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

Are these layoffs really about AI replacing humans?

Accepted Answer

_Not fewer humans, but different humans with AI judgment skills._

This storyline is wrong. Meta needs different skills, AI focused skills. They need people who know how to work with AI. Engineers and Product Managers are replaced with AI Engineers and AI Product Managers. Companies are not firing engineers and product managers because they want fewer humans. They are hiring different humans. Humans who bring judgment into AI systems rather than working around them. Meta's departing employees are not being replaced by robots. They are being replaced by engineers who can define termination conditions for autonomous agents, and PMs who know how to build evaluation datasets.

— [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 3

Can a well-designed prompt outperform OpenAI's o1 model?

Accepted Answer

_Human-written prompts can still beat o1's automated reasoning processes._

Yes. A well-designed prompt, meaning the human writes the instruction, can, so far, outpace OpenAI's o1. This is noteworthy because o1 is designed specifically for reasoning through an iterative, self-calling process with dedicated steps for iteration, reflection, and feedback loops. The fact that careful human-written prompts can still exceed its performance suggests that the model's reasoning capabilities, while advanced, have not yet surpassed what skilled human prompt engineering can achieve.

— [Sam Altman Says, 'We Know How To Build AGI' — Not So Fast](https://www.forbes.com/sites/lutzfinger/2025/01/07/sam-altman---agi--ai-workforce-in-2025-the-battle-of-tech-giants/) · _Forbes_

Question 4

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 5

Can we conclude that trust causes higher vaccination rates?

Accepted Answer

_Correlation found, but causation works both ways with trust._

While we have shown the impact of trust on covid vaccination, correlations do not indicate causation. Trust is a two-way street. If governments do an excellent job vaccinating their population, it might alter people's overall trust in their institutions. Appropriate, transparent responses will improve the ability of countries to deal with the effects of the COVID pandemic in a virtuous cycle, suggesting the relationship flows both ways.

— [Get Vaccinated? How Trust In Institutions Determines COVID Vaccination Rates In The EU](https://www.forbes.com/sites/lutzfinger/2021/05/06/get-vaccinated-how-trust-into-the-institutions-determines-vaccination-rates-in-the-eu/) · _Forbes_

Question 6

Did you actually replace yourself as a teacher with an AI clone?

Accepted Answer

_A teaching experiment reveals AI's limits in education._

I built one of the first online certificates, Designing and Building AI Solutions, where I replaced myself with an AI clone. The clone is public. It's now a no-code course where students can imagine business ideas and have the AI clone write the necessary code. We have a 24/7 teaching assistant. But did this mean I replaced myself as a teacher? Not even close. AI can summarize information, but it can't predict future trends. Teaching is more than just delivering knowledge. It's about understanding and evaluating AI trends to find the best ways to use AI for business.

— [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 7

Did you find other factors that influenced vaccination rates besides trust?

Accepted Answer

_Other factors tested but trust stood out statistically._

There are other factors besides trust that might influence vaccination rates, including accessibility and vulnerability. We analyzed the old-age dependency ratio and health care expenditure per inhabitant but did not find statistically significant dependencies. This suggests that while these factors could theoretically matter, in our analysis trust emerged as the key differentiator when controlling for these other variables across EU member states.

— [Get Vaccinated? How Trust In Institutions Determines COVID Vaccination Rates In The EU](https://www.forbes.com/sites/lutzfinger/2021/05/06/get-vaccinated-how-trust-into-the-institutions-determines-vaccination-rates-in-the-eu/) · _Forbes_

Question 8

Does Google still have a chance against the new AI search competitors?

Accepted Answer

_Google's experience and data give it staying power in AI search._

Yes. Don't count Google out just yet. Google has a significant advantage with its years of experience and vast amounts of data. The comeback it made with its generative search experience was impressive. While Google was late to the game, its generative search experience is coming back quite strong. It won't be the end of Google, but the fight is definitely on.

— [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 9

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 10

How accurate are current deepfake detection technologies?

