Question 1

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 2

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 3

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 4

Is Sam Altman's claim about AGI just marketing?

Accepted Answer

_Bold AGI promises grab attention but miss the real story._

Yes, it's mostly marketing. Big, bold claims grab attention. The promise of AGI positions OpenAI as the leader in the AI space, but the real story in 2025 will be the killer application of large language models. Predictions of AGI in 2025 are more about hype than reality. Instead of achieving Artificial General Intelligence or the Singularity, we'll see narrow AI solutions integrated into productive chains that excel at specific tasks without becoming generally intelligent.

— [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 5

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 6

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 7

What are the main problems with using simple GPT wrappers for enterprise AI applications?

Accepted Answer

_Why basic ChatGPT integrations fail in business settings._

GPT wrappers have several critical shortcomings that prevent them from working well in enterprise settings. They lack system integration, so they cannot check calendars or access other tools. They have no context about user preferences or past interactions. Security is missing, which means they could accidentally reveal private details. They lack guardrails to handle controversial questions appropriately. They offer no user control over outputs. And they suffer from hallucinations, where the AI sometimes makes things up. These issues make simple wrappers unsuitable for real enterprise deployment.

— [AI Agents In 2025: What Enterprise Leaders Need To Know](https://www.forbes.com/sites/lutzfinger/2025/01/05/ai-agents-in-2025-what-enterprise-leaders-need-to-know/) · _Forbes_

Question 8

What are the three key takeaways about competitive advantages with LLMs?

Accepted Answer

_Model advantages fade, data and brand loyalty offer lasting protection._

First, be cautious with claims about a Model Moat. A model moat is most likely only transient at best, and models will often be available as open source. Second, focus on Data Moats. Data moats offer a more sustainable advantage, and exclusive access to proprietary data can be a significant barrier to entry, but data needs to create value. Third, Stickiness Moat. Brand can create a powerful competitive advantage if startups focus on user happiness.

— [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 9

What do companies need to win the AI search war?

Accepted Answer

_User engagement and understanding context are keys to winning search._

Like in the 1990s, it all boils down to users. If a search tool can grab enough attention, keep people engaged, and build loyalty, it has a shot at success. Companies need to grasp the concept of the long tail in search. Human queries are inherently complex, which is why keyword-based search initially struggled. Understanding context while providing the right level of information is a challenging task that winners must master.

— [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 10

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

Accepted Answer

_Business needs remain constant, workflows must evolve._

Our business hasn't changed. We still have needs, we still travel, we still consume, we still build. The only question is how can I do this business more effectively? Yes, there will be people who use ChatGPT to write an email, but the winners will be those who either build the platforms or rearrange the workflows in order to be effective. The focus is on the workflow, the surrounding, the setup. How do we empower you in your environment, not can you use it?

— [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 11

What does 'AI as an operating model' mean?

Accepted Answer

_Why the winners redesign work around AI rather than just adopting tools._

> 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 12

What is the BizML framework and why is it needed for AI projects?

Accepted Answer

_*A business framework for predictive AI, not just technical tools*_

BizML is a six-step framework I created that ushers predictive AI projects successfully from inception to deployment. It's not like MLOps or any technical solution. It's an organizational procedure, a management protocol that bridges the tech-biz divide. It brings together business-side stakeholders with data scientists through semi-technical collaboration. The framework addresses what's predicted, how well, and what's done about it. Without this kind of practice or framework, most business people don't realize you need a very specialized approach to get these projects successfully through to deployment.

— [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 13

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 14

What is the difference between AGI, singularity, and superintelligence?

Accepted Answer

_Understanding the core terms of the AGI conversation today_

AGI, or artificial general intelligence, is the ability to generalize beyond what you were trained for. If given an unfamiliar environment and goal, it should figure out how to achieve that goal through creative imagination and trial and error. Singularity, coined by Vernor Vinge and popularized by Ray Kurzweil, refers to a point where the rate of progress feels infinite to legacy humans. This happens through recursive self-improvement, where an AI makes a smarter AI. Superintelligence follows AGI. Many believe that once we achieve human-level AGI, it will optimize its own code and exceed human levels much faster than predicted, creating a steep exponential curve rather than gradual progress.

