Question 1

Are large language models the path to AGI, or an off-ramp?

Accepted Answer

_LLMs are powerful and useful, but scaling them may be a detour rather than the road to general intelligence._

> I don't think LLMs are AGI, nor that fully scaling up LLMs is going to get you to AGI. I sort of agree with Yann LeCun that on the path to AGI, LLMs are an off-ramp. I still think we can get there by 2029 or even sooner, because the pace of progress is just insane and there are a lot of other techniques now being tried at smaller scale.

Source: [Rethinking the AGI Race, with Benjamin Goertzel (eCornell Keynote)](https://ecornell.cornell.edu/keynotes/view/K052826/)

Question 2

Can AI transcription tools introduce bias based on employee names?

Accepted Answer

_Meeting transcription and AI-driven contribution tracking may disadvantage non-Western names._

> Tools like Granola, Read.ai, Fireflies, and Avoma now routinely track talk time and participation, and firms like Amazon and Meta are folding AI-surfaced contribution into how they review people.

Source: [If Your Name Isn't Western, AI Could Cost You The Promotion.](https://www.forbes.com/sites/lutzfinger/2026/05/25/if-your-name-isnt-western-ai-could-cost-you-the-promotion/)

Question 3

Does the U.S. still have a lead in AI models after DeepSeek?

Accepted Answer

_U.S. and China are tied on models, but America leads on applications._

On the math level, on the computational side, we don't have a big head start anymore. We are at an equal playing field in models. It's like an arm's length race. However, if you lift it up beyond specific computational benchmarks, the guts of the systems, the processes, the whole package, and the application developer ecosystem, America has got a little bit more of its act together. The probability that the industry vertical solutions are going to be U.S. born and scaled still feels right, even if on a specific benchmark there's mathematical parity. Winning a benchmark doesn't get you a prize. You've got to have products and services that can be scaled.

— [AI, Innovation, and Risk, with Aneesh Chopra](https://www.lutzfinger.com/transcript/2025-02-19-ai-innovation-and-risk-with-aneesh-chopra/) · _The Keynote on AI_

Question 4

How are AI agents different from RPA (robotic process automation)?

Accepted Answer

_Agents add reasoning where RPA's rule engine stops._

RPA is a rule engine. It automates structured, low-skilled tasks where every decision is essentially zero or one, which is why UiPath and Automation Anywhere built real businesses but hit a ceiling: a script either breaks or runs, and it cannot handle uncertainty or unstructured data. Agents add a language model on top, so they can read an email's style, cope with ambiguity, and self-correct. Cherry's portfolio company Octomind does this for front-end testing with a self-healing loop that notices when a test goes wrong and tries another path, something a rule engine cannot do. The catch: you still have to give an agent clear boundaries, or it goes rogue.

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

Question 5

How are AI agents rewriting the internet and changing shopping?

Accepted Answer

_OpenAI and other LLM providers are becoming the dominant interface for online discovery._

> OpenAI's ambition to become the universal interface to what we knew as the Internet isn't surprising. If anything, they might be late. Remember Perplexity's 'Shop Like a Pro'? Their goal was to handle all of shopping — from research to payment.

Source: [AI Agents - The Internet Is Being Rewritten - Survey Users Vs Brands?](https://www.forbes.com/sites/lutzfinger/2025/07/24/ai-agentsthe-internet-is-being-rewrittensurvey-users-vs-brands/)

Question 6

How are jobs changing due to AI?

Accepted Answer

> The word 'engineer' still appears in job postings. So does 'product manager.' But the people being hired for those roles bear little resemblance to the people being let go.

Source: [Meta Laid Off 8,000 And Launched AI - Why Jobs Need Different Skills](https://www.forbes.com/sites/lutzfinger/2026/04/25/meta-laid-off-8000-and-launched-aiwhy-jobs-need-different-skills/)

Question 7

How can a cancer cell be made to produce its own drug?

Accepted Answer

_The aim is a therapy produced by the tumor, against the tumor, in the tumor, so potent drugs stay local._

> We created the first genetic switches made out of DNA that only turn on in cancer cells. You inject them and they go everywhere, but they only flip on inside a cancer cell, like a light switch. We only get into about 1% of the cancer cells, so the cell produces a message, a smoke signal, that brings the immune system to attack the rest, like a domino chain reaction. The hard part is keeping all of it inside the tumor so you avoid off-target effects.