Accepted Answer

_Detection tools fail more often than they succeed right now._

Current deepfake detection technology isn't very good yet. The best deepfake detector is only 65% accurate. Even Microsoft's Azure Cognitive Services was fooled over 78% of the time. This means detection tools fail more often than they succeed. The future of these detectors will mirror bot detectors, spam detectors, or any other cyber threat detectors. It will be an arms race between those creating deepfakes and those trying to detect them, similar to how spam and virus detection evolved over time.

— [Deepfakes - The Danger Of Artificial Intelligence That We Will Learn To Manage Better](https://www.forbes.com/sites/lutzfinger/2022/09/08/deepfakesthe-danger-of-artificial-intelligence-that-we-will-learn-to-manage-better/) · _Forbes_

Question 11

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 12

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 13

How are companies currently using AI tools to measure employee contributions?

Accepted Answer

_AI meeting tools are reshaping how companies evaluate employee performance._

Companies are using AI tools like Granola, Read.ai, Fireflies, and Avoma to record and transcribe meetings, tracking metrics like talk time and participation. Firms like Amazon and Meta are folding these AI-surfaced contributions into how they review people. The idea is that when the transcript becomes the record, ideas can propagate on merit rather than by who has the most power or the loudest voice. This shift means contribution gets measured by what you actually said, not by your title or office.

— [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 14

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 15

How are users and brands responding differently to AI agents in e-commerce?

Accepted Answer

_Users bypass homepages while brands invest in generative engine optimization._

It's not yet clear how AI agents will win in e-commerce. Users prefer answer-first experiences powered by ChatGPT, Gemini, and Perplexity. They now bypass the homepage entirely, leading to a decline of the website. Brands, on the other hand, are pushing GEO, which is Generative Engine Optimization. They're also creating ChatGPT-like experiences on their site search. Meanwhile, LLMs like OpenAI and Perplexity want to integrate agent capabilities and the ability to place ads.

— [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/) · _Forbes_

Question 16

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 17

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 18

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 19

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 20

How can fine-tuned language models transform B2B distributor expertise?

Accepted Answer

_Decades of technical knowledge, now instantly accessible at scale._

A fine-tuned, domain-specific LLM can capture and deliver what once lived only in the minds of seasoned technical staff. The accumulated knowledge behind decades of "call Bob in tech support" scenarios, including spec sheets, material tolerances, and compatibility nuances, can now be made instantly accessible at zero marginal cost. This scales expertise in a way that was previously impossible, turning institutional knowledge into a programmable, always-on asset.

— [AI For Private Equity — The Future Of B2B Distribution](https://www.forbes.com/sites/lutzfinger/2025/07/15/ai-for-private-equity---the-future-of-b2b-distribution/) · _Forbes_

Question 21

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 22

How can transparency tools like NFTs help combat deepfakes?

Accepted Answer

_NFTs could verify authentic videos by tracking their original sources._

In a world of deepfake abundance, verified accounts alone will not be sufficient to instill trust. One proposition to combat deepfakes points to the usage of NFTs. If everyone encrypted their videos as NFTs, it would be easy to find and compare different sources of the same moment. This transparency mechanism would help establish which version of a video is authentic by creating a verifiable chain of custody. Social media companies created verified accounts as one trust mechanism, but new technologies like NFTs may be needed as the deepfake threat grows.

— [Deepfakes - The Danger Of Artificial Intelligence That We Will Learn To Manage Better](https://www.forbes.com/sites/lutzfinger/2022/09/08/deepfakesthe-danger-of-artificial-intelligence-that-we-will-learn-to-manage-better/) · _Forbes_

Question 23

How could Europe break down existing monopolies to help AI startups compete?

Accepted Answer

_Portability and open weights could level the AI playing field._

Europe must find ways to level the playing field by breaking down legacy moats. Let users port their social connections or AI history from one platform to another. Ironically, European privacy laws, meant to protect consumers, often reinforce monopolies instead. Additionally, what if Europe required AI companies to make their weights open? This would give European startups a fighting chance to build on shared infrastructure instead of starting from scratch. Europe's push for data spaces is well-intentioned but overengineered. Data is AI's oxygen, and enabling true data access is essential for competition.

