowledge graphs.

— [The synergy between LLMs and knowledge graphs](https://www.lutzfinger.com/transcript/2023-08-24-the-synergy-between-llms-and-knowledge-graphs/) · _The Edge_

Question 16

Why did you leave your healthcare company to start an AI e-commerce company?

Accepted Answer

_From healthcare prediction models to e-commerce AI startup founder_

I was president of a NASDAQ listed company in New York in the healthcare space, running product and technology for a third party administrator. We built predictive algorithms to reduce the burden of diseases for our members. I left to build a startup focused on fine-tuned large language models for e-commerce because I saw an opportunity. The technology behind it, transformer models, exists since 2017, but how everyone uses it creates new questions. I realized I could create value by knowing things the industry didn't know yet, specifically solving search and discovery problems in e-commerce with this new architecture better than anyone else.

— [After the Gold Rush, with Kurt Wagner](https://www.lutzfinger.com/transcript/2025-10-21-after-the-gold-rush-with-kurt-wagner/) · _The Keynote on AI_

Question 17

Why have AI-native startups historically struggled to succeed, and what changed with generative AI?

Accepted Answer

_Why AI startups failed until generative AI changed the economics completely_

For 30 years, AI has been great technology but terrible business. The economics were never there. AI could improve existing businesses by 20 percent, which is amazing for Google or LinkedIn, but a 20 percent improvement doesn't make a startup. The big change with generative AI is it brings the marginal cost of creation and language reasoning to zero. For example, getting a Pixar-style avatar used to cost 200 dollars from a designer. Today, a model generates it for one one-thousandth of a penny. That's a five order magnitude cost difference, an economic dislocation that super cycles are made from.

— [The Future of AI, with Martin Casado](https://www.lutzfinger.com/transcript/2025-03-20-the-future-of-ai-with-martin-casado/) · _The Keynote on AI_

Question 18

Why is now a good time for founders despite potential market corrections?

Accepted Answer

_Excess infrastructure investment creates opportunities for practical applications._

As we come into those crunch times, you as a founder can get access to all this hardware and compute power for a much better future. We have too much supply of compute, and some people say there's 600 billion or 300 billion in excess hardware. People have invested and have to get their money back, so you can get better access to talent and resources. Even though I believe a correction will happen, the actual value we will see is way bigger. Plus, compute costs should drop massively in the future.

— [AI Gold Rush: Is the Market Overheating?](https://www.lutzfinger.com/transcript/2024-08-08-ai-gold-rush-is-the-market-overheating/) · _The Edge_

Question 19

Why weren't traditional banks serving the underserved population before Varo?

Accepted Answer

_Economic incentives and outdated systems excluded millions from banking._

There are two main reasons, focusing on economic incentives. It was really difficult for banks to make money off consumers with low-value, high-frequency transactions. Banks perceived them as higher credit risk because they used backward-looking systems instead of monitoring real-time cash flow. Vulnerable populations are also more susceptible to fraud and scams, creating high costs for dispute processing. The economic incentives made it very difficult for incumbent institutions to effectively serve this large population. Technology was needed to solve this.

— [Disrupting Traditional Banking, with Colin Walsh](https://www.lutzfinger.com/transcript/2025-05-22-disrupting-traditional-banking-with-colin-walsh/) · _The Keynote on AI_

Q&A, Startups

How can startups use large language models to improve their products?

How do you decide if AI is the right fit for your product problem?

How does Neuralens measure brain health non-invasively compared to current methods?

How does your company Gooder AI address the gap between technical metrics and business value?

How does Zenith OS reduce costs in neuropsychiatry treatment?

Should founders focus on building new AI models or developing applications with existing technology?

What advantage do existing platforms like Google, Meta, and TikTok have in the bot space?

What advice do you have for someone considering building a fintech or launching in Africa?

What does it mean for a company to be AI-native versus just using AI?

What is Runway at Cornell Tech and how does it support PhD entrepreneurs?

What is the minimum viable quality concept when building AI products?

What problems with RAGs still need to be solved before widespread adoption?

Where does Ultimate AI see its competitive defensibility beyond just AI models?

Why are radically lean teams the future?

Why are startups not yet widely adopting knowledge graphs with their LLM applications?

Why did you leave your healthcare company to start an AI e-commerce company?

Why have AI-native startups historically struggled to succeed, and what changed with generative AI?

Why is now a good time for founders despite potential market corrections?

Why weren't traditional banks serving the underserved population before Varo?