Question 1

How does Catalog prevent large language models from hallucinating when retrieving enterprise information?

Accepted Answer

_Stopping hallucinations by feeding LLMs the right information first._

The way to avoid hallucination is to stop the model from reaching into the model layers for information. If you give it structured or unstructured information in raw format and then ask it to summarize or list, it gives you exactly what you provided. Without that context, it makes things up because it's doing probabilistic retrieval from its training. This is retrieval augmented generation. You get the information first, augment the prompt with it, then generate the response based on that specific context.

— [Large Language Models: one interface to rule them all?](https://www.lutzfinger.com/transcript/2023-08-15-large-language-models-one-interface-to-rule-them-all/) · _The Edge_

Question 2

How does Catalog use large language models to simplify enterprise software access?

Accepted Answer

_One interface to access all your enterprise tools at once._

Catalog uses LLMs to create a unified interface for all enterprise software. Instead of navigating multiple systems, employees enter a single prompt. The LLM understands the query, identifies which data repository holds the answer (like Workday for payslips), converts the request into an API call, performs deterministic retrieval, and returns the result. We don't use LLMs for data retrieval itself, only for reasoning and synthesis. This compresses workflows from multiple surfaces and ten to fifteen clicks down to one query and two or three clicks.

— [Large Language Models: one interface to rule them all?](https://www.lutzfinger.com/transcript/2023-08-15-large-language-models-one-interface-to-rule-them-all/) · _The Edge_

Question 3

How does LegalOS use LLMs to make legal knowledge more accessible?

Accepted Answer

_LLMs translate legal knowledge into something everyone can use._

LegalOS originally built a no-code visual diagramming tool that lets legal experts encode their knowledge in a structured, machine-readable way. It's like a programming language for law. In recent months, we've pivoted to use LLMs much more heavily to make that legal knowledge accessible. LLMs provide a higher level of abstraction for automations. They let us map open-ended legal requests to structured tasks without requiring someone to manually encode every single rule. The LLM acts as an interface or translator, so humans don't have to understand legalese directly but can still interact with legal text.

— [Will LLMs scale and revolutionize the legal space?](https://www.lutzfinger.com/transcript/2023-06-15-will-llms-scale-and-revolutionize-the-legal-space/) · _The Edge_

Question 4

How has natural language processing changed since 2015, and why does that matter for legal tech?

Accepted Answer

_From counting words to real understanding in less than a decade._

I quit my PhD in 2015 and co-founded a natural language processing company. Back then, the state of the art was basically counting words in a glorified way. You had an idea of what the user might be talking about, but you couldn't map it to something with certainty. You had to collect your own data and train specialized models to do one narrow task. LLMs changed that. You can use foundational models out of the box without fine-tuning. They solve many tasks already. Understanding text and mapping open-world requests to closed sets of tasks became way more accessible.

— [Will LLMs scale and revolutionize the legal space?](https://www.lutzfinger.com/transcript/2023-06-15-will-llms-scale-and-revolutionize-the-legal-space/) · _The Edge_

Question 5

If agents are the future, why won't one giant model just do everything?

Accepted Answer

_The moat is the workflow, not a bigger model._

Because the durable approach is the opposite: smaller, faster, specialized models orchestrated together, not one ever-bigger general model. Even OpenAI orchestrates the long tail of consumer search with smaller models, because a big model would be far too slow. For founders this is the key warning: if your agent's next step is something generic, like deciding whether a calculator is needed, that is the long tail, and the big models will absorb it, just as Google eventually swallowed the specialized search engines. The defensible business is one where the founder knows the specific stepwise workflow of a domain and chains the right models and rules to serve it.

— [The Edge: “AI Agents: The Future of Work?”](https://www.lutzfinger.com/transcript/2024-12-12-ai-agents-the-future-of-work/) · _Podcast_

Question 6

Is OpenAI going bankrupt?

Accepted Answer

_Lutz's take on OpenAI economics and moats._

> OpenAI — "Experts predict bankruptcy" — was attention-grabbing, a deeper look reveals that the company has simply seen a drop in its users. Should you fear ChatGPT will disappear? No, quite the opposite.

— [OpenAI Isn't Going Bankrupt, But It Has A Business Model Problem](https://www.forbes.com/sites/lutzfinger/2023/08/18/is-openai-going-bankrupt-no-but-ai-models-dont-create-moats/) · _Forbes_

Question 7

What is the competitive advantage of LLMs for business?

Accepted Answer

_How enterprises can build durable AI advantage._

> AI-driven businesses have a combination of either a model moat, a data moat, or the brand moat. The new LLMs, like OpenAI's model, can give an advantage in all areas, but it is by no means a given. Since OpenAI launched ChatGPT, large language models have entered the fantasies of founders worldwide. Every day about a dozen new startups announce their AI-enabled feature.

— [What Is The Competitive Advantage Of LLMs Like ChatGPT For Your Business? Three Takeaways.](https://www.forbes.com/sites/lutzfinger/2023/04/04/what-is-the-competitive-advantage-of-llms-like-chatgpt-for-your-business-three-takeaways/) · _Forbes_

Question 8

What is the missing moat in AI?

Accepted Answer

_Eval data as the durable moat._

> What wins the AI race? UX?

— [The Missing Moat In AI: Your Eval Data](https://www.forbes.com/sites/lutzfinger/2026/05/26/the-missing-moat-in-ai-your-eval-data/) · _Forbes_

Question 9

Why don't AI models create moats?

Accepted Answer

_The structural reasons foundation models commoditize._

> AI does not easily create moats. Early in the LLM hype, I wrote that a model moat does not exist for OpenAI — such a moat also did not exist in previous Machine Learning waves. No Model Moat: transformer models are math.

— [DeepSeek - New Economic Rules And Regulatory Challenge](https://www.forbes.com/sites/lutzfinger/2025/01/29/deepseeknew-economic-rules-and-regulatory-challenge/) · _Forbes_

Question 10

Why is eval data a moat?

Accepted Answer

_Evaluation data as the strategic differentiator._

> All of them matter, and all of them are being fought over. But the real value may sit in the one thing OpenAI, Anthropic, and Gemini have not yet made central: your own eval data. Sure, there is the occasional thumbs-up button. But who is really using that signal?