— [Nvidia Wants To Power Europe's AI Dreams. The EU Isn't Ready](https://www.forbes.com/sites/lutzfinger/2025/06/16/nvidia-wants-to-power-europes-ai-dreams-the-eu-isnt-ready/) · _Forbes_

Question 24

How could regulation help OpenAI create a competitive moat?

Accepted Answer

_Advocacy for oversight and NoAI tags could limit new competitors._

Regulation can create a moat. Sam Altman has been on tour with policymakers advocating for more oversight and control. Additionally, OpenAI now allows blocking their scraping mechanism with a NoAI tag. Both efforts would create a moat in two ways: First, regulators would allow OpenAI as a tool but be more restrictive to newcomers. Second, newcomers could no longer scrape data with a NoAI tag. This is one of five strategies in OpenAI's playbook to secure its investments.

— [How OpenAI Can Secure Its Investments - A Playbook That Needs Microsoft](https://www.forbes.com/sites/lutzfinger/2023/08/18/the-potential-moat-of-openaihow-openai-can-secure-its-investments/) · _Forbes_

Question 25

How did AI models respond when asked to create a plan for a U.S. president's third term?

Accepted Answer

_AI models compiled scattered ideas into a cohesive third term outline._

One of my students, Dane Troup, prompted OpenAI, Grok, DeepSeek and Gemini with a question framed as research for a political thriller novel. Google's Gemini did not answer, claiming it had to learn, though dishonestly. All the other models responded. The main ideas they produced have all been discussed before: amending the 22nd Amendment, reshaping the Supreme Court, and bombarding society with fake news. No single model provided a complete list, but together they offer a solid outline and plan.

— [Trump's Third Term — The Role Of AI In Creating A Strategy](https://www.forbes.com/sites/lutzfinger/2025/03/31/trumps-third-termhow-ai-can-help-to-overthrow-the-us-government/) · _Forbes_

Question 26

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 27

How did OCR technology affect the number of lawyers?

Accepted Answer

_OCR reduced costs but increased demand for lawyers overall._

Before OCR, legal teams relied on staff to manually read, annotate, and organize documents, a slow and expensive process. With OCR, lawyers could search and analyze text instantly, dramatically reducing costs. By lowering costs, OCR democratized access to legal services, increasing the overall demand for lawyers. Today, AI is once again reshaping legal work. FLANK.AI automates legal document summarization. Instead of spending hours reviewing paperwork, lawyers can now focus on higher-value tasks like strategy, case analysis, and client advocacy.

— [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 28

How did online teaching create a more inclusive classroom?

Accepted Answer

_Pre-screening answers leveled the playing field for introverts and extroverts._

Traditional classrooms favored extroverts, but pre-screening answers and quizzes let everyone participate. I used multiple-choice questionnaires and a Google Spreadsheet where students outlined discussion answers in one to two sentences. I could follow live and pick the most interesting answers to discuss in class. This pre-screening of arguments worked extremely well to create robust class discussion outcomes, giving all students a voice regardless of personality type.

— [How COVID Changed Our Classrooms? A Self-Experiment Of Online Teaching.](https://www.forbes.com/sites/lutzfinger/2020/05/14/how-covid-changed-our-classrooms-a-self-experiment-of-online-teaching/) · _Forbes_

Question 29

How did racial bias in a healthcare algorithm affect Black patients according to the Science study?

Accepted Answer

_Training data bias reduced Black patient care identification by half._

A study published in Science revealed that a healthcare algorithm showed racial bias in patient care. Specifically, Black patients who were assigned the same risk level as White patients were actually sicker. This bias had serious consequences, reducing the number of Black patients identified for extra care by more than half. The study points to initial training data as often being the culprit for such algorithmic bias, demonstrating how AI systems can perpetuate inequities in critical areas like healthcare.

— [President Biden Is Man, Woman And 40 Years Old - Why We Need Algorithmic Transparency](https://www.forbes.com/sites/lutzfinger/2021/02/04/president-biden-is-man-woman-and-40-years-old---why-we-need-algorithmic-transparency/) · _Forbes_

Question 30

How did the Apple Card algorithm discriminate despite not using gender as a direct feature?

