The Subtle Art of Monopolizing New Technology

Monopolizing new technology is rarely the result of some grand, sinister plan. More often, it quietly emerges from self-interest. People do not set out to dominate a market; they simply recognize an opportunity to position themselves between groundbreaking technology and everyday users. The most effective tactic? Convince people that the technology is far too complex or risky to handle on their own.

It starts subtly. As soon as a new tool gains attention, industry insiders begin highlighting its technical challenges—security risks, integration headaches, operational difficulties. Some of these concerns may be valid, but they also serve a convenient purpose: You need us to make this work for you.

Startups are particularly skilled at this. Many offer what are essentially "skins"—polished interfaces built on top of more complex systems like AI models. Occasionally, these tools improve workflows. More often, they simply act as unnecessary middlemen, offering little more than a sleek dashboard while quietly extracting value. By positioning their products as essential, these startups slide themselves between the technology and the user, profiting from the role they have created. 

Technical language only deepens this divide. Buzzwords like API, tokenization, and retrieval-augmented generation (RAG) are tossed around casually. The average user may not understand these terms. The result is predictable: the more confusing the language, the more necessary the “expert.” This kind of jargon-laden gatekeeping turns complexity into a very comfortable business model.

Large organizations play this game just as well. Within corporate structures, IT departments often lean into the story of complexity to justify larger budgets and expanded teams. Every new tool must be assessed for “security vulnerabilities,” “legacy system compatibility,” and “sustainability challenges.” These concerns are not fabricated, but they are often exaggerated—conveniently making the IT department look indispensable.

None of this is to say that all intermediaries are acting in bad faith. New technology can, at times, require expert guidance. But the line between providing help and fostering dependence is razor-thin. One must ask: are these gatekeepers empowering users, or simply reinforcing their own relevance?

History offers no shortage of examples. In the early days of personal computing, jargon like RAM, BIOS, and DOS made computers feel inaccessible. It was not until companies like Apple focused on simplicity that the average person felt confident using technology unaided. And yet, here we are again—with artificial intelligence, blockchain, and other innovations—watching the same pattern unfold.

Ironically, the true allies of the everyday user are not the flashy startups or corporate tech teams, but the very tech giants so often criticized. Sometimes that criticism is justified, other times it is little more than fashionable outrage. Yet these giants, locked in fierce competition for dominance, have every incentive to simplify access. Their business depends on millions of users engaging directly with their products, not through layers of consultants and third-party tools. The more accessible their technology, the more users they attract. These are the unlikely allies of a non-techy person. 

For users, the best strategy is simple: do not be intimidated by the flood of technical jargon or the endless parade of “essential” tools. Always ask: Who benefits from me feeling overwhelmed? Whenever possible, go straight to the source—OpenAI, Anthropic, Google. If you truly cannot figure something out, seek help when you need it, not when it is aggressively sold to you.

Technology should empower, not confuse. The real challenge is knowing when complexity is genuine and when it is merely someone else’s business model.

Is AI Better Than Nothing? In Mental Health, Probably Yes

 In medical trials, "termination for benefit" allows a trial to be stopped early when the evidence of a drug’s effectiveness is so strong that it becomes unethical to continue withholding the treatment. Although this is rare—only 1.7% of trials are stopped for this reason—it ensures that life-saving treatments reach patients as quickly as possible.

This concept can be applied to the use of AI in addressing the shortage of counsellors and therapists for the nation's student population, which is facing a mental health crisis. Some are quick to reject the idea of AI-based therapy, upset by the notion of students talking to a machine instead of a human counselor. However, this reaction often lacks a careful weighing of the benefits. AI assistance, while not perfect, could provide much-needed support where human resources are stretched too thin.

Yes, there have been concerns, such as the story of Tessa, a bot that reportedly gave inappropriate advice to a user with an eating disorder. But focusing on isolated cases does not take into account the larger picture. Human therapists also make mistakes, and we do not ban the profession for it. AI, which is available around the clock and costs next to nothing, should not be held to a higher standard than human counselors. The real comparison is not between AI and human therapists, but between AI and the complete lack of human support that many students currently face. Let's also not forget that in some cultures, going to a mental health professional is still a taboo. Going to an AI is a private matter. 

I have personally tested ChatGPT several times, simulating various student issues, and found it consistently careful, thoughtful, and sensible in its responses. Instead of panicking over astronomically rare errors, I encourage more people to conduct their own tests and share any issues they discover publicly. This would provide a more balanced understanding of the strengths and weaknesses of AI therapy, helping us improve it over time. There is no equivalent of a true clinical trial, so some citizen testing would have to be done. 

The situation is urgent, and waiting for AI to be perfect before deploying it is not much of an option. Like early termination in medical trials, deploying AI therapy now could be the ethical response to a growing crisis. While not a replacement for human counselors, AI can serve as a valuable resource in filling the gaps that the current mental health system leaves wide open.

Where is the work? AI and Creativity

For ages, we have blurred the lines between ideation and execution, treating them as inseparable parts of creativity. Craftsmanship was tightly bound to originality. Think of Michelangelo working on the Sistine Chapel, a project that spanned nearly a decade. Where does his genius truly lie? In envisioning those profound images, or in the labor of painting them? What, exactly, is the essence of the work?

The rise of AI forces us to untangle these ideas and reconsider what it means to produce "human" work. Take a recent story I heard from from the audience of one of my talks: a person described how he fed an AI every detail about a retiring colleague, and the AI generated a speech so moving that it brought the retiree to tears. But the retiree, upon learning the speech's origin, was dumbfounded.

What is interesting is not the retiree’s reaction, but the storyteller's own oversight. He failed to see his own critical role in the process. By gathering the details, curating moments that best captured the retiree’s essence, he performed the most human part of the creative act. He mistook the act of turning those ideas into words as the creative work, but that is not the case.

AI, ironically, is pushing us to be more human, not more like machines. It is forcing us to recognize that our true contribution lies in the ability to think, to create, and to feel. As AI takes over the mechanical aspects of tasks we once considered integral to creativity—whether that is writing, painting, or coding—we are left with the more uniquely human roles: original thinking and emotional depth.

This shift reshapes our understanding of creativity and work. It shows that human value does not lie in production—the technical aspect of turning an idea into a product—but in the deeper conceptual and emotional layers that AI still cannot reach.

As we move forward, we are compelled to rethink productivity itself. The future will not belong to those who can outdo AI in execution, but to those who can combine AI’s strengths with our unique capacities for innovation, empathy, and insight.

The challenge we face is not to resist AI, but to fully embrace our humanity—to cultivate the traits that machines cannot replicate. With AI taking over the drudgery, we are freed to focus on higher-order thinking and those creative leaps that define human ingenuity.

Ironically, the more we develop artificial intelligence, the more we learn about what human intelligence really is. And in that discovery lies our future—a future where AI does not replace creativity, but elevates it to new possibilities.

Four Myths About AI

AI is often vilified, with myths shaping public perception more than facts. Let us dispel four common myths about AI and present a more balanced view of its potential and limitations.

1. AI Is Environmentally Costly

One of the most persistent claims about AI is that its use requires massive amounts of energy and water, making it unsustainable in the long run. While it is true that training large AI models can be energy-intensive, this perspective needs context. Consider the environmental cost of daily activities such as driving a car, taking a shower, or watching hours of television. AI, on a per-minute basis, is significantly less taxing than these routine activities.

More importantly, AI is becoming a key driver in creating energy-efficient solutions. From optimizing power grids to improving logistics for reduced fuel consumption, AI has a role in mitigating the very problems it is accused of exacerbating. Furthermore, advancements in hardware and algorithms continually reduce the energy demands of AI systems, making them more sustainable over time.

In the end, it is a question of balance. The environmental cost of AI exists, but the benefits—whether in terms of solving climate challenges or driving efficiencies across industries—often outweigh the negatives.

2. AI Presents High Risks to Cybersecurity and Privacy

Another major concern is that AI poses a unique threat to cybersecurity and privacy. Yet there is little evidence to suggest that AI introduces any new vulnerabilities that were not already present in our existing digital infrastructure. To date, there has not been a single instance of data theft directly linked to AI models like ChatGPT or other large language models (LLMs).

In fact, AI can enhance security. It helps in detecting anomalies and intrusions faster than traditional software, potentially catching cyberattacks in their earliest stages. Privacy risks do exist, but they are no different from the risks inherent in any technology that handles large amounts of data. Regulations and ethical guidelines are catching up, ensuring AI applications remain as secure as other systems we rely on.

It is time to focus on the tangible benefits AI provides—such as faster detection of fraud or the ability to sift through vast amounts of data to prevent attacks—rather than the hypothetical risks. The fear of AI compromising our security is largely unfounded.

3. Using AI to Create Content Is Dishonest

The argument that AI use, especially in education, is a form of cheating reflects a misunderstanding of technology’s role as a tool. It is no more dishonest than using a calculator for math or employing a spell-checker for writing. AI enhances human capacity by offering assistance, but it does not replace critical thinking, creativity, or understanding.

History is full of examples of backlash against new technologies. Consider the cultural resistance to firearms in Europe during the late Middle Ages. Guns were viewed as dishonorable because they undermined traditional concepts of warfare and chivalry, allowing common soldiers to defeat skilled knights. This resistance did not last long, however, as societies learned to adapt to the new tools, and guns ultimately became an accepted part of warfare.

Similarly, AI is viewed with suspicion today, but as we better integrate it into education, the conversation will shift. The knights of intellectual labor are being defeated by peasants with better weapons. AI can help students better understand complex topics, offer personalized feedback, and enhance learning. The key is to see AI as a supplement to education, not a replacement for it.