Source: [Tomorrow's Medicine, with Cyriac Roeding (eCornell Keynote)](https://ecornell.cornell.edu/keynotes/view/K052126/)

Question 8

How can a large language model design DNA for new cancer drugs?

Accepted Answer

_DNA is a four-letter text, which makes sequence design a next-token problem, but only with the right data and a tight feedback loop._

> What is DNA? It's text. The alphabet of DNA has four letters and they keep permutating, so it's a perfect problem for an LLM, if it's trained on the right data. The general models are only okay-ish for DNA. There's one trained specifically on DNA, Evo 2 by the Arc Institute, the best out there, but still 99% of what comes out is useless. So we built a proprietary Oracle to predict what is not useless, feed it to the wet lab, and the outliers go back into the model. Every spin of the wheel, it gets smarter.

Source: [Tomorrow's Medicine, with Cyriac Roeding (eCornell Keynote)](https://ecornell.cornell.edu/keynotes/view/K052126/)

Question 9

How can AI support frontline workers who aren't sitting at computers all day?

Accepted Answer

_Think voice-first interfaces and knowledge retrieval, not just desktop tools._

Frontline workers move around and might not have time to sit at a laptop, but they could have a quick second to speak or type a question. You could support them with an AI interface that answers questions in their language, translates into technical knowledge, and searches knowledge databases. The key is making sure answers are right. Don't just put ChatGPT on your FAQs. You need human in the loop at the beginning, testing a lot to ensure frontline workers are happy with the output before rolling it out widely.

— [You asked, we answered](https://www.lutzfinger.com/transcript/2023-07-28-you-asked-we-answered/) · _The Edge_

Question 10

How can educators use AI clones to scale their teaching?

Accepted Answer

_Creating an AI version of yourself to teach online courses at scale._

> I created the Lutz-Clone, an AI version of myself designed to teach my online certificate about AI with eCornell. This clone looks like me, but more importantly, it contains my knowledge and teaching style.

Source: [AI In Education — Up-level Your Teaching With AI By Cloning Yourself](https://www.forbes.com/sites/lutzfinger/2025/03/31/ai-in-educationup-level-your-teaching-with-ai-by-cloning-yourself/)

Question 11

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 12

How did LinkedIn use knowledge graphs to solve the job title problem?

Accepted Answer

_Structuring 150 million job titles required more than simple categorization._

LinkedIn had over 150 million unique job title strings, which was impossible to model effectively. We decomposed titles into four facets: seniority, employment status, role, and specialty. For example, Senior Software Engineer contains seniority information, a role, and a specialty. We then built knowledge graphs for each dimension to show how values interrelated. This helped us understand completeness and vagueness of titles. A VP at a bank means something completely different than a VP at a tech company. Taxonomies alone were not expressive enough to capture these nuances.

— [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 13

How did Varo use AI to reduce operational costs while improving customer service?

Accepted Answer

_An AI chatbot cut costs 60% while handling most inquiries._

We started by providing voice support for everybody, but it got very expensive. We moved to live person chat, then built an AI chatbot. That chatbot now contains over 55% of our inbound inquiries and allowed us to reduce operational costs by about 60%. This is part of solving the high-cost problem that made serving underserved populations uneconomical for traditional banks. The AI handles routine questions efficiently while maintaining service quality for customers who value access and responsiveness.

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

Question 14

How did you replace yourself with a bot in your AI and product course?

Accepted Answer

_A virtual instructor keeps course content fresh and current._

I launched a public certificate program on product and AI at Cornell's Johnson School. The course is about 100 hours long. To keep the content up to date, I replaced myself with a bot, a virtual copy of myself. This allows the course material to stay current without requiring me to manually update everything. It's an example of how AI can maintain educational content in a scalable way while I focus on other work, including running my Gen AI platform for e-commerce.

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

Question 15

How do biases in narrow AI systems create real-world harm?

Accepted Answer

_AI systems enforce human prejudices at massive scale._

Narrow AI amplifies existing biases at scale. In parole decisions, computers were meant to remove human bias, but when fed biased data, they kept more minorities in jail. Loan application systems with few women in training data couldn't correctly handle female applicants. The stop and frisk program in New York used a computer to select precincts, but it was racially unjust. When you remove skin color from the data, the system became even more skewed. There's a logical data science explanation for this.

— [Bias in, bias out: Addressing the impact of bias in AI](https://www.lutzfinger.com/transcript/2023-06-07-bias-in-bias-out-addressing-the-impact-of-bias-in-ai/) · _The Edge_

Question 16

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 17

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 18

How do you measure quality in healthcare data?

Accepted Answer

_Quality metrics for healthcare ML._

> Knowing quality is therefore of utmost importance. Three broad measurement areas in today's healthcare: (1) Metrics about quality of care — related to treatment effectiveness and patient satisfaction. (2) Metrics about need and utilization of care — gauging health requirements of a population and how often one uses what type of service. (3) Metrics about value-based healthcare — they measure disease prevention rather than disease…

— [Healthcare And Data - How To Measure Quality](https://www.forbes.com/sites/lutzfinger/2023/06/27/healthcare-and-datahow-to-measure-quality/) · _Forbes_

Question 19

How do you spot social media bots?

Accepted Answer

_Detection heuristics._

> Figure 6-4. Subset of a social network, created using LinkedIn Labs Since bot networks will first create their own group by linking to one another before they start to infiltrate the rest of the network, especially in the beginning, you will see a highly dense network with very low cohesion to the rest of the network. However, those measures using network topology only work in the beginning as the bot network is formed. Once the bots…

— [Finger Dutta Ask Measure Learn (1)](https://www.lutzfinger.com/book/) · _Book — Ask, Measure, Learn_

Question 20

How does accountability work when AI agents are doing tasks for us?

Accepted Answer

_You remain accountable for your AI agents, like a CEO for employees._

If people write an email and that email is wrong, who is accountable for it? Should I say it was my AI agent? Not quite. It was me who trained and set up and focused an AI agent. We talk about the new role that all of us become managers or CEOs of many agents. If that description is true, then the accountability lies in you, the CEO. When something happens at a traditional corporate, the CEO might lose their job if somebody of their employees did not do the right thing, because ultimately they are responsible.

— [From Google to Cornell: Rethinking AI Workflows | Lutz Finger | Build AI Podcast 17](https://www.lutzfinger.com/transcript/2026-04-16-from-google-to-cornell-rethinking-ai-workflows-lutz-finger-build-ai-podcast-17/) · _Build AI_

Question 21

How does AI change healthcare?

Accepted Answer

_AI applications, value-based care, regulation._

> I just got back from the JP Morgan conference. A few learnings that apply to any industry, not just healthcare:Humans + AI will change the industry. Not AI alone.Cash flow matters. Not AI hype.The real AI revolution is operational.One of the most hyperbolic promises in healthcare in the last month was the claim that: "AI is going to design our drugs and will replace researchers." In my view, this is not true.

— [Healthcare's AI Lesson: Autocomplete Isn't Understanding](https://www.forbes.com/sites/lutzfinger/2026/01/16/healthcares-ai-lesson-autocomplete-isnt-understanding/) · _Forbes_

Question 22

How does AI change the way we interact with computers compared to previous interfaces?

Accepted Answer

_From punch cards to conversation: AI as natural interface_

AI enables me to communicate with machines using natural language instead of learning complex commands or navigating graphical interfaces. Previously, we evolved from punch cards to MS-DOS commands to graphical interfaces with mice and clicking. Each step made things more intuitive, but I still had to search and learn the system's rules. Now with large language models, I can simply describe what I want in my own words. The AI understands my intent and produces output, even if I have no idea how it works technically. This is revolutionary because humans naturally communicate through language.

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

Question 23

How does AI enable better information retrieval than traditional search?