Accepted Answer

_Apple Card offered vastly different limits to spouses with identical assets._

David Heinemeier Hansson was offered a credit limit 20 times higher than his wife despite having the same shared assets. The algorithm likely learned from a world where men earn on average more than women and concluded that men are more creditworthy. This shows how bias creeps in even when protected characteristics like gender aren't direct features. The training data itself was biased, reflecting existing societal inequalities, and the algorithm learned to perpetuate them without explicitly considering gender.

— [It's The Data, Stupid! Why AI Might Get It Wrong.](https://www.forbes.com/sites/lutzfinger/2020/05/28/its-the-data-stupid-why-ai-might-get-it-wrong/) · _Forbes_

Question 31

How did the U.S. chip ban inadvertently help DeepSeek become more competitive?

Accepted Answer

_Hardware restrictions forced resourceful innovation that beat U.S. competitors._

The U.S. imposed chip export bans to limit China's AI capabilities, assuming that without advanced hardware, AI development would stall. However, AI is software, created differently compared to a nuclear facility, making traditional protectionist measures ineffective or even counterproductive. DeepSeek, banned from using top hardware, ended up building a really resourceful approach and became better than top U.S. AI companies. This demonstrates that AI models are math and neural networks, essentially stacked linear regressions within activation functions that one cannot protect easily.

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

Question 32

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 33

How did we learn that location data from our phones was being sold?

Accepted Answer

_Pandemic dashboards exposed a hidden ecosystem of location tracking._

During the pandemic, many companies created dashboards showing aggregated movement data. This revealed that smaller companies like Unacast and Placer.ai gathered location information via hundreds of apps on your phone. Additionally, phone companies Sprint, AT&T, T-Mobile, and Verizon sold our location data. There is an entire ecosystem of apps that share a lot of information about us. WhatsApp is probably not the biggest privacy offender we have in our phones when you consider this broader data sharing landscape.

— [WhatsApp: We Should Discuss What Our Data Is Used For, Not Who Has It](https://www.forbes.com/sites/lutzfinger/2021/01/26/whatsapp-we-should-discuss-what-our-data-is-used-for-not-who-has-it/) · _Forbes_

Question 34

How did you create the Lutz-Clone and what can it do for students?

Accepted Answer

_An AI teaching assistant that mirrors knowledge and teaching style._

I created the Lutz-Clone as 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. It can help students code, discuss AI applications, and raise awareness of ethical challenges, all without me being physically present. The AI mirrors my teaching style and follows the course's learning path. Unlike generic AI tools, this clone is fine-tuned to introduce concepts logically and progressively.

— [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 35

How did you isolate trust as a factor in COVID vaccination rates?

Accepted Answer

_Using the EU to study trust's role in vaccination._

We looked towards the EU because the distribution of vaccines to Member States happened simultaneously and under the same conditions. But not all member states show the same trust, allowing us to isolate trust as a variable. We used the European Social Survey, a bi-annually collected survey of thousands of EU citizens from 29 countries, to measure trust levels. We then correlated that data with vaccination data from the European Centre for Disease Prevention and Control.

— [Get Vaccinated? How Trust In Institutions Determines COVID Vaccination Rates In The EU](https://www.forbes.com/sites/lutzfinger/2021/05/06/get-vaccinated-how-trust-into-the-institutions-determines-vaccination-rates-in-the-eu/) · _Forbes_

Question 36

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 37

How did you solve the fairness problem in peer reviews for your Cornell data products course?

Accepted Answer

_Statistical personas identify unfair reviewers in peer grading systems._

We developed a data validation process to ensure fairness in peer reviews. The process analyzes metrics like total review time, time spent on each review, and number of comments left on each project. Most variables followed a Gaussian distribution, letting us use statistical tools to identify problematic reviewers. We built student personas including Lazy Graders, Harsh and Happy Graders, and Misaligned Students. Examining just three to four projects from a single student typically gave us enough insight to determine if they fit these personas, and we would re-evaluate their feedback accordingly.

— [Hack For Fairness: Unlocking The Predictive Power Of Data Validation And Quality Assurance In Education](https://www.forbes.com/sites/lutzfinger/2023/04/03/hack-for-fairness-unlocking-the-predictive-power-of-data-validation-and-quality-assurance-in-education/) · _Forbes_

Question 38

How did you use quizzes to improve the flipped classroom model?

Accepted Answer

_Quizzes revealed where students struggled before class even started._

I asked students to do a quiz before coming to class to find out which areas they didn't understand. For example, a FiveThirtyEight video on k-means clustering Margaritas was fun, but the quiz revealed it failed to explain where the 'k' in k-means came from. This feedback let me spend more time on that point in class than I would have usually done. Content curation became the critical path, and quizzes helped me identify gaps.

— [How COVID Changed Our Classrooms? A Self-Experiment Of Online Teaching.](https://www.forbes.com/sites/lutzfinger/2020/05/14/how-covid-changed-our-classrooms-a-self-experiment-of-online-teaching/) · _Forbes_

Question 39

How do AI teaching assistants provide personalized feedback in your course?

Accepted Answer

_Capstone critiques available anytime, mirroring the instructor's feedback style._

In my course, AI-based teaching assistants provide tailored feedback on student projects. Students upload their capstone work, and the AI critiques it, just like I would. This gives students access to a personalized teacher anytime, anywhere. Am I truly replaced? Not entirely. What I've done is scale myself. The AI mirrors my teaching style and follows the course's learning path. This way, students can learn at their own pace with personalized AI tutors available 24/7.

— [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 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 41

How do chained AI models create better workflows than simple generative AI tools?

Accepted Answer

_Building dynamic workflows by connecting AI models in sequence._

Chained AI models connect multiple models together so the output of one becomes the input for the next, creating dynamic workflows rather than fixed steps. For an email assistant, the system first analyzes the email and figures out needed steps, then checks the calendar, looks up past emails, and predicts likelihood of attending. It creates three draft replies with different options and presents them to the user. This approach enables system integration, context search through RAG, predictions using traditional AI, and user control through multiple options.

— [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 42

How do encoder-decoder pairs work to create deepfakes?

Accepted Answer

_Extracting and reconstructing facial features to swap identities in images._

Encoder-decoder pairs are one of the main tools for creating deepfakes today. Going from an original image to a latent image is called encoding. Going from latent back to image is called decoding, similar to how you close your eyes and picture a cat based on knowledge stored in your brain. The encoder extracts latent features of face images, and the decoder reconstructs the face images from those features. To generate a deepfake, the decoder for the target draws the target image with the source's latent features, such as expressions.

— [Overview Of How To Create Deepfakes - It's Scarily Simple](https://www.forbes.com/sites/lutzfinger/2022/09/08/overview-of-how-to-create-deepfakesits-scarily-simple/) · _Forbes_

Question 43

How do existing laws already address AI concerns without new AI-specific regulation?

Accepted Answer

_Current laws already cover many AI use cases effectively._

Existing frameworks already address biases and ensure fairness without reinventing the wheel. If an AI-driven healthcare app misdiagnoses a patient, current product liability laws hold companies accountable. AI tools used in hiring must not introduce bias, and anti-discrimination laws like the Civil Rights Act already govern hiring practices and can be applied to AI-driven decisions. Instead of focusing on the technology itself, regulations should target how AI is used through these established legal frameworks.

— [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 44

How do Generative Adversarial Networks make deepfakes more realistic?

Accepted Answer

_Two competing neural networks train each other to create realistic fakes._

Generative Adversarial Networks, or GANs, were introduced in a 2014 academic study by Goodfellow and colleagues. GANs set up two neural networks to compete against each other. The first network, a generative neural network, creates a realistic image from a random seed through decoding. The second, a discriminative classifier, checks whether the image is real or fake. Through this competition, the two neural networks train each other and become more and more realistic over time. This architecture reinvigorated interest in deepfakes.

