AI pioneer Geoffrey Hinton warns that the world would be better off without artificial intelligence

Tech magnate Elon Musk presented his vision of the future at the 'All-in Summit,' where he detailed the impact of artificial intelligence (AI) and robotics on human life. Musk predicts that in the near future, technology will free people from the need to work for a living.

Musk is confident that soon, Tesla's humanoid robots, such as 'Optimus,' combined with AI, will be able to perform any task analogous to a human. He stated that this will lead to the emergence of a 'Universal High Income,' making labor not a necessity, but merely a choice.

According to Musk, the transformation will affect not only physical labor but also highly skilled positions, including office work and data processing. He believes that automation will create such an abundance of goods and services that people can live significantly better than they do now.

Unlike the concept of 'Universal Basic Income' (UBI), Musk envisions a more extensive development—a universal high income. Production, transportation, agriculture, and healthcare will become so accessible and cheap that they will ensure a high quality of life for everyone.

Despite the seemingly utopian nature of this scenario, Elon Musk emphasized that the path to such a future will be difficult and fraught with numerous threats. He acknowledged that the period when machines begin replacing jobs will not be easy.

Families whose income depends entirely on salaries and physical labor may face financial difficulties before this 'great prosperous era' arrives. A fundamental question arises about how wealth will be distributed in an automated world and who will truly own these machines. Musk has previously mentioned in podcasts that this transition will be a 'shaky and difficult journey.'

Uttar Pradesh Chief Minister Yogi Adityanath made significant remarks on the use of Artificial Intelligence (AI) during the 'TechSangam 2026' event held in Lucknow. He stated that while AI can be helpful, it should not be the final decision-maker. The Chief Minister also highlighted controlling criminals through technology and the expansion of forensic facilities in the state. On this occasion, he shared an incident of a land mafia who had registered government land in his name, and at that time, the police situation was extremely pitiable.

At the 'TechSangam 2026' event at the Uttar Pradesh State Institute of Forensic Science (UPSIFS) in Lucknow, Chief Minister Yogi Adityanath spoke about the importance of Artificial Intelligence, cybersecurity, and forensic science. His view was that technology should be used to improve human life, but humans should not become completely dependent on technology.

Chief Minister Yogi Adityanath emphasized that 'Artificial Intelligence can be our assistant, but AI should not be our decider. We must follow our own judgment, not AI's judgment. If we start doing everything by depending entirely on it, that power itself can become a threat to us.' He mentioned that currently, discussions are happening on AI and modern technologies in Lucknow, whereas ten years ago, imagining such events in Uttar Pradesh was a dreamlike idea.

The Chief Minister also referred to the land on which the Uttar Pradesh State Institute of Forensic Science is currently established. He explained that initially, this land was in disarray. A land mafia had manipulated documents to register this land in his name. Yogi Adityanath stated that he was informed of this matter by the then DGP of Uttar Pradesh. He said that even the world's largest civil police was helpless against the land mafia.

According to the Chief Minister, he requested the file related to the DGP and investigated the matter. Subsequently, an FIR was registered. When the accused reached the High Court, another FIR was also registered. Eventually, the person surrendered and started apologizing.

The Chief Minister explained by giving the example of the popular TV serial CID that five family members used to watch this show together. This show depicted how doctors could reach the culprit by examining a person's death, such as hair or nails. He mentioned that such examination was not possible at that time, but today, this work is being done through modern forensic technology. With the help of science and technology, it has become difficult for criminals to escape.

Chief Minister Yogi Adityanath gave an example of a mafia who committed his first crime in Uttar Pradesh in 1980. When his government took action against him in 2017, he was sent to Punjab. He mentioned that it took three years to bring that mafia back from Punjab to Uttar Pradesh, for which the government had to go to the Supreme Court. According to the Chief Minister, finally, after an interval of 42 years, that mafia received punishment.

The Chief Minister also stated that previously, adequate facilities for forensic investigation were not available in Uttar Pradesh. DNA samples used to remain in the waiting list for ten years for testing. Due to the lack of timely availability of scientific evidence, the accused would escape legal consequences. He informed that now, 12 forensic laboratories are ready in the state, and more than 150 forensic vans are available. Samples can now be tested with the help of modern means and reports can be presented before the court.

The Chief Minister further stated that before 2017, there were only two cyber police stations and four forensic laboratories in Uttar Pradesh, and the conviction rate was less than 30 percent. Currently, cyber police stations are established in all 75 districts of the state, and work is progressing towards establishing a forensic laboratory in every division. He concluded that 'TechSangam 2026' is discussing topics such as AI, cyber forensics, quantum technology, and data protection. The goal of this event is to connect technological innovation with citizen safety, a robust judicial system, and new opportunities for education, research, and employment for the youth.

In recent years, the development of artificial intelligence has been perceived as a technological race, where after each launch, new comparisons, benchmark tables, and predictions emerged about which company was approaching leadership in the next major digital transformation. It often seemed that the entire debate boiled down to one question: which model is the best at the moment?

However, after a recent immersion in Silicon Valley, the author got the feeling that this question, while still relevant, is no longer central. Part of this understanding stems from the fact that he visited Silicon Valley almost ten years after his first visit.

There is something interesting about immersing oneself in an ecosystem that unites some of the world's most influential companies, investors, and universities. Just as when traveling to any culture, we begin to absorb local peculiarities, in the US, especially in California, you inevitably become influenced by the speed of releases, technology consumption, and the constant feeling that the future is being built there.

Something similar is happening in Silicon Valley: there is a natural tendency to focus on themes dominating regional conversations and consider them an accurate reflection of the coming years. This often has a basis, as many significant technological changes of the last few decades took place there before reaching the rest of the world.

But any highly connected environment carries a risk: the risk of confusing consensus with absolute certainty. This is what attracted the most attention during the trip.

Unlike most public discussions about AI, few conversations revolved around competition between models or the search for the next technological leap capable of changing the market. A more frequent topic was how artificial intelligence is integrating into the products, processes, and operations of organizations, rather than which laboratory owns the most advanced system at the moment.

This shift in focus manifested differently throughout the week. Discussions with executives from Snowflake and Elementum, for example, were not about which model ranks highest or which company would release the next big thing. The discussion was focused on the transformation of enterprise software. The general consensus was that a phase is beginning where intelligent agents start interacting directly with large volumes of data, performing tasks that previously required manual navigation through systems, report analysis, and making several operational decisions by a human.

For decades, most enterprise software was created to organize information and facilitate human labor. Now, there is an expectation that these systems will not just present information but also act upon it. This may be one of the most important changes brought about by AI. The impact is not only in the technology itself but also in how companies begin to function. For the first time in many years, the discussion seems less related to the tool being used and more to the ability to quickly, integratively, and efficiently turn data into action.

At the same time, some visits showed that not everyone follows the exact same path. For instance, at Liquid AI, there was a viewpoint contrasting with most popular narratives in the sector. While a large part of the industry continues to bet on ever-larger models and ever-more powerful infrastructure, this company is developing more compact systems designed for local operation on devices such as mobile phones, vehicles, and onboard equipment.

The interest in this concept is not just in the technology itself. It reminds us that innovation does not happen in one direction. There is a natural inclination to link technological progress with increased computational power, investment, and model size. However, in many cases, the task is precisely the opposite: creating more efficient, accessible, and easily integrated solutions into people's daily lives and businesses.

Perhaps this was one of the main lessons of the trip. When we look at the AI market from afar, it seems like there is one race going on. But when we look closely, we realize that many different races are happening simultaneously. There are large generative models; there are specialized systems; there are multimodal applications combining text, image, audio, and video; there are enterprise solutions designed to solve very specific problems; and there are companies betting on making all of this easier and more distributed.

Therefore, the idea that the market will move towards one dominant technology is becoming less likely. A scenario of coexistence of different approaches is emerging, each suited for specific contexts, constraints, and needs.

This shift is also changing how organizations approach technology strategy. For a long time, the logic was relatively simple: find the best available tool and implement it. With the advent of AI, the equation includes a number of other variables. Issues of integration, governance, privacy, speed of adoption, operational costs, and business compliance are becoming as important as the complexity of the chosen technology.

In practice, a company may gain much greater value from a simple, well-integrated solution in its processes than from using extremely advanced technology in isolation. Perhaps this is why the discussion of implementation is taking up more space than the discussion of capabilities.

Another aspect that caught attention was the frequent turning of technological conversations into conversations about people. This became particularly evident during the visit to Stanford. Despite the university being in one of the most advanced ecosystems in the world for AI, a significant part of the discussions remained focused on topics such as education, talent development, shaping organizational culture, and leadership development.

This does not seem coincidental. The history of Silicon Valley has never been exclusively a story about technology. It has always been deeply connected to the ability to transform knowledge into innovation. Tools change. Platforms change. Companies appear and disappear. But preparing people who can learn, adapt, and create remains a constant element.

This point becomes even more relevant as AI becomes accessible to more and more organizations. Access to the technology, naturally, tends toward democratization. Knowledge of how to use it strategically, however, remains a difficult-to-replicate advantage.

The systematic implementation of AI requires much more than just hiring a platform or providing new resources to teams. It requires rethinking processes, developing competencies, revising workflows, creating oversight mechanisms, and preparing people to work in an environment where part of the analysis, decision-making, and execution is supported by intelligent systems.

Therefore, the main takeaway the author had after this experience is not about which company will lead the AI race or which model will become the standard in the coming years. The feeling is that we are leaving the phase where the center of discussion was isolated technology and entering a stage much more focused on practical application. The next phase of AI will not be defined by one revolutionary model replacing all others. It will be built on a combination of various technologies, the ability to integrate them into existing processes, and most importantly, the ability of organizations to turn these capabilities into tangible results.

Ultimately, artificial intelligence remains a technological topic. But after so many conversations during the trip, it is hard not to conclude that it is increasingly turning into a discussion of strategy, execution, and organizational adaptation. Perhaps this is why the most important question is no longer who possesses the most advanced AI. The question that seems to be gaining more importance is another: who will be able to use it better.