Accepted Answer

_LLMs transform how we access and summarize information across all domains._

Large language models can read language, extract and summarize knowledge, and create new data based on what you're asking for. The internet democratized data, but data still needs processing and summarizing into chunks we can act on. LLMs make this chunking more effective. It's like how you can watch a two-hour movie and then concisely tell a friend the content. An LLM can do this for you, providing compact, concise information retrieval. This isn't just one size fits all. In education, for example, you might have many explanations for a math problem, and the question is which explanation works best for a specific student.

— [Cherry's Investment Thesis on AI](https://www.lutzfinger.com/transcript/2024-07-19-cherrys-investment-thesis-on-ai/) · _The Edge_

Question 24

How does AI find patterns in data that humans miss, like in healthcare?

Accepted Answer

_AI detects invisible patterns in healthcare data to prevent emergencies._

AI takes data and finds tiny clues that you and I might not see. At Google Health, we worked with electronic health records to predict whether somebody would end up in an emergency room six months before it happened. The AI sees the writing on the wall in the data. Roughly one third of people in hospitals shouldn't be there if we would have just known. The computer gets trained on data and figures out those tiny clues to predict preventable hospitalizations.

— [AI, Clones, Deep Fakes, and Neural Networks](https://www.lutzfinger.com/transcript/2024-09-12-ai-clones-deep-fakes-and-neural-networks/) · _The Keynote on AI_

Question 25

How does Catalog ensure certain workflows always follow the same steps when using LLMs?

Accepted Answer

_A rules engine ensures critical workflows always execute consistently._

We built a workflow engine underneath our LLMs. The LLM generates a workflow on the fly every time, but users can design deterministic journeys inside our system using workflows (like Zapier but internal). If a request matches a workflow definition, it triggers that workflow every time instead of a dynamically generated journey. It's no code, so users can do it themselves. First, the LLM does a workflow lookup to see if there's something you always want to do, then for the rest it goes into free flow logic.

— [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 26

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 27

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 28

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 29

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 30

How does Richard distinguish between jobs that will be automated by AI and those that won't?

Accepted Answer

_Data availability determines which jobs AI will transform or replace first._

Richard says you can predict how much an industry will change based on how much data is available for a given task. Jobs where outputs are publicly available on the internet, especially all-digital jobs, are where AI can learn from humanity's collective knowledge and automate the most. Plumbing, for example, has no automation potential because no one is collecting data about what plumbers do. Jobs with abundant digital data and public outputs face the highest automation risk in the coming years.

— [The Future of AI, with Richard Socher](https://www.lutzfinger.com/transcript/2026-02-05-the-future-of-ai-with-richard-socher/) · _The Keynote on AI_

Question 31

How does the EU AI Act build in flexibility to adapt as technology changes?

Accepted Answer

_Delegated powers and co-regulation let the AI Act evolve over time._

The EU AI Act is not rigid and set in stone forever. We have more empowerment through what we call Delegated and Implementing Acts. The executive, the European Commission, is empowered to take certain measures if necessary. For example, we have a threshold of 10 to the 25 flops for models. If the Commission feels overwhelmed or there are too few models in scope, we can change the threshold up or down. We can provide guidance and change it. A large part is co-regulation, working with standardization organizations and stakeholders to flesh this out in a way that makes sense.

— [SZ 139: AI in the EU Ft. Yaad, Gerard, Florence, Julie, & Lutz](https://www.lutzfinger.com/transcript/2024-12-09-sz-139-ai-in-the-eu-ft-yaad-gerard-florence-julie-lutz/) · _Silicon Zombies_

Question 32

How does Think Labs AI use physics informed AI differently than general purpose models like ChatGPT?

Accepted Answer

_Grid AI must be grounded in physics, not just patterns in data._

We cannot just put SCADA measurements and smart metering information into ChatGPT or Microsoft Copilot or Gemini and ask how the grid works. It does not work like that. We lean into something called Physics Informed AI. We train our AI models using physics. It is physics driven, physics based, and physics accurate. We teach AI how the grid works and reproduce engineering analysis faithfully. The AI can turn traditional studies that take weeks to months into minutes, and go from analyzing one feeder at a time into full system analysis.

— [AI Needs Power, the Grid Needs AI, with Josh Wong](https://www.lutzfinger.com/transcript/2026-04-02-ai-needs-power-the-grid-needs-ai-with-josh-wong/) · _The Keynote on AI_

Question 33

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 34

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 35

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 36

How has online shopping changed over the past 25 years?

