After months of testing, writing, rewriting (and battling with a few AI hallucinations) the new book in my Solo Thought Leadership series is finally out: The Solo Thought Leader’s Guide to AI.

This isn’t another “50 AI prompts to go viral” kind of book. It’s a strategic, human-first guide for creators, consultants, and solo founders who wa…

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Just wanted to send you a quick note to announce the launch of The AI Advertising Podcast—a brand-new show that pulls back the curtain on how AI is transforming the advertising industry.

You may be saying, “Another AI podcast?”

Well, this isn’t your average marketing podcast. No long, boring, talking heads. Instead, each episode is a storytell…

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I started this weekly newsletter almost three years ago while working on my book, The Solo Thought Leader.

“Think. Write. Lead.” was about the intersection of writing and thought leadership. It reflected my interests as a content creator and marketer focused on thought leadership.

However, after a couple of years, it felt like I …

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The rise of generative AI has come with its own hope and doom—it will eliminate jobs but create new ones; it will automate our tasks but free us to be more creative.

That, at least, is the promise.

One of those promised new jobs is that of an AI ethicist—an expert in the ethical issues surrounding AI who can help companies, …

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This past week, Apple said it would delay iOS 18’s marquee AI features in the European Union, blaming Digital Markets Act (DMA) regulations. That means they would block the launch of Apple Intelligence, iPhone Mirroring on the Mac and SharePlay Screen Sharing in the EU this year, according to Bloomberg.

Articles in The Information and elsewhere, as well as tweets from technologists, keep calling doom on Europe because of its strong regulation to protect competition, privacy, and safety. 

In fact, the phrase "regulation stifles innovation" has been a common argument in the tech industry, particularly regarding AI regulations in Europe.

Is this claim true? Should we just let tech companies run wild, you know, laissez faire, laissez passer?

Well, you might have a gut-feeling response to this question. But don’t rush into an answer. Let’s apply a thinking framework to analyze this claim and see what we come up with.

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A Critical Thinking Framework for Analyzing Any Claim

In The Irrational Ape, David Robert Grimes provides a framework for critically analyzing claims by employing principles of critical thinking and scientific skepticism. Here are some key suggestions he offers:

1. Question the source: Evaluate the credibility of the source making the claim. Is it a reputable institution or an expert in the field? Be wary of sources with potential biases or conflicts of interest.
   

2. Check for evidence: Look for empirical evidence supporting the claim. Data from controlled studies, peer-reviewed research, or credible experiments usually back reliable claims. Anecdotal evidence or testimonials are not sufficient.
   

3. Consider alternative explanations: Think about other possible explanations for the phenomenon. Avoid jumping to conclusions without considering all plausible alternatives.
   

4. Look for logical consistency: Assess whether the claim follows logically from the evidence presented. Look for logical fallacies such as ad hominem attacks, false dichotomies, or appeals to emotion.
   

5. Replicability: Reliable claims should be replicable by other researchers. Check if the results have been reproduced in other studies or if they stand alone.
   

6. Evaluate the methodology: Scrutinize the methods used to gather and analyze the data. Were the studies well-designed? Were the sample sizes adequate? Were the statistical analyses appropriate?
   

7. Beware of confirmation bias: Recognize your own biases and the tendency to favor information that confirms pre-existing beliefs. Strive for objectivity and be willing to change your mind in light of new evidence.
   

8. Seek peer review: Claims that have been peer-reviewed by experts in the field are generally more reliable. Peer review helps to ensure that the research methods and conclusions are sound.
   

9. Understand the consensus: It’s important to know where the consensus is in scientific and technical fields. Individual studies can be misleading, while the broader scientific consensus often provides a more accurate picture.
   

10. Ask for clarification: Don’t be afraid to ask questions or seek clarification if a claim seems unclear or confusing. Understanding the details and context is crucial.

Applying the Framework

So, let’s apply these steps to critically analyze the claim that "regulation stifles innovation," in the context of European AI regulations.

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Photo by Maxime Gilbert on Unsplash

The Light Side vs. the Dark Side of the Force in Star Wars. The rivalry between Gryffindor and Slytherin houses in Harry Potter.

Pop culture is full of stories of good versus evil, of a struggle between two opposite sides. 

Religion (yin and yang), politics (left vs. right), and sports (Yankees vs. Red Sox) are also involved, with a constant battle between us and them.

One would think this is a black-and-white world, but the truth is that it is an infinite scale of grays.

This is the binary thinking fallacy, which simplifies complex issues into two opposing perspectives or choices. 

Why do people fall so easily into binary thinking?

Let’s think about children.

Children are more prone to binary thinking for several reasons. Their brains are still developing, especially the parts responsible for complex reasoning. They have limited life experiences to draw from, making it harder to see gray areas. Their moral reasoning is still forming, so they see things as right or wrong with little middle ground. Children usually develop more nuanced thinking skills as they grow and learn, but this takes time and exposure to diverse ideas and situations.

I would say that adults with binary thinking lack some of the experiences and perspectives that children lack, plus they add a few aggravating factors along the way, like:

• Their brains take shortcuts that can lead to oversimplified views.

• Some people prefer clear-cut answers and dislike ambiguity.

• Strong feelings can make them see things as all good or all bad.

• Less education or training in critical thinking can limit nuanced understanding.

• Some environments encourage black-and-white views on certain topics.

• Stress or time pressure can push them towards simpler thinking.

Interestingly, people might think in binary terms about some things but not others. For example, someone might have nuanced views about their job but think in black-and-white terms about politics. This difference can be due to:

• Knowledge: We tend to see more complexity in areas we know well.

• Emotional connection: Topics we care deeply about might trigger more extreme views.

• Context: Our thinking can change based on where or who we're with.

• Personal values: Core beliefs might lead to more rigid thinking in some areas.

When it comes to AI, binary thinking can lead to oversimplified views on complex issues. For example, people may perceive AI as inherently good or bad, view it as either a panacea or an existential threat, and adopt extreme positions on regulation and privacy concerns.

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To find real solutions, we must apply non-binary thinking to AI issues. Let’s examine two concrete scenarios to see how this might play out and some thinking frameworks to avoid binary thinking.

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Photo by Stefano Pollio on Unsplash

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I don’t know about you, but I love variety.

And books.

So, I thought I would bring some variety to this newsletter and tell you about some books I’ve been reading that have helped me create strong thinking frameworks. If I get a good response, I’ll make a monthly issue with my recommended reads to think better.

The two books I’ll review today are not spec…

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When Sam Altman was fired from his position as CEO of OpenAI last November, I found myself, like many others, aghast at the decision of OpenAI’s board. 

They said he wasn’t candid with his decisions and lied on several occasions. Certainly, this couldn’t be true. I mean, Sam seems like such a nice guy.

Reports came out back then criticizing the board members for their ideology (effective altruism) and defending Altman as a brilliant leader persecuted for his passion for progress. The fact that most OpenAI employees sided with him showed how wrong the board was, right?

RIGHT?!

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Fast forward to May 2024, and the public’s perception of Altman has taken a hit.

First, there was the blunder with Scarlett Johanson’s voice. Then, the leaked documents showing Altman was complicit in threatening former employees.

And now, the other side of the story about his firing back in November. In an interview with TED Tech, former OpenAI board member Helen Toner explained what led to the decision to fire Altman. 

And it’s not pretty. 

I won’t repeat what she said here (I suggest you listen to the podcast). BTW, Altman’s defenders wasted no time responding.

My point here is the realization that humans tend to trust famous people irrationally.   

This can have tragic implications, like people dying when a president suggests a bogus remedy for Covid.

Thankfully, most people dismissed that idea.

However, when it comes to AI, blindly trusting tech leaders like Sam Altman or Elon Musk has serious implications.

8 Problems with Trusting Celebrity CEOs

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The three biggest players in the AI space (OpenAI, Google, and Microsoft) have announced recently the new features of their generative AI systems. 

Apart from the disappointment of fans expecting GPT-5, most people seem quite impressed with the multimodal capabilities of ChatGPT-4o and Google’s Astra.

Oddly, nobody seemed to ask the question that crossed my mind as I saw the demos for these two products: 

Are people not concerned about the possibility of privacy invasion and corporate surveillance when giving video and audio access to AI tools?

Google’s demo was quite telling. 

In it, someone walks around an office with the phone’s camera on (is it being recorded?) and points at stuff. She may be asking particular questions, but the AI captures data of whatever is in the frame. This differs from the usual selfie videos posted on social media because it involves corporate settings with sensitive information.

Can you see the implications here?

According to Google’s Privacy policy for its Gemini apps (including Astra), some conversations are stored and used for feedback. They say they won’t sell the data, but it can be hacked or requested by the government. How much do you trust Google?