— [Overview Of How To Create Deepfakes - It's Scarily Simple](https://www.forbes.com/sites/lutzfinger/2022/09/08/overview-of-how-to-create-deepfakesits-scarily-simple/) · _Forbes_

Question 45

How do transparency requirements differ between the EU and the US regarding algorithmic decision making?

Accepted Answer

_EU grants rights against automated decisions; US relies on external investigations._

In the US, algorithms are not allowed to discriminate against race, but without transparency it is hard to surface those biases. It often takes external investigations like ProPublica's study showing Asian families were charged higher prices or the ACLU's analysis revealing Amazon's AI incorrectly identified 28 Congress members as criminals. In the EU, citizens have the right not to be subject to decisions based solely on automated means. However, it would be more powerful if mandatory registers were established making this information publicly accessible.

— [President Biden Is Man, Woman And 40 Years Old - Why We Need Algorithmic Transparency](https://www.forbes.com/sites/lutzfinger/2021/02/04/president-biden-is-man-woman-and-40-years-old---why-we-need-algorithmic-transparency/) · _Forbes_

Question 46

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 47

How do you categorize shoppers and how do LLMs serve all three types?

Accepted Answer

_LLMs now serve inspirational, research, and high-intent shoppers equally well._

In my UX research, I classify shoppers into three categories. Inspirational shoppers browse without a clear goal, often on Instagram or TikTok. Research shoppers are eager to learn and explore the best option, diving into Reddit threads or YouTube reviews. High-intent shoppers are ready to buy but looking for specific product information, visiting brand websites and combing through reviews. It's easy to see how all these needs can now be met through a conversation with LLMs like ChatGPT, Claude, Gemini, or Perplexity.

— [Study Shows LLM Conversion Rate Is 9x Better — AEO Is Coming](https://www.forbes.com/sites/lutzfinger/2025/06/19/study-shows-llm-conversion-rate-is-9x-better---aeo-is-coming/) · _Forbes_

Question 48

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 49

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 50

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 51

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 52

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 53

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 54

How does AI change the workflow for creative and product teams?

Accepted Answer

_Creation is now cheap; curation and courage become the scarce skills._

AI inverts the economics of creation. In the old model, creation was expensive. Research, storyboarding, scripting, production all cost time and money before you knew if the idea was any good. Now creation is cheap, which means you don't end up with one concept. You end up with fifty. The craft doesn't vanish, it migrates. The center of gravity moves downstream into selection, refinement, and courage. Knowing which of the fifty concepts is actually good, and being willing to fight for it, becomes the scarce skill.

— [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 55

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 56

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 57

How does AI transcription technology actually work under the hood?

Accepted Answer

_Three-step process turns sound into text using probabilities, not understanding._

AI transcription systems use neural networks trained on billions of examples of speech paired with text. The process has three steps. First, encoding sound by breaking audio into very short chunks and turning them into numerical features. Second, predicting text where a language model predicts the most likely word that fits both the sound and the context. Third, stringing it together, just like ChatGPT predicts the next word in a sentence. It's all probabilities. The model doesn't understand the words, it just plays the odds.

— [AI Fail — Trump Says 'Merz' —YouTube Transcribes 'Merkel'](https://www.forbes.com/sites/lutzfinger/2025/08/18/ai-fail---chancellor-merz-is-not-chancellor-merkel/) · _Forbes_

Question 58

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 59

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 60

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 61

How does data access create competitive advantages for countries according to Lutz?

Accepted Answer

_Liberal data policies determine which nations will lead in AI._

Biden's plan doesn't stress enough the importance of access to data, even though the U.S. is light-years ahead of Europe on this. China collects data aggressively, and if this continues, China will build better AI models and gain more economic advantages. In the next few years, countries with the most liberal data access will gain the most competitive power on the world stage. Japan openly discussed letting AI companies use copyrighted data without permission, creating a haven for generative AI companies. Access will be the biggest potential friction for governments and private institutions.

— [Biden's AI Plan And Why Central Control Might Not Work](https://www.forbes.com/sites/lutzfinger/2024/10/26/bidens-ai-plan--why-central-control-might-not-work/) · _Forbes_

Question 62

How does Earli's data flywheel work in practice?