Accepted Answer

_Twenty-five years later, online shopping looks surprisingly familiar._

Surprisingly, e-commerce hasn't changed much in the last 25 years. If you compare Amazon in 1999 to Amazon today, the core structure is essentially the same. You still need to select a category, then a subcategory, and you better know what you want and where it fits. The 1999 version had long written lists. Today looks slicker with more images, but shopping is still complicated. Most of the time, subcategories let you filter by stars or price, but they don't really describe product dimensions. For those details, you still need to click into the product and read if you care. It's still a transactional act of buying.

— [AI in E-Commerce](https://www.lutzfinger.com/transcript/2024-10-03-ai-in-e-commerce/) · _The Keynote on AI_

Question 37

How has the shift from SEO to GEO changed what websites need to do for brands?

Accepted Answer

_Websites now feed AI rather than convert human visitors directly._

Websites used to be designed to convert human visitors, but now their main purpose needs to be feeding data into AI. The three-party model of searcher, Google, and end content is disintegrating into a two-party model. Decisions are getting made before consumers hit the website, so the website is no longer the main conversion point. Instead, websites should be clean data delivery mechanisms to computers, very explicit about who you sell to, your target use case, pain points, and the entire buyer journey. If it's not explicit, the AI has no way to know and might recommend somebody else.

— [Marketing to Humans Changed, with Alex Dees](https://www.lutzfinger.com/transcript/2026-03-24-marketing-to-humans-changed-with-alex-dees/) · _The Keynote on AI_

Question 38

How is AI changing e-commerce?

Accepted Answer

_Fine-tuned models, attribution, category death._

> To explore this question, I looked at one industry: e-commerce. In my online certificate programs, I've explored AI use cases across many industries such as healthcare, media, coffee, finance and e-commerce. While healthcare tends to be slowest to adopt AI, e-commerce consistently leads.

— [ShopTalk 2025: Just 1% Of Vendors Use Generative AI To The Max](https://www.forbes.com/sites/lutzfinger/2025/03/21/state-of-genai--only-1---shoptalk-2025-in-numbers/) · _Forbes_

Question 39

How is AI changing education?

Accepted Answer

_What AI does for and to students, teachers, and curricula._

> AI agents will transform every industry — and one industry I know well is education. So, how will AI agents change how we teach and learn? To explore this question, I created the Lutz-Clone, an AI version of myself designed to teach my online certificate about AI with eCornell.

— [AI In Education — Up-level Your Teaching With AI By Cloning Yourself](https://www.forbes.com/sites/lutzfinger/2025/03/31/ai-in-educationup-level-your-teaching-with-ai-by-cloning-yourself/) · _Forbes_

Question 40

How is AI changing the role of software developers?

Accepted Answer

_Coding goes away, but software remains. Developers become technical architects._

Software developers are taking on a much more abstract role, similar to technical product managers. Many AI-first companies claim they're writing 80 to 90 percent of their code via AI, and a replacement event is beginning to occur. While coding as an activity is probably going away, software itself is still needed. Developers will need to be quite specific about how they want code to be stitched together and architected. They're essentially moving to a higher level of abstraction, just like when we went from punch cards to compilers to higher order languages.

— [Reshaping Investing With AI, with Lila Tretikov](https://www.lutzfinger.com/transcript/2026-02-17-reshaping-investing-with-ai-with-lila-tretikov/) · _The Keynote on AI_

Question 41

How is DNA being treated as a programmable language?

Accepted Answer

_DNA as text that can be read and written using AI models._

> For fifty years, biology was something we could only read. We are now starting to write it. DNA is next. Models like Evo 2 treat the genome as what it actually is, a language with a four-letter alphabet.

Source: [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/)

Question 42

How is Europe positioned in the global AI race compared to the US and China?

Accepted Answer

_Europe's talent, market size, and research create competitive advantages._

Europe is not badly positioned because it has important strengths. There are more engineers per capita in the EU than in the United States or China. Europe has a very large and rich single market, bigger than the US with more people. Europe is also very strong in research with a very high number of publications and excellent universities. The European population is on average very well educated and therefore likely to uptake technology. These factors give Europe a solid foundation for AI development and adoption.