On the other hand, Microsoft announced a new feature called Recall, which is now “on” by default in Windows 11. This feature will regularly take screenshots of your computer. Privacy experts are already sounding the alarm about the implications.

Tech companies and governments have made the public believe that sharing their information is okay as long as they have nothing to hide (e.g., they are not doing anything illegal).

Nothing further from the truth.

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In this issue, I will explain why you should care about your digital privacy and how AI increases the risk of privacy invasion and surveillance.

When you’re done, you’ll have a different way of thinking about AI and privacy.

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A school principal in Baltimore was placed on administrative leave and shamed publicly after an audio clip of him making racist comments was posted on Instagram. 

According to the New York Times, “the audio clip, which lasts less than a minute, was shared more than 27,000 times and generated more than 2,800 comments, many calling for the principal to be …

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There’s a scene in The Avengers: End Game where they are discussing the laws of time travel.

The idea was to go back in time to kill Thanos as a baby or steal the Stones before Thanos got them, thus preventing the annihilation of half of living beings. To support their plan, Rhodey and Ant-Man cite every movie that deals with time travel, mentioning Star Trek, Terminator, Timecop, Time After Time, Hot Tub Time Machine, and Bill & Ted’s Excellent Adventure, and others.

The scientist among them, Bruce Banner (The Hulk), tells them that “time doesn’t work that way. Changing the past doesn’t change the future.”

It makes for a funny scene full of pop-culture references and science-fiction paradoxes. 

But it also illustrates how many people form their views about mystifying topics—through archetypical stories embedded in their culture.

Even movies.

No, especially movies.

What is the most common reference when discussing advanced AI's potential risks and dangers?

Terminator. Even though it’s an inaccurate metaphor.

Sadly, smart people are not immune to the influence of fictional stories in their thinking.

Elon Musk has cited several science fiction books, especially Isaac Asimov's The Foundation Series, as major influences on his thinking and the motivations behind his companies, such as SpaceX and Neuralink.

One can’t help but wonder if Musk’s existential fear of AI is (unconsciously) rooted more in science fiction than science.

Although I cannot prove it, I believe I can make a strong case for the role of cultural biases in AI thinking.

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East versus West and the Fear of AI

In many Eastern cultures, particularly in countries like China, Japan, and South Korea, there's a notable embrace of AI and technology in general. 

These countries have historically shown a strong inclination towards integrating advanced technology into everyday life. For example, Japan has a long-standing cultural affinity for robots and AI, reflected in their use of robots for tasks ranging from caregiving to customer service. 

The fear of AI causing catastrophic harm to humanity, often portrayed in dystopian movies and books as a rogue AI taking over the world, resonates globally. Still, the extent and focus of these fears can differ significantly between Eastern and Western perspectives.

In the West, there’s a prominent concern about existential risks from AI, influenced by high-profile tech figures known as AI doomers. 

For instance, in March 2023, The Future of Life Institute published an open letter titled "Pause Giant AI Experiments." The letter called for an immediate pause of at least six months on the training of AI systems more powerful than GPT-4. It cited potential risks such as AI-generated propaganda, extreme job automation, human obsolescence, and loss of societal control. It received over 20,000 signatures from prominent AI researchers, industry leaders like Elon Musk and Steve Wozniak, and public figures like Yuval Noah Harari.

In contrast, in many Eastern cultures, especially in technologically advanced countries like Japan, South Korea, and China, the discourse centers more on the benefits of AI and how it can contribute to societal good. 

Concerns about issues such as privacy, security, and employment exist, but the existential fear of AI destroying humanity is generally less emphasized. Instead, these societies might focus more on managing and integrating AI effectively to enhance economic growth and improve daily life, with a strong belief in human oversight and control.

Why this difference?

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Let’s play a game.

I’m going to ask you ten questions about AI. For each question that you feel you have a solid answer to, give yourself 2 points; for each question that you don’t have an answer to but have given some thought to, you get 1 point; and if you have no clue and have never thought about it, then you get no points.

Count your total score, and then I’ll tell you mine. Let’s see who wins. 

Ready?

Here are the 10 questions:

1. How can we ensure AI systems are free from biases, and what mechanisms can be put in place to detect and mitigate biases that arise?

2. How can AI systems be made transparent so that users can understand and trust their decisions?

3. How do we balance the benefits of AI with the need to protect individuals' data (privacy)?

4. Who should be held responsible when an AI system makes a mistake or causes harm? Is it the developers, the company that deployed the system, the end-users, or a combination of stakeholders?

