Question 1

How do venture firms use AI in their own operations?

Accepted Answer

_AI in sourcing, diligence, portfolio construction, and founder support._

> AI — it's who controls the data and who builds products that businesses and people actually use.

— [Stargate: The $500 Billion Message To China — The Future Of AI Regulation](https://www.forbes.com/sites/lutzfinger/2025/01/22/stargate-the-500b-message-to-china---the-future-of-ai-regulation/) · _Forbes_

Question 2

Should enterprises invest equally in predictive and generative AI, or focus on one?

Accepted Answer

_*Predictive and generative AI should not compete for the same budget*_

Enterprises should really be investing at least as much into the potential value from predictive use cases as generative. Right now it's becoming kind of a zero-sum game, with a lot of budgets moving away from predictive. But predictive is the technology you turn to for improving your existing large-scale operations. These are very different kinds of value propositions. They should compete no more than water parks and ski resorts, but they totally are competing. We need to be doing both. Most of predictive's potential is still on tap.

— [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 3

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 4

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 5

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 6

What is BioAI?

Accepted Answer

_AI applied to biology — new molecules, new medicines._

> AI, Roeding said, is like suddenly having a bazooka in your toolbox. That means though, you still have to aim, and you still have to pull the trigger.

— [DNA Is Becoming Programmable. Curing Cancer With AI.](https://www.forbes.com/sites/lutzfinger/2026/05/25/dna-is-becoming-programmable-curing-cancer-with-ai/) · _Forbes_

Question 7

Why did Elon Musk try to buy OpenAI, and what was he really trying to accomplish?

Accepted Answer

_A calculated disruption disguised as an acquisition attempt._

Elon was likely making a power move to disrupt Sam Altman's fundraising. OpenAI is transitioning from nonprofit to for-profit, and by putting a price tag on the nonprofit side, Elon made Sam's fundraising deal look bad. Sam is trying to raise 40 billion at a 300 billion valuation. Elon's offer was probably driven by pettiness, but also because his own AI efforts, Grok and XAI, haven't gained traction like Mistral, Anthropic, or DeepSeek. Some saw it as an attempt to catch up, similar to when he called for an AI pause, which people interpreted as buying time.

— [SZ 159: AI, Innovation, & Risk Ft. Lutz Finger](https://www.lutzfinger.com/transcript/2025-04-04-sz-159-ai-innovation-risk-ft-lutz-finger/) · _Silicon Zombies_

Question 8

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 9

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_

Q&A, Investment

How do venture firms use AI in their own operations?

Should enterprises invest equally in predictive and generative AI, or focus on one?

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

What is BioAI?

Why did Elon Musk try to buy OpenAI, and what was he really trying to accomplish?

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?