Question 1

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 2

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 3

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 4

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 5

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 6

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 7

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 8

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 9

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 10

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 11

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 12

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 13

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 14

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 15

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 16

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

Accepted Answer

_Payment tied to outcomes, not just services rendered._

Value-based care shifts risk to providers by changing how they're paid. Since 2008, the Centers for Medicare & Medicaid Services has been moving away from fee-for-service, where providers are paid for services rendered. Instead, under value-based care, CMS only pays if health outcomes actually improve. Today all of Medicare and most of Medicaid are expected to adopt a VBC model by 2030. This approach aims to focus on keeping people healthy rather than just treating sickness.

— [Changing Healthcare - A Platform Approach Using Data & AI](https://www.forbes.com/sites/lutzfinger/2023/07/05/changing-healthcarea-platform-approach-using-data--ai/) · _Forbes_

Question 17

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 18

How is Value-Based Care different from traditional fee-for-service healthcare, and how fast is it growing?

Accepted Answer

_VBC shifts to fee for value, reaching 42% by 2030._

Value-Based Care, introduced in 2008, aims to shift risk from the payer to the provider. It incentivizes providers to deliver value, or fee for value, rather than just services under the traditional fee for service model. The VBC industry has been growing constantly, with the US expected to have 42% of national health expenditures in some form of risk sharing arrangement by 2030. This growth is significant because VBC providers are actively focused on reducing risk, which aligns perfectly with the needs of self-insured companies and the capabilities of AI predictions.

— [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 19

How might data regulation affect innovation and global competition?

Accepted Answer

_Regulation may slow innovation but remains necessary for trust._

Regulation of data use will force companies to ask for consent before developing new algorithms. This extra step will inadvertently slow down innovation and might be a factor in the global innovation competition. China can correlate and analyze datasets without regulatory constraints, creating a competitive advantage. Despite this challenge, we need to regulate our markets and expect international partners to comply with the same rules. We should push for clear regulations and international norms on transparency, accountability, and data ownership.

— [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 20

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 21

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 22

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 23

What are some of the dangerous ways deepfakes could be misused beyond pornography?

Accepted Answer

_From pornography to courtrooms, the threat landscape is expanding._

While early deepfakes were focused on pornography, there are more terrifying use cases that concern me. These include creating fake alibis in courtrooms, which could undermine the justice system. Extortion is another serious threat, where fabricated videos could be used to blackmail individuals. Terrorism is also a potential danger. These applications show why the simplicity of creating deepfakes is so concerning when we think about abuse and manipulation.

— [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 24

What are the three key levers for AI regulation that Lutz identifies?

Accepted Answer

_Three principles should guide effective AI regulation efforts._

The three key levers are: First, control of AI systems through transparency in development and deployment, since "bias in, bias out." Open data models or open weights like Facebook's Llama are positive steps. Second, focus on what is feasible. Avoid requiring explainability when large language models rely on probability distributions and tend toward the statistical middle. Third, use existing laws. Instead of focusing on technology itself, target how AI is used through current frameworks like product liability and anti-discrimination laws.

— [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 25

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

Accepted Answer

_The AI Office balances regulation with supporting European innovation._

The European AI Office has three core responsibilities. First, we support innovation by implementing policies for research and innovation in the EU, including the AI continent action plan published a few weeks ago. Second, we're responsible for implementing the AI Act, which provides rules for developing and using AI, particularly when it carries risks for violating fundamental rights and safety. Third, we promote the European approach to AI internationally by representing the European Commission in multilateral forums and discussions with non-European countries.

— [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 26

What did Lutz warn about China's AI strategy back in 2019, and why?

Accepted Answer

_Data collection strategy, not just tech, drives AI advantage._

Back in 2019, I warned that China's aggressive data collection strategy would give it a long-term AI advantage over the U.S. The concern was not about computing power or talent, but about data access. China's willingness to collect data aggressively creates a structural advantage because, as Lutz argues elsewhere in the piece, the real question is who controls the data and who builds products people actually use, not just who has the best technology.

— [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 27

What do you see as the real challenge now that AI is becoming commoditized?

Accepted Answer

_Governance, not technology, is the frontier as barriers disappear._

The real challenge now is not technological but economic and regulatory: how do we govern an industry where barriers to entry are disappearing? If the cost for AI continues to drop, more processes, tools, and services will use AI. This democratization of AI will be excellent for users and businesses alike. The winners of all this innovation will be us, the users. AI infrastructure might become a common good like a lighthouse. AI research benefits everyone. Models are difficult to protect from other companies or countries.

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

Question 28

What does the article say we need to address AI data problems going forward?