5. How do we prevent AI from becoming a tool for excessive surveillance and control, potentially encroaching on individual freedoms and rights?

6. What policies should be implemented to manage the economic impact of AI, particularly concerning job displacement?

7. AI autonomy vs. human oversight: To what extent should AI systems operate independently? Where do we draw the line for human oversight?

8. Ethical data usage: How can we ensure that the data used in training AI systems is ethically sourced and used?

9. How can AI be developed and deployed in ways that respect cultural and ethical differences across and within societies?

10. What frameworks and institutions are necessary to govern the rapidly advancing capabilities of AI at both national and international levels?

What was your score? Mine was nine. And I didn’t get two points on a single one. Actually, very few people would. These are hard questions with many points of view, depending on who you ask.

This is so much so that according to a survey of 100 Fortune 1,000 IT leaders, 100% are concerned about the security risks of AI, and 98% have paused generative AI (GenAI) projects as a result. About 42% said they are worried about the ethics of the GenAI, particularly the inherent societal biases of training data (26%) and lack of regulation (26%).

Unless you’re an AI ethicist, this is new territory for you—as it is for most company executives now trying to make decisions about implementing AI tools.

So, how should we approach these complex questions to make ethical decisions?

Let’s start by looking at some common approaches and two frameworks for considering tough ethical questions about AI. In the end, I provide two useful checklists for applying these frameworks.

Three Approaches to AI Ethics

I see three common ways AI ethics is approached in the world today: as a technologist, an industrialist, or an ethicist.

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The Marketing Apocalypse is imminent.

At least, that’s what recent surveys and reports about the impact of AI on marketing jobs are prophesying.

According to Forrester, AI will replace 4.9% of US jobs by 2030, with a higher-than-average level of displacement more likely for frontline marketers.

In an interview in late 2023, OpenAI’s CEO, Sam Altman said that soon “95% of what marketers use agencies, strategists, and creative professionals for today will easily, nearly instantly and at almost no cost be handled by the AI — and the AI will likely be able to test the creative against real or synthetic customer focus groups for predicting results and optimizing. Again, all free, instant, and nearly perfect. Images, videos, campaign ideas? No problem.”

Perhaps they are exaggerating, right?

Marketing influencers like Randy Fishkin think so. He recently made a “bold prediction” on LinkedIn: “I don't think AI/LLMs are coming for your job." In fact, I'm willing to bet $5,000 (in a charitable donation) that in five years, less than 10% of all information-economy jobs and less than 5% of marketing jobs will be displaced by AIs.”

So, we have two sides here: those who believe AI will create more marketing jobs and those who believe it will replace most marketers.

How are we to think about this?

First, let’s look at any possible logical fallacies from both sides.

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Dan Brown (the author of The Da Vinci Code) has an entertaining novel with a prediction gone wrong.

Inferno’s plot is about the threat of overpopulation destroying the planet.

It plays out from Paul Ehrlich's book, The Population Bomb (1968), which predicted massive shortages of food and resources from overpopulation by the end of the 20th century.

The Un…

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Having a title doesn’t make you a thought leader.

Neither being a successful business leader.

That’s obvious, right?

Well, apparently not, as many companies today want to push out thought leadership content by having their CEOs share their opinions and thoughts.

And having interviewed many business leaders, I can tell you that not all have leading thoughts.

…

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I ghostwrite thought leadership articles for several subject matter experts.

One common theme in these articles in the past few months has been the benefits of personalization in marketing and advertising.

Personally (pun intended), I’m somewhat skeptical about the whole personalization thing.

It’s become a trending buzzword in marketing (it was actuall…

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“Do you trust Facebook? What about Apple or Google?”

That’s a question I asked my students during a discussion about privacy statements and data collection by big tech companies.

They had to pick a company between Amazon, Meta, Google, X, and Spotify, research their data collection policies, and answer these questions:

• What information do they collect?

• What…

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In the past few months, I’ve closed my Facebook, X, and Instagram accounts.

I’m only keeping LinkedIn.

Apart from the fact that I despise Meta for its shady ethics, there are more practical reasons to quit social media.

And if you’re serious about thought leadership, perhaps you should too.

If you spend too much time scrolling through social media (especial…

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This semester, I’m teaching a marketing research class at the local college.

Part of what I’m teaching my students is to use the scientific method to make marketing decisions—not their gut or their instincts.

Because in most companies I’ve worked for, marketing decisions are educated guesses passed down from the CEO or a rehash of what worked before for …

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