— [Rethinking the AGI Race, with Benjamin Goertzel](https://www.lutzfinger.com/transcript/2026-05-28-rethinking-the-agi-race-with-benjamin-goertzel/) · _The Keynote on AI_

Question 15

What is the practical framework for implementing AI agents in enterprise settings?

Accepted Answer

_Five steps to deploy AI agents that create business value._

The framework I recommend has five essential steps. First, define the business objective to ensure you are solving a real problem. Second, collect and clean the data needed to power the workflow. Third, develop the agentic workflow by chaining AI models appropriately. Fourth, test with actual users to validate the solution works. Fifth, create a feedback loop for continuous improvement. Most importantly, the focus must remain on value creation throughout the entire process, not just on implementing technology for its own sake.

— [AI Agents In 2025: What Enterprise Leaders Need To Know](https://www.forbes.com/sites/lutzfinger/2025/01/05/ai-agents-in-2025-what-enterprise-leaders-need-to-know/) · _Forbes_

Question 16

What is your view on AGI and the current hype around achieving it soon?

Accepted Answer

_*AGI is a ghost story, not a roadmap for near-term investment*_

I assert that AGI is essentially a myth. I'm not saying it's theoretically impossible, but nothing in all the advancements of technology represent a concrete, clear step toward AGI. The distance between what is capable and what humans are is going to become increasingly clear. AGI is just another way of saying artificial human. It's a ghost story, the novel Mary Shelley would have written if she knew about algorithms. The false narrative that we're headed towards AGI, possibly within a few years or couple of decades, is part of the problem.

— [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 17

What mistakes do brands make in AI-driven e-commerce?

Accepted Answer

_Common pitfalls and how to avoid them._

> And underneath it all, brands risk losing their narrative to an intermediary that now owns the customer touchpoint. Having spent years building fine-tuned AI models for online retail, I keep watching e-commerce executives respond to all of this by making the same three mistakes. Each one is avoidable, and each one is expensive.

— [Three Mistakes Brands Make In The Age Of AI-Driven E-Commerce](https://www.forbes.com/sites/lutzfinger/2026/05/23/three-mistakes-brands-make-in-the-age-of-ai-driven-e-commerce/) · _Forbes_

Question 18

What should enterprise leaders expect from AI agents in 2025?

Accepted Answer

_Expect many workflow improvements, not one transformative application._

2025 will be the year of AI agents. Rather than expecting one killer app, leaders should anticipate AI simplifying, enhancing, or automating many smaller workflows across industries. We will see applications in areas like customer care, legal support, and sales. The key shift is moving from isolated generative AI experiments to properly integrated agentic workflows that chain multiple AI models together. These workflows will have system integration, context through RAG, appropriate guardrails, and user control, making them viable for production enterprise use.

— [AI Agents In 2025: What Enterprise Leaders Need To Know](https://www.forbes.com/sites/lutzfinger/2025/01/05/ai-agents-in-2025-what-enterprise-leaders-need-to-know/) · _Forbes_

Question 19

What's the biggest mistake CEOs make when trying to integrate AI into their companies?

Accepted Answer

_Don't just replace humans, reimagine the entire workflow_

The challenge is not just replacing humans in existing workflows. It's reimagining how the workflow actually would work without the human in the loop. When we had the first wired telephone, they used it to transmit symphonies because they thought that was the killer app. It took quite a while until Spotify actually made the news. Your challenge as a CEO is to not go through every workflow and say how can I replace my customer care agent. That's an old workflow. Maybe build it into the product experience so you don't need a customer AI agent in the first place. That is the change.

— [Deep Dive Into The Current And Future State Of Ai With Lutz Finger](https://www.lutzfinger.com/transcript/2025-04-24-deep-dive-into-the-current-and-future-state-of-ai-with-lutz-finger/) · _The Pulse of AI_

Question 20

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 21

Why are AI workflows the real moat?

Accepted Answer

_How redesigning work around AI beats model upgrades._

> SaaS can build the new AI-driven workflows on top of its user data and customer surfaces. But workflows are brittle. One new prompt, one model update, and the whole chain quietly breaks. I watch this every time I get students [coding agents in my Cornell workshops](https://ecornell.cornell.edu/courses/artificial-intelligence/building-enterprise-ready-ai-agents-from-prototypes-to-production/).