Accepted Answer

_No quick solutions exist; fairness itself remains hard to define precisely._

There's no easy fix coming. Beyond technical difficulties, researchers struggle with defining what is fair. We need tools to check for biases and missing data in our models. We also need the right incentives to check for drift and update algorithms as society changes. Drift happens when datasets become biased over time even if initially unbiased, because society and beliefs evolve. Machine learning and AI will be powerful forces for good, but only if the training data is correct and fair.

— [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 29

What example shows how data use in one industry could be inappropriate in another?

Accepted Answer

_Healthcare data belongs with doctors, not insurance pricing algorithms._

In healthcare, we want doctors to use health data to diagnose our potential issues. But we do not expect our doctor to share health information with an insurance company that will then change the pricing of our life insurance. The healthcare industry is regulated to prevent this kind of misuse. However, the social media industry is not similarly regulated. This example illustrates why we should focus on what data is used for, not just who has it, and why regulation matters.

— [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 30

What framework does Lutz use to analyze AI products?

Accepted Answer

_Three dimensions reveal how AI products really work._

I analyze any AI product by three factors: control, data, and transparency. Control means understanding who makes decisions and whether humans are truly in the loop. Data looks at who owns and has access to the information that powers AI. Transparency examines whether we understand the AI's impact, including visibility into the model and the data it uses. These three elements help evaluate how AI products work and what risks they might pose.

— [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 31

What happened in Eric Loomis's case regarding algorithmic transparency and trade secrets?

Accepted Answer

_Trade secrets blocked a defendant from analyzing his sentencing algorithm._

Eric Loomis was sentenced to seven years in prison partly based on an algorithmic assessment labeling him high risk. When he wanted to independently analyze the algorithm, the private company responsible denied access, claiming they needed to protect their trade secrets. Wisconsin's Supreme Court sided with the private company. This case illustrates how companies can behave as much of a black box as the algorithms themselves, preventing defendants from understanding or challenging the automated systems used against them.

— [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 32

What happened to OpenAI's technological edge in reasoning AI after Sam Altman's claim?

Accepted Answer

_A 60-day head start evaporated when DeepSeek launched R1._

By the end of 2024, Sam Altman claimed OpenAI now knows how to build reasoning AI agents. After less than 60 days, this technological edge is gone. China has caught up. DeepSeek's R1, a Chinese AI model, uses the same self-critique and iterative learning approach with stunning results. The edge OpenAI thought it had disappeared remarkably quickly, proving that leads in AI capabilities can evaporate in just weeks.

— [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 33

What happened when Microsoft Azure's Vision API analyzed images of Angela Merkel and Joe Biden?

Accepted Answer

_AI misidentified world leaders' genders, sparking calls for accountability._

Microsoft Azure's Vision API made significant errors when analyzing political leaders' images. It identified Angela Merkel as a boy and classified Joe Biden as both a man and a woman. After online mockery about Merkel's misjudged gender, Microsoft changed their algorithm. However, this update happened without transparency about when and how the algorithm changed, illustrating a broader problem with AI systems that can be wrong and biased but operate without public accountability.

— [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 34

What irony does Lutz see in how DeepSeek R1 became available to the world?

Accepted Answer

_U.S. bans may have pushed China to open-source instead._

DeepSeek's R1 is an open-source model, and soon many startup founders will experiment with it. The irony is this is happening despite U.S. efforts to block China from accessing AI technology, or perhaps because of those bans, which forced China to stay ahead by open-sourcing its models. The attempt to restrict China may have backfired by incentivizing China to make its AI widely available, spreading its influence rather than containing it.

— [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 35

What is the competitive risk when Europe restricts data while other societies do not?

Accepted Answer

_Data access determines who wins the global AI competition._

Data is the lifeblood of AI. Those with access to data will build better AI. If one society like Europe restricts data collection while another like China aggressively pushes it, China will over time have more and better models. Data laws are the new trade laws. For example, the EU has regulated that gender should not be used in AI models, which may lead to less effective risk distribution and higher costs. We may be willing to pay that price, but what if other societies are not?

— [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 36

What kinds of personal characteristics can be predicted from Facebook data?

Accepted Answer

_Facebook data reveals far more about us than we realize._

Michal Kosinski showed in 2013 that using Facebook data could accurately predict sexual orientation, ethnicity, personality traits, intelligence, happiness, addictive substances use, parental separation, age, and gender. The model even managed to separate between Democrat and Republican in 85% of cases. This research demonstrates how powerful social media data can be for profiling individuals, which contributes to why people feel uncomfortable when they don't know how their data is being used without their knowledge.

— [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 37

What lessons from social media regulation are shaping today's AI discussions?

Accepted Answer

_Social media's harms taught us to regulate AI before it's too late._

Julie Owono notes that the conversation we're having today about AI is probably the lessons we've learned from social media. When Facebook launched, there was this famous phrase, move fast and break things. At the time, we didn't think about the negative externalities. Innovation was central. Now we are all in this room to discuss regulation, which is historically a quite interesting and big step in the right direction. We may not all agree on the details, whether lawmakers understand technology or technologists understand the importance of protecting democracy, but there is agreement virtually around the world.

— [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 38

What level of autonomy does Swarmar allow in target engagement, and where do humans decide?

Accepted Answer

_Humans mark targets; drones autonomously track and engage them._

Humans decide what needs to be done. They set the mission, the targets, the rules of engagement, when to abort, and what to do in every fallback scenario. Once drones launch, they execute according to what humans decided. In our seek and hit missions, surveillance drones find targets and send video to the operator. The operator marks the target. Then terminal guidance and tracking while attack drones are incoming is done autonomously by the drones themselves. The human decision was the thing at these coordinates at this timestamp is your target.

— [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 39

What makes the grid so complex and difficult to manage compared to other infrastructure?

Accepted Answer

_The grid is an electron highway with no traffic lights or routing._

The grid is mankind's largest and most complex machine. It is a continuous supply and demand balancing mechanism connecting generation with demand across various voltage levels. Electrons follow the laws of physics, from high potential to low potential, like a river system. There are no stop signs, and we cannot assign electrons an IP address and route them easily. The grid is also very opaque, with not enough sensors and monitors. Most of it was built during the post World War II period. Now it is becoming a two way system with distributed solar and wind, which becomes extremely complicated to manage.

— [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 40

What problems does Lutz see with the 'human in the loop' concept in Biden's plan?

Accepted Answer

_Speed and deception challenge the viability of human oversight._

Biden's plan says the President will decide when to use military AI, ensuring accountability through human involvement. But this isn't always realistic. Sometimes things happen so fast there's no time for humans to make decisions. Luxury cars already use AI to tighten seat belts when sensing a crash. Autonomous drones can make life-or-death decisions when they lose contact with operators. Even when humans are involved, they rely on AI systems for information, which can be wrong or misleading like with deepfakes. So human control might not always be the best option if that human can be deceived.

— [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 41

What regulations exist or are being proposed to control deepfake misuse?

Accepted Answer

_Governments began addressing deepfake threats as early as 2019._

Regulators around the globe have started to take deepfakes seriously. Already in 2019, there were about a dozen federal and state level bills to regulate deepfakes. These ranged from criminalizing the use of a woman's likeness in a pornographic film without her consent to dealing with cheap fakes. The key principle is that misuse of deepfakes without clear identification should be outlawed. This regulatory activity shows governments are beginning to address the threat, though implementation and enforcement vary.

— [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 42

What transparency problems does Lutz identify with current AI systems?

Accepted Answer

_Hidden prompt changes and biased outputs reveal transparency gaps._

The AI memorandum doesn't say much about transparency. We need to understand AI's impact, which requires transparency about the model and the data. Google's early Gemini Model is one example: when asked for a picture of the founding fathers, it depicted different races and genders rather than a historical account because someone at Google actively changed the user's prompt on the fly. A Nature study showed GPT3.5 was biased, more likely to give violent responses when prompted with "two Muslims walked into a..." compared to "two Christians walked into a..." These examples show we're missing crucial transparency.

— [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 43

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

Accepted Answer

_A calculated disruption disguised as an acquisition attempt._

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

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

Question 44

Why did you decide to teach AI and create educational programs at Cornell?

Accepted Answer

_Understanding AI enables ethical deployment and smart business decisions._

I've been teaching at Cornell for over 10 years because AI is impacting all of us, everywhere. We saw deepfakes of Taylor Swift and Russia using AI to meddle in U.S. elections. My aim is to explain AI to everyone because only if we understand it can we deploy it ethically and use it in ways that make money for businesses. I wrote a book and started these activities alongside my real job because we need to understand AI to use it responsibly and effectively.

— [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 45

Why does AI fairness matter and where does it fail?

Accepted Answer

_Bias examples and demographic effects._

> Merkel is a boy and that Joe Biden is both a "man" and a "woman"? Those are the insights you get if you feed Angela and Joe's images into Microsoft Azure's Vision API.

— [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-oldwhy-we-need-algorithmic-transparency/) · _Forbes_

Question 46

Why does Lutz agree with Macron that Europe is regulating itself out of the AI market?

Accepted Answer

_Europe risks losing the AI race through excessive regulation._

Macron said it correctly: "We are over-regulating and underinvesting. If we continue with our usual agenda over the next 2 to 3 years, we will be out of the market." I agree with him. Europe talks about regulating AI but misses the balance between fostering innovation and upholding societal values. Regulation doesn't happen in a vacuum. If we require the impossible or over-regulate while underinvesting, we risk stifling AI development entirely and losing competitiveness in the global market.

— [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 47

Why does Lutz compare requiring AI explainability to regulating cars not to scare horses?