— [AI in the EU, with Lucilla Sioli](https://www.lutzfinger.com/transcript/2025-04-24-ai-in-the-eu-with-lucilla-sioli/) · _The Keynote on AI_

Question 43

How is the Amazon homepage dying?

Accepted Answer

_Retail unbundling, AI assistants as the new aisle._

> The days of big online retail as we know it are over. Amazon, Walmart, Best Buy, and many others will all need to reinvent themselves as e-commerce changes. Once again, the homepage is dead. What will this mean for the big brands?

— [The End Of Amazon, Walmart, Best Buy? AI-Driven Retail Unbundling](https://www.forbes.com/sites/lutzfinger/2025/06/30/the-end-of-amazon-walmart-best-buy---ai-driven-retail-unbundling/) · _Forbes_

Question 44

How is the e-commerce industry changing due to AI?

Accepted Answer

> Every layer is collapsing into the one thing brands actually own, their shopper-behavior data and the fine-tuned AI it makes possible.

Source: [The State Of E-Commerce: Every Layer Of Discovery Changed At Once](https://www.linkedin.com/pulse/state-e-commerce-finetuned-ai-every-brand-lutz-finger-gs5bf/)

Question 45

How is the internet changing due to AI?

Accepted Answer

> The future belongs to AI assistants like ChatGPT, Claude and Perplexity.

Source: [AI Is Changing The Rules Of Web Traffic — A GEO Playbook For Online Retailers](https://www.forbes.com/sites/lutzfinger/2025/08/20/generative-engine-optimization-geo-a-playbook-for-online-retailers/)

Question 46

How many e-commerce vendors are actually using generative AI effectively?

Accepted Answer

_Despite hype, adoption of generative AI in business remains minimal._

> I analyzed 700 vendors at ShopTalk — the biggest e-commerce conference of the year — in regards to generative AI. The results are surprising. Just 1% of vendors use generative AI to the max.

Source: [ShopTalk 2025: Just 1% Of Vendors Use Generative AI To The Max](https://www.forbes.com/sites/lutzfinger/2025/03/21/state-of-genai--only-1---shoptalk-2025-in-numbers/)

Question 47

How might AI impact creative teams?

Accepted Answer

> AI Is Average And That's A Problem The core problem is that AI, by design, is average. It's spectacular at pattern recognition and synthesis

Source: [AI Cuts Creative Teams: What Companies Must Learn About AI Leadership](https://www.forbes.com/sites/lutzfinger/2026/02/13/ai-cuts-creative-teams-what-companies-must-learn-about-ai-leadership/)

Question 48

How should businesses prepare for AGI, and what comes after chatbots and agents?

Accepted Answer

_The practical move is to avoid overcommitting and to build toward agents with persistent memory rather than one-shot chatbots._

> The right answer is not to overcommit to any particular technology at a particular moment, because if the singularity idea is at all real, we're all going to have to pivot and re-pivot. What's coming is agents with persistent memory that reason on that memory and build their own world model as they go. An agent that remembers everything it ever did feels less like a query loop and more like a personal assistant you actually have a relationship with.

Source: [Rethinking the AGI Race, with Benjamin Goertzel (eCornell Keynote)](https://ecornell.cornell.edu/keynotes/view/K052826/)

Question 49

How should executives decide where to apply AI?

Accepted Answer

_Start with the problem, not the tool, and the right use of AI emerges on its own._

> You don't start with the tool, you start with the problem, and then it naturally emerges which tools are best to use. If you want a house, you start with wanting the house, then find the materials and skilled people. You don't start with a screwdriver and ask what you could build with it. AI is like suddenly having a bazooka in your toolbox: it's incredibly powerful, but you still need to aim it and know exactly what to do with it.

Source: [Tomorrow's Medicine, with Cyriac Roeding (eCornell Keynote)](https://ecornell.cornell.edu/keynotes/view/K052126/)

Question 50

How should product builders think about data bias in AI systems?