Accepted Answer

_Oracle predicts, wet labs test, winners feed back in_

Earli's proprietary prediction model called Oracle proposes candidate genetic switches. A wet lab then tests up to 250,000 of them in a single batch, each tagged with a DNA barcode like a product at a checkout. The rare winners, the true outliers, get read out and fed back into the model. Every turn of the wheel makes the system smarter. That loop is data to model to candidate to experiment, and back to data. The company that builds the machine to generate proprietary data and the loop to learn from it wins.

— [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 63

How does jamming affect drone operations, and why does it make autonomy necessary?

Accepted Answer

_Communication denial forces drones to decide without waiting for human orders._

Jammers emit white noise across frequencies to drown out communication. GPS satellite signals go first because they are weak. Your connection to home base gets jammed second because you are far away and the jammer is closer. But drones close together, only hundreds of meters apart, can still communicate with each other because the nearest jammer is kilometers away. You have to assume information from home base arrives intermittently at best. This is why drones must make autonomous decisions. They cannot wait for human instructions that may never arrive.

— [AI on the Front Lines, with Alexander Fink](https://www.lutzfinger.com/transcript/2026-04-30-ai-on-the-front-lines-with-alexander-fink/) · _The Keynote on AI_

Question 64

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_

Q&A

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

Are these layoffs really about AI replacing humans?

Can a well-designed prompt outperform OpenAI's o1 model?

Can AI transcription tools introduce bias based on employee names?

Can we conclude that trust causes higher vaccination rates?

Did you actually replace yourself as a teacher with an AI clone?

Did you find other factors that influenced vaccination rates besides trust?

Does Google still have a chance against the new AI search competitors?

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

How accurate are current deepfake detection technologies?

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

How are AI agents rewriting the internet and changing shopping?

How are companies currently using AI tools to measure employee contributions?

How are jobs changing due to AI?

How are users and brands responding differently to AI agents in e-commerce?

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 fine-tuned language models transform B2B distributor expertise?

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

How can transparency tools like NFTs help combat deepfakes?

How could Europe break down existing monopolies to help AI startups compete?

How could regulation help OpenAI create a competitive moat?

How did AI models respond when asked to create a plan for a U.S. president's third term?

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

How did OCR technology affect the number of lawyers?

How did online teaching create a more inclusive classroom?

How did racial bias in a healthcare algorithm affect Black patients according to the Science study?

How did the Apple Card algorithm discriminate despite not using gender as a direct feature?

How did the U.S. chip ban inadvertently help DeepSeek become more competitive?

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

How did we learn that location data from our phones was being sold?

How did you create the Lutz-Clone and what can it do for students?

How did you isolate trust as a factor in COVID vaccination rates?

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

How did you solve the fairness problem in peer reviews for your Cornell data products course?

How did you use quizzes to improve the flipped classroom model?

How do AI teaching assistants provide personalized feedback in your course?

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

How do chained AI models create better workflows than simple generative AI tools?

How do encoder-decoder pairs work to create deepfakes?

How do existing laws already address AI concerns without new AI-specific regulation?

How do Generative Adversarial Networks make deepfakes more realistic?

How do transparency requirements differ between the EU and the US regarding algorithmic decision making?

How do venture firms use AI in their own operations?

How do you categorize shoppers and how do LLMs serve all three types?

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 change the workflow for creative and product teams?

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 AI transcription technology actually work under the hood?

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 data access create competitive advantages for countries according to Lutz?

How does Earli's data flywheel work in practice?

How does jamming affect drone operations, and why does it make autonomy necessary?

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

How does Lutz compare Clubhouse to the early internet and talk radio?

How does Marpai Connect use AI to overcome the small numbers problem?

How does multimodal AI improve the healthcare experience for doctors dealing with overwhelming patient data?

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 article compare the Steam Era, Digital Era, and AI Era?

How does the BYOD strategy with Microsoft create a moat, and for whom?

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 TikTok's algorithm differ from Clubhouse's approach to content discovery?

How does value-based care differ from traditional fee-for-service payment?

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 far behind is Europe compared to the United States in AI enterprise value?

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