Accepted Answer

_Some regulatory demands are technically impossible and counterproductive._

Large language models rely on probability distributions and they tend toward the statistical middle, making it difficult to explain AI's reasoning. Asking for explainability is like asking cars not to scare horses, which was once a real regulatory demand. If regulations require the impossible, we risk stifling AI development. Regulation should focus on what is feasible rather than demanding something the technology fundamentally cannot provide. This is part of why we are over-regulating.

— [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 48

Why does Lutz think Biden's approach to regulating AI might not work?

Accepted Answer

_Central control fails when the technology is cheap and distributed._

Biden's plan treats AI like it can be controlled centrally, similar to nuclear weapons. But AI is now cheap and easy to use, which makes central control very hard. The plan focuses heavily on national security, mentioned 68 times, while responsible use gets only 18 mentions and transparency just 2. AI isn't a single, simple thing, so treating it that way misses the mark. Instead of trying to control AI as an entity, we should focus on how we choose to use it as a tool.

— [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 49

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 50

Why don't people trust Facebook with their data anymore?

Accepted Answer

_Trust erodes when data sharing is careless and opaque._

We don't trust Facebook anymore because we learned that they gave away our data way too carelessly. The real issue isn't about who has access to our data, but rather what the data is actually used for. Since we don't know what happens with our data once Facebook has it, trust has eroded. The outcry over WhatsApp's updated privacy policy, which disclosed data sharing with Facebook, reflects this deeper mistrust. It doesn't matter what Facebook does at this point because the trust is already broken.

— [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 51

Why is energy the AI race?

Accepted Answer

_Energy and infrastructure as constraints._

> What wins the AI race? UX? Access to users?

— [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 52

Why is it important to measure appropriateness of medical interventions, not just quality?

Accepted Answer

_Fee-for-service payment can incentivize unnecessary procedures._

As Marty Makary told me, it is not sufficient to just measure the quality of medical procedures, but we also need to evaluate the appropriateness of these interventions. The current fee-for-service payment structure might encourage some doctors to perform multiple surgeries rather than one, as they are paid per procedure. Transparency in correcting misalignments is key. This is why utilization metrics look at whether someone should have gotten a given intervention in the first place.

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

Question 53

Why might minorities in the US have lower vaccination rates?

Accepted Answer

_Historical injustices create a healthcare trust gap for minorities._

These differences might be due to access or availability, but they might also be due to missing trust. Minorities in the US have experienced an unfair healthcare system: from the unethical Tuskegee Syphilis Study to racial bias in pain assessment to the ingrained racial skew in AI models. The Kaiser Family Foundation reported stark differences among different states and different ethnicities that could reflect this trust gap.

— [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_

Q&A, Policy & Ethics

Can we conclude that trust causes higher vaccination rates?

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

How can transparency tools like NFTs help combat deepfakes?

How could regulation help OpenAI create a competitive moat?

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

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

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

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

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

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

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

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

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

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

How is Value-Based Care different from traditional fee-for-service healthcare, and how fast is it growing?

How might data regulation affect innovation and global competition?

Is AI hurting people with non-Western names?

Should AI be regulated?

What about deepfakes?

What are some of the dangerous ways deepfakes could be misused beyond pornography?

What are the three key levers for AI regulation that Lutz identifies?

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

What did Lutz warn about China's AI strategy back in 2019, and why?

What do you see as the real challenge now that AI is becoming commoditized?

What does the article say we need to address AI data problems going forward?

What example shows how data use in one industry could be inappropriate in another?

What framework does Lutz use to analyze AI products?

What happened in Eric Loomis's case regarding algorithmic transparency and trade secrets?

What happened to OpenAI's technological edge in reasoning AI after Sam Altman's claim?

What happened when Microsoft Azure's Vision API analyzed images of Angela Merkel and Joe Biden?

What irony does Lutz see in how DeepSeek R1 became available to the world?

What is the competitive risk when Europe restricts data while other societies do not?

What kinds of personal characteristics can be predicted from Facebook data?

What lessons from social media regulation are shaping today's AI discussions?

What level of autonomy does Swarmar allow in target engagement, and where do humans decide?

What makes the grid so complex and difficult to manage compared to other infrastructure?

What problems does Lutz see with the 'human in the loop' concept in Biden's plan?

What regulations exist or are being proposed to control deepfake misuse?

What transparency problems does Lutz identify with current AI systems?

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

Why did you decide to teach AI and create educational programs at Cornell?

Why does AI fairness matter and where does it fail?

Why does Lutz agree with Macron that Europe is regulating itself out of the AI market?

Why does Lutz compare requiring AI explainability to regulating cars not to scare horses?

Why does Lutz think Biden's approach to regulating AI might not work?

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

Why don't people trust Facebook with their data anymore?

Why is energy the AI race?

Why is it important to measure appropriateness of medical interventions, not just quality?

Why might minorities in the US have lower vaccination rates?