Accepted Answer

_Bias in data is inevitable; product decisions determine how much to accept._

There is never a way to have non-biased data, because data is biased. As a product person, you need to decide by how much you want to de-bias the data. Sometimes bias is actually good because you don't want averages, you want the best outcome. For example, if identifying certain patients or optimal machinery output, you don't want what the machine did in the past all the time, but just the best parts of it. You're injecting very good data versus just average data.

— [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 51

How will AI change jobs?

Accepted Answer

_Disruption, enablement, and the skills that matter._

> Microsoft offered voluntary retirement to thousands of long-tenured staff. Twenty thousand jobs gone in seven days, from two of the most profitable technology companies in history. The news call it an AI replaces humans. This storyline is wrong.

— [Meta Laid Off 8,000 And Launched AI - Why Jobs Need Different Skills](https://www.forbes.com/sites/lutzfinger/2026/04/25/meta-laid-off-8000-and-launched-aiwhy-jobs-need-different-skills/) · _Forbes_

Question 52

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 53

Is AGI near?

Accepted Answer

_Lutz's stance on the AGI timeline._

> Both have been hot topics in AI, sparking both fear and excitement. Sam Altman predicted AGI would arrive in 2025, and Elon Musk 2026. Both are more about hype than reality. In 2025, there will be no AGI but large language models will find their "killer app." What are AGI and the Singularity?

— [No AGI But A 'Killer App' — 2025 AI Prediction](https://www.forbes.com/sites/lutzfinger/2025/01/03/no-agi-but-a-killer-app2025-ai-prediction-110/) · _Forbes_

Question 54

Is AI hurting people with non-Western names?

Accepted Answer

_Concrete bias outcomes in hiring and promotion._

> The junior analyst who keeps pushing the conversation forward finally gets seen, even though she never had the corner office. Tools like Granola, Read.ai, Fireflies, and Avoma now routinely track talk time and participation, and firms like Amazon and Meta are folding AI-surfaced contribution into how they review people.

— [If Your Name Isn't Western, AI Could Cost You The Promotion.](https://www.forbes.com/sites/lutzfinger/2026/05/25/if-your-name-isnt-western-ai-could-cost-you-the-promotion/) · _Forbes_

Question 55

Is Google search dying?

Accepted Answer

_What changes when LLMs become the search interface._

> Back in the 1990s, people said "All the information is out there at the internet... we just need to find it." That was the time of search engines — AltaVista, Yahoo! Search, Google, Lycos, WebCrawler, Excite. Even Disney tried its hand with Go (1998). New Technology — New Search: Large Language Models Build AI Search.

— [2025: The Year Of Search But Not The End Of Google](https://www.forbes.com/sites/lutzfinger/2025/01/03/2025-the-year-of-search-but-not-the-end-of-google/) · _Forbes_

Question 56

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 57

Is SaaS dead due to AI?

Accepted Answer

> The interface is. SaaS is not dead. The interface is.

Source: [SaaSpocalypse Is Dead - The Future Of SaaS Is SaaS](https://www.forbes.com/sites/lutzfinger/2026/04/24/saaspocalypse-is-deadthe-future-of-saas-is-saas/)

Question 58

Should AI be regulated?

Accepted Answer

_Lutz's view on AI regulation._

> But regulation doesn't happen in a vacuum. The EU has regulated that gender should not be used in AI models — even though this may lead to less effective risk distribution and higher overall costs. As a society, we may be willing to pay that price, but what if other societies are not? Data is the lifeblood of AI.

— [Emmanuel Macron On AI: We Are Regulating Ourselves Out Of The Market](https://www.forbes.com/sites/lutzfinger/2024/10/22/emmanuel-macron-is-right-about-ai-over-regulatingi-have-no-doubt/) · _Forbes_

Question 59

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 60

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 61

Should you trust OpenAI blindly?

Accepted Answer

_Vendor risk and architectural diversification._

> Stargate's $500B investment — message to the competitors — meant to intimidate potential challengers with overwhelming financial backing. AI is extremely dangerous; only a select few should be allowed to develop it — OpenAI's storyline to use safety concerns as justification for a regulatory setup that creates barriers to entry and gives them more monopolistic power. From now on, models should respect IP rights — OpenAI ignored IP…

— [Is OpenAI A Victim — DeepSeek And IP Rights — A Playbook For Leaders](https://www.forbes.com/sites/lutzfinger/2025/01/29/is-openai-a-victim---deepseek-and-ip-rights---a-playbook-for-leaders/) · _Forbes_

Question 62

What about deepfakes?