— [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 22

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 23

Why did many generative AI projects fail to scale in 2024?

Accepted Answer

_The two fatal flaws in most 2024 AI implementations._

Many companies rushed to try generative AI in 2024, and some even used chained models. But most projects failed to scale for two main reasons. First, they were test projects not integrated into system infrastructure, meaning they existed in isolation rather than connecting to existing business systems. Second, they were missing guardrails and quality controls necessary for production deployment. Without proper integration and controls, these pilot projects could not move beyond experimentation to deliver real business value at scale.

— [AI Agents In 2025: What Enterprise Leaders Need To Know](https://www.forbes.com/sites/lutzfinger/2025/01/05/ai-agents-in-2025-what-enterprise-leaders-need-to-know/) · _Forbes_

Question 24

Why do AI copilots sometimes fail to deliver value in enterprises?

Accepted Answer

_Even advanced AI copilots can fail without proper user training._

Mark Benioff from Salesforce joked that Microsoft's copilots have been hit and miss, comparing them to Clippy, the failed desktop assistant. The challenge is it's not easy for enterprises to make AI useful. In my course, I was the first to completely integrate OpenAI and replace myself as a teacher, giving students AI tools to handle complicated coding without needing to code. What I saw is that students initially struggled with this new colleague. Over time, once we trained them on how to use it, they learned. So the hit and miss nature comes from the difficulty of integrating AI into existing workflows and user expectations.

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

Question 25

Why do business consulting firms get the AI job replacement question wrong?

Accepted Answer

_Consulting firms miss how efficiency creates demand, not scarcity._

Business consulting firms often push the narrative that AI automation means fewer jobs, but they're missing the bigger picture. AI is augmenting knowledge workers, not replacing them. It makes them more efficient, allowing them to create more value. As any enterprise leader or CEO knows, when more value is created, the demand for skilled professionals increases. In the long run, AI will generate more jobs, not fewer. The real question is how we, as a society, support those who need to learn new skills.

— [Is AI Replacing Us — Good News For Knowledge Workers](https://www.forbes.com/sites/lutzfinger/2025/01/22/is-ai-replacing-us-good-news-for-knowledge-workers/) · _Forbes_

Question 26

Why do major tech CEOs keep claiming AGI is imminent when current AI still has obvious limitations?

Accepted Answer

_Market pressures shape how companies talk about AGI timelines publicly_

There is tension between AGI timelines and current product reality. While Altman, Musk, and Hassabis suggest AGI is near or already here, anyone who codes knows current systems still fail frequently. At Google I/O, Demis Hassabis mentioned AGI by 2029 and being at the foothills of singularity, but this was buried at the end. Most of the keynote focused on incremental AI features in products. This makes sense because publicly listed companies need to highlight quarterly profits. Saying AGI comes in 2029 might cause investors to dump stock and wait. So companies focus on near-term utility while their research arms pursue AGI separately. The timeline itself has become more plausible to many, though skeptics remain.

— [Rethinking the AGI Race, with Benjamin Goertzel](https://www.lutzfinger.com/transcript/2026-05-28-rethinking-the-agi-race-with-benjamin-goertzel/) · _The Keynote on AI_

Question 27

Why do self-insured companies have different incentives than traditional healthcare payers when it comes to preventing avoidable admissions?

Accepted Answer

_Self-insured companies bear actual costs, creating aligned health incentives._

Research shows that one third to one quarter of hospital admissions in the US are avoidable. In traditional healthcare, hospitals view admissions as revenue and insurance companies have already priced those risks into premiums, so there's little incentive to prevent them. Self-insured companies are different because they pay the actual care their employees receive and bear the actual cost of healthcare. This creates alignment between the payer and the patient, giving self-insured companies a vested interest in promoting healthier lifestyles and a shared commitment to health that didn't exist before.

— [Three Trends Of Transformation: How AI & Value-Based Care (VBC) Will Drive Transformation Of Self-Insured Healthcare](https://www.forbes.com/sites/lutzfinger/2023/05/01/three-trends-of-transformation-how-ai--value-based-care-vbc-will-drive-transformation-of-self-insured-healthcare/) · _Forbes_

Question 28

Why do you say AGI won't happen in 2025 despite predictions from Sam Altman and Elon Musk?