4. AI Is Inaccurate and Unreliable

Critics often argue that AI models, including tools like ChatGPT, are highly inaccurate and unreliable. However, empirical evidence paints a different picture. While no AI is perfect, the accuracy of models like ChatGPT or Claude when tested on general undergraduate knowledge is remarkably high—often in the range of 85-90%. For comparison, the average human memory recall rate is far lower, and experts across fields frequently rely on tools and references to supplement their knowledge.

AI continues to improve as models are fine-tuned with more data and better training techniques. While early versions may have struggled with certain tasks, the current generation of AI models is much more robust. As with any tool, the key lies in how it is used. AI works best when integrated with human oversight, where its ability to process vast amounts of information complements our capacity for judgment. AI’s reliability is not perfect, but it is far from the "uncontrollable chaos" some claim it to be.

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AI, like any revolutionary technology, invites both excitement and fear. Many of the concerns people have, however, are rooted in myth rather than fact. When we consider the evidence, it becomes clear that the benefits of AI—whether in energy efficiency, cybersecurity, education, or knowledge accuracy—far outweigh its potential downsides. The challenge now is not to vilify AI but to understand its limitations and maximize its strengths.

 

Navigating the AI Gold Rush: Skins, Security, and the Real Value Proposition

 The economic battle surrounding artificial intelligence is intensifying at an unprecedented pace. Major AI players like OpenAI, Google, Meta, and Anthropic are leading this technological revolution. Tech giants such as Microsoft, Amazon, and Apple, along with thousands of startups, are vying for a stake in this burgeoning market without being able to develop their own competitive models. Amidst this frenzy, a critical question arises: what exactly is being sold?

Two primary value propositions have emerged in this landscape: skins and security mongers. Skins are interfaces or applications that overlay major AI models, aiming to simplify user interaction. They cater to individuals lacking advanced prompting skills, offering a more user-friendly experience. Security mongers, on the other hand, emphasize heightened privacy and security, often exaggerating potential risks to entice users.

While both propositions seem valuable on the surface, a deeper examination reveals significant shortcomings. Skins promise to streamline interactions with AI models by providing preset prompts or simplified interfaces. For instance, a startup might offer a chatbot specialized in drafting business emails, claiming it saves users the hassle of formulating prompts themselves. However, is this convenience truly worth it?

Major AI models are increasingly user-friendly. ChatGPT, for example, has an intuitive interface that caters to both novices and experts. Users often find they can achieve the same or better results without intermediary platforms. Additionally, skins often come with subscription fees or hidden costs, meaning users are essentially paying extra for a service the primary AI model already provides. There is also the issue of limited functionality; skins may restrict access to the full capabilities of the AI model, offering a narrow set of functions that might not meet all user needs.

The second proposition taps into growing concerns over data privacy and security. Vendors claim to offer AI solutions with superior security measures, assuring users their data is safer compared to using mainstream models directly. But does this claim hold up under scrutiny?

Most of these intermediaries still rely on API connections to major AI models like ChatGPT. Your data passes through their servers before reaching the AI model, effectively adding another point of vulnerability. Introducing additional servers and transactions inherently increases the risk of data breaches. More touchpoints mean more opportunities for data to be intercepted or mishandled. Furthermore, major AI providers invest heavily in security and compliance, adhering to stringent international standards. Smaller vendors may lack the resources to match these safeguards.

For example, a startup might advertise an AI-powered financial advisor with enhanced security features. However, if they are routing data through their servers to access a model like GPT-4, your sensitive financial data is exposed to additional risk without any tangible security benefit. The promise of enhanced security becomes questionable when the underlying infrastructure depends on the same major models.

AI platforms have not introduced new risks to privacy or security beyond what exists with other online services like banks or credit bureaus. They employ advanced encryption and security protocols to protect user data. While no system is infallible, major AI models are on par with, if not superior to, other industries in terms of security measures. They use end-to-end encryption to protect data in transit and at rest, implement strict authentication measures to prevent unauthorized access, and conduct regular security assessments to identify and mitigate vulnerabilities. It is easy to opt out of providing your data to train new models. It is much more difficult to know what your vendors are going to do with your data.

In a market flooded with AI offerings, it is crucial to approach vendors' claims with a healthy dose of skepticism. Validate the functionality by testing whether the convenience offered by skins genuinely enhances your experience or merely repackages what is already available. Assess the security measures by inquiring about the specific protocols in place and how they differ from those used by major AI providers. Transparency is key; reputable vendors should be open about how your data is used, stored, and protected.

As the AI gold rush continues, distinguishing between genuine innovation and superficial value propositions becomes essential. Skins and security mongers may offer appealing pitches, but often they add little to no value while potentially increasing costs and risks. It is wise to try using major AI models directly before opting for third-party solutions. Research the backgrounds of vendors to determine their credibility and reliability. Seek reviews and testimonials from other users to gauge the actual benefits and drawbacks.

In the end, the most powerful tool at your disposal is due diligence. By critically evaluating what is being sold, you can make informed decisions that truly benefit you in the rapidly evolving world of AI. Beware of vendors selling either convenience or security without substantial evidence of their value. At the very least, take the time to validate their claims before making an investment.