Accepted Answer

_How society will adapt to synthetic media._

> As AI lowers the barriers to content creation, abuse is also becoming more feasible. Deepfakes — AI-generated images, videos and audio designed to mimic real people — pose serious risks. Already in 2022, I demonstrated how easily they can be created. This increases the risk of misinformation, identity theft and political manipulation.

— [Goku, A Chinese Open-Source Model, Challenges OpenAI And The U.S.](https://www.forbes.com/sites/lutzfinger/2025/02/11/goku---a-chinese-open-source-model-challenges-openai-and-the-us/) · _Forbes_

Question 63

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 64

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_

Q&A

Are large language models the path to AGI, or an off-ramp?

Can AI transcription tools introduce bias based on employee names?

Does the U.S. still have a lead in AI models after DeepSeek?

How are AI agents different from RPA (robotic process automation)?

How are AI agents rewriting the internet and changing shopping?

How are jobs changing due to AI?

How can a cancer cell be made to produce its own drug?

How can a large language model design DNA for new cancer drugs?

How can AI support frontline workers who aren't sitting at computers all day?

How can educators use AI clones to scale their teaching?

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

How did LinkedIn use knowledge graphs to solve the job title problem?

How did Varo use AI to reduce operational costs while improving customer service?

How did you replace yourself with a bot in your AI and product course?

How do biases in narrow AI systems create real-world harm?

How do venture firms use AI in their own operations?

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

How do you measure quality in healthcare data?

How do you spot social media bots?

How does accountability work when AI agents are doing tasks for us?

How does AI change healthcare?

How does AI change the way we interact with computers compared to previous interfaces?

How does AI enable better information retrieval than traditional search?

How does AI find patterns in data that humans miss, like in healthcare?

How does Catalog ensure certain workflows always follow the same steps when using LLMs?

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

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

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

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

How does Richard distinguish between jobs that will be automated by AI and those that won't?

How does the EU AI Act build in flexibility to adapt as technology changes?

How does Think Labs AI use physics informed AI differently than general purpose models like ChatGPT?

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

How does Zenith OS reduce costs in neuropsychiatry treatment?

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

How has online shopping changed over the past 25 years?

How has the shift from SEO to GEO changed what websites need to do for brands?

How is AI changing e-commerce?

How is AI changing education?

How is AI changing the role of software developers?

How is DNA being treated as a programmable language?

How is Europe positioned in the global AI race compared to the US and China?

How is the Amazon homepage dying?

How is the e-commerce industry changing due to AI?

How is the internet changing due to AI?

How many e-commerce vendors are actually using generative AI effectively?

How might AI impact creative teams?

How should businesses prepare for AGI, and what comes after chatbots and agents?

How should executives decide where to apply AI?

How should product builders think about data bias in AI systems?

How will AI change jobs?

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

Is AGI near?

Is AI hurting people with non-Western names?

Is Google search dying?

Is OpenAI going bankrupt?

Is SaaS dead due to AI?

Should AI be regulated?

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?

Should you trust OpenAI blindly?

What about deepfakes?

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 are the core limitations of transformers and backpropagation for building AGI?

What are the four approaches to addressing bias in AI models?

What are the fundamental limits of large language models that people should understand?

What are the main limitations of large language models that knowledge graphs help address?

What are the three main activities the European AI Office is responsible for?

What could be the deciding factor in the AI race?

What did Jim Allison's checkpoint inhibitors miss, and how does early aim to complete the picture?

What did you get wrong about multimodal AI interfaces?

What do you mean when you say winners won't be those who use AI, but those who redesign work around it?

What does 'AI as an operating model' mean?

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

What does the eCornell AI certificate cover?

What does the term agents actually mean in AI, and is the technology working?

What framework helps assess whether imperfect AI decisions are acceptable?

What is 2025 for search?