Accepted Answer

_Predictions from AI leaders are more hype than reality._

Sam Altman predicted AGI would arrive in 2025, and Elon Musk said 2026. Both are more about hype than reality. Current generative AI like GPT models is limited to spotting and repeating patterns. ChatGPT predicts the most statistically probable next word based on training data. This isn't real understanding, just pattern matching. It's no more self-aware than a chess computer that doesn't care if it loses. Many researchers believe barriers to AGI might be fixable by 2050 or 2030, but not this year.

— [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 29

Why does 'good enough' beat perfect for AI products?

Accepted Answer

_Shipping iteratively vs chasing perfection._

> The core problem is that AI, by design, is average. It's spectacular at pattern recognition and synthesis, but it produces the most statistically likely output — not the most surprising, not the most brave, not the most emotionally resonant. ### What Made Steve Jobs Leadership Special Let's look at Apple in the iPod era.

— [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/) · _Forbes_

Question 30

Why does the EU believe governments, not just tech companies, should regulate AI?

Accepted Answer

_Society, not billionaires, should decide how AI shapes our lives._

As Gerard de Graaf puts it, who elected Elon Musk, Sundar Pichai, and Sam Altman to make decisions on our behalf? These technologies have real implications on people's lives, and these are decisions that collectively as a society we should take. The industry has its own incentives. There's a race to the market, and you need to be first. In the internet, if you come in number two or three, you're a loser. So there's tremendous incentive for industry to roll out the latest generation. If you don't regulate, the decisions will be taken by industry. Done correctly, regulation brings legitimacy to how these technologies work.

— [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 31

You've been building AI-driven products for over 20 years at companies like Google, LinkedIn, and Snap. How do you view the current hype around agentic AI compared to past technology revolutions?

Accepted Answer

_Agentic AI is fundamentally an interface innovation, not magic_

History is a good guide here. We've been through this before with data, then mobile. I was at LinkedIn during the mobile revolution when we essentially stopped the whole company to move everything to mobile. Now with agentic AI, it's important to understand what it actually is. It's essentially an interface. It's the moment of the mouse, but now you can use language to describe what you want. We live through a time when analytics were done with SQL. Startups tried semantic text mining and failed, going back to SQL. Now we have a better interface where you can have agents that search, shop, or analyze for you.

— [Deep Dive Into The Current And Future State Of Ai With Lutz Finger](https://www.lutzfinger.com/transcript/2025-04-24-deep-dive-into-the-current-and-future-state-of-ai-with-lutz-finger/) · _The Pulse of AI_

Q&A, Leadership

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

How is AI changing education?

How will AI change jobs?

Is Sam Altman's claim about AGI just marketing?

Should AI be regulated?

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

What are the main problems with using simple GPT wrappers for enterprise AI applications?

What are the three key takeaways about competitive advantages with LLMs?

What do companies need to win the AI search war?

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 is the BizML framework and why is it needed for AI projects?

What is the competitive advantage of LLMs for business?

What is the difference between AGI, singularity, and superintelligence?

What is the practical framework for implementing AI agents in enterprise settings?

What is your view on AGI and the current hype around achieving it soon?

What mistakes do brands make in AI-driven e-commerce?

What should enterprise leaders expect from AI agents in 2025?

What's the biggest mistake CEOs make when trying to integrate AI into their companies?

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

Why are AI workflows the real moat?

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

Why did many generative AI projects fail to scale in 2024?

Why do AI copilots sometimes fail to deliver value in enterprises?

Why do business consulting firms get the AI job replacement question wrong?

Why do major tech CEOs keep claiming AGI is imminent when current AI still has obvious limitations?

Why do self-insured companies have different incentives than traditional healthcare payers when it comes to preventing avoidable admissions?

Why do you say AGI won't happen in 2025 despite predictions from Sam Altman and Elon Musk?

Why does 'good enough' beat perfect for AI products?

Why does the EU believe governments, not just tech companies, should regulate AI?

You've been building AI-driven products for over 20 years at companies like Google, LinkedIn, and Snap. How do you view the current hype around agentic AI compared to past technology revolutions?