In recent years, the world artificial intelligence witnessed extremely fast and even dramatic changes, which is often described as a new global "arms race". Big tech companies have committed their resources and teams to develop and launch the latest and most advanced AI models. This struggle for technological dominance is not only a business conflict, but also a strategic game in which innovation is key to survival in the global market.
Between innovation and risk
Companies like Meta, Google, OpenAI and Antropika are constantly advancing their technologies, striving to develop models that not only meet projected market needs, but also shape the way individuals and organizations operate around the world. The development of these systems, with the potential to completely transform business, education and everyday life, creates a strong pressure to quickly bring new AI models to market – often without enough prior testing to ensure greater control over the process and reduce possible social consequences.
In this context, the competition of AI companies for a competitive advantage opens up the broader question of the future of the relationship between man and technology, as well as the way in which technological development can shape society and our everyday life.
Generation 2026.
In the spring of 2026, the new generation of AI models is no longer just trying to be "smarter" - it has the ambition to take on concrete roles in everyday life, work and decision-making. The differences between the announced Muse Spark (Meta company), Spud (OpenAI), Mythos (Antropik) and Gemma 4 (Google) models are best understood not through their technical specifications, but through the question: what exactly will they do for us.
According to available media information, Muse Spark will function as a digital organizer, almost like a personal assistant, using a system of multiple AI agents. Instead of a single linear model, it uses a "router" architecture that delegates tasks to smaller, specialized models (agents). In other words, Muse Spark internally "distributes" the work - one agent researches, another compares, and a third optimizes price and time. It is focused on everyday decisions (shopping, traveling, etc.) to reduce the user's burden and waste of time.
Photo: Unsplash/Sanket MishraArtificial intelligence
Although still codenamed, Spud goes a step further: he's an autonomous enforcer, not just an advisor. Its intended role is not to tell you what to do, but to do it for you. For example, instead of a user searching for flight information, comparing prices and booking a ticket, Spud is given a goal ("I want the cheapest trip to Tokyo next week") and goes through all the steps independently - search, selection, booking and payment. Nevertheless, this high level of AI automation causes the most discussions about security, precisely because of access to money and personal data.
Mythos, on the other hand, is designed for in-depth planning and strategic analysis. Unlike standard models that give an immediate response, this model uses the so-called "Chain of Thought" to simulate deep thinking before offering a solution. By breaking down complex problems (like security systems) into multiple levels, it actually predicts scenarios. Because of its ability to "think ahead", it is a powerful tool that requires strict control to avoid misuse.
And finally, Gemma 4 is not a general-purpose application, but a powerful open-source tool that gives development teams and developers complete freedom to create custom AI solutions. Its key advantage is the ability to run locally, customize and integrate into different systems. It serves as a foundation on which companies and developers can build their own, specialized AI tools, with full control over data and processes. In other words, it is a platform for the development of other AI solutions.
Strategist under lock and key
However, the story of the aforementioned Mythos model seems somewhat like a plot from science fiction and is actually a combination of real technological power and serious fear of the consequences that its creators warn about. The company Antropik has developed an extremely powerful artificial intelligence model - so powerful that they have decided not to offer it to the general public for now.
Mythos has proven to be extremely adept at finding weaknesses in software and digital systems. He knows how to "see" cracks where even experienced experts do not notice them. What would take a team of experts weeks or months of work, Mythos can do in a very short time. And that's where the problem arises. In the wrong hands, that same power can be used for cyber attacks, allowing others to more easily and quickly find ways to breach digital security.
That's why Antropiko CEO Dario Amodei openly said that the model is potentially dangerous, and instead of immediately releasing it to the market, they decided to keep it in a controlled environment and use it primarily for defense: to detect and patch vulnerabilities before someone abuses them. Basically, Mythos showed one important thing about the future of artificial intelligence: as models get smarter, the line between a useful tool and a potential risk gets thinner. And that is precisely why we are witnessing that sometimes the greatest progress does not go straight to the market - it goes to the key first.
Each model, different purpose
When these roles are placed side by side, it becomes clear that the AI market is beginning to split into functions that were once more or less unified. The difference between the two is not only in technology, but in how much control the user actually retains and how much he is willing to give up to the "system".
Therefore, Muse Spark, Spud, Mythos and Gemma 4 already represent four clearly profiled visions of the future of artificial intelligence. Perhaps the most important change is reflected in this variety of approaches: artificial intelligence is no longer a single technology that develops in one direction, but a complex ecosystem of different ideas and solutions. The key question that arises is which of these visions will be closest to the real needs of people who are just learning how to live in an environment where digital minds are becoming ubiquitous.
Photo: Pixabay/LeoWhat jobs will artificial intelligence take over?
In the modern techno-economy, however, the same pattern repeats itself: States invest enormous resources - from subsidies and tax breaks to infrastructure, training and access to public data - to attract and support large technology companies. In that process, resources are used that are, in essence, collective: energy, land, scientific research financed from the budget, and even social stability as a prerequisite for business development.
New accountability architecture
However, this dynamic is made problematic by the way in which value is ultimately distributed. Although the investment in the start-up comes from a wider social base, the profits mostly flow to a narrow circle of owners and shareholders of big-tech companies. This is how a paradox arises: risk and cost are largely "public", while profit is completely privatized.
This model would not necessarily be controversial if there was proportional redistribution – through higher taxes, reinvestment in local communities or long-term public benefits. However, in practice, the opposite often happens: companies use global tax incentives, negotiate special conditions and additionally strengthen their bargaining power in relation to the countries that initially supported them. In this sense, the discussion about artificial intelligence and the future of technology cannot be separated from the questions of ownership, control and distribution of profits.
"New Deal" in the era of artificial intelligence
But some new winds are beginning to blow from the very center of the great technological machinery. OpenAI CEO Sam Altman recently presented a document outlining a vision for a "new social contract" adapted to the era of artificial intelligence. As Axios reports, Altman was the first big tech leader to propose specific ways in which governments should tax, regulate and redistribute the wealth created by the development of AI technology. Altman warns that the arrival of superintelligence is inevitable and will cause tectonic changes comparable to the industrial revolution, which is why he proposes a series of radical measures to ensure that technological progress remains at the service of humanity.
His plan involves the creation of a national public fund that would be partially financed by the AI industry to pay profits to citizens, while at the same time shifting the tax burden from labor to capital and corporate profits. In addition, he advocates the introduction of a universal "right to AI" through the widest availability of basic models, shortening the working week to four days without reducing income, as well as establishing new, "portable benefits" for employees that would be completely independent of the employer. Altman's conclusion is cautionary: any indecision in implementing these reforms could directly lead to massive job losses, deep social unrest, and a loss of control over advanced AI systems.
And that is already the scenario of dystopian Hollywood movies that probably no one would want to see come to life.
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