SOMEBODY STOP US: Who Will Control Artificial Intelligence?
Is artificial intelligence really slipping out of control, or are we moving toward a different kind of danger: a future in which only a handful of governments and technology giants are able to develop the most advanced AI systems?
For me, the central question is no longer simply, “How dangerous could artificial intelligence become?”
The more important question is:
“Who will control artificial intelligence, and could that control mechanism eventually turn into a monopoly that decides who is allowed to develop AI?”
2023: The First Serious Alarm Bells
Today’s debate is not entirely new. The year 2023 marked an important turning point in discussions about the future of artificial intelligence.
Geoffrey Hinton, one of the pioneers of modern AI, left Google in 2023 and began speaking more openly about the risks of artificial intelligence. He warned that AI systems were developing much faster than he had expected and that increasingly capable systems could create serious risks in the future.
In the same year, an open letter published by the Future of Life Institute made an even more extraordinary proposal.
Among its signatories were Elon Musk, Yoshua Bengio, Steve Wozniak and many researchers and technology leaders.
The letter called for at least a six-month pause in the training of AI systems more powerful than GPT-4.
At the time, many people considered the proposal unnecessarily dramatic.
The dominant logic of the technology industry was exactly the opposite: build larger models, use more computing power, collect more data, and push performance further.
Three years later, however, the picture looks quite different.
2026: This Time, the AI Companies Themselves Are Sounding the Alarm
Today, concerns about AI safety are no longer being raised only by academics, activists or technology critics.
Some of the companies leading the AI race are themselves calling for stronger safety and governance mechanisms.
OpenAI has supported mandatory, capability-based national AI safety rules. Anthropic has argued that a technology developing as rapidly and affecting society as broadly as artificial intelligence should not be governed by industry alone, and that governments also need enforceable rules. Google DeepMind, through its Frontier Safety Framework, has established critical capability thresholds for areas including cybersecurity, biological risk, autonomy, manipulation and loss of control.
In other words, the debate is no longer purely theoretical.
The companies developing the most capable AI systems are themselves evaluating scenarios in which future models could enable powerful cyberattacks, biological threats, large-scale manipulation or serious control problems.
And the concern is no longer limited to malicious humans using AI.
Increasing attention is also being paid to highly autonomous systems that may pursue unintended objectives, circumvent human supervision or behave in unexpected ways.
Therefore, it would be a mistake to dismiss the entire AI safety debate as manufactured fear. There are real technical and societal risks that deserve serious attention.
But Another Race Is Taking Place at the Same Time
This is where the second part of the problem begins.
Artificial intelligence is no longer merely a scientific or commercial technology.
It has become a strategic technology with implications for economic power, cybersecurity, defense, intelligence, scientific research and geopolitical influence.
This leads to another powerful argument, particularly in the United States:
“If we slow down, there is no guarantee that our competitors will slow down with us.”
The fact that the U.S. AI Action Plan prominently frames the issue in terms of “Winning the AI Race” illustrates how central this logic has become.
The plan emphasizes accelerating innovation, expanding AI infrastructure and maintaining American leadership in artificial intelligence.
Particularly in the context of competition with China, AI policy is increasingly beginning to resemble national security policy rather than conventional innovation policy.
This creates a remarkably difficult equation:
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On one side: More capable AI models could create serious risks. Testing, oversight, safety standards and, in some cases, restrictions may be necessary. |
On the other side: If one country or company slows down, there is no guarantee that its competitors will do the same. |
My concern begins precisely where these two arguments intersect.
Could Safety Regulation Become a Barrier to Entry?
I also believe that artificial intelligence needs meaningful control mechanisms.
But I strongly disagree with the assumption that “AI needs control” must automatically lead to the conclusion that control should rest in the hands of a few large governments and a few major corporations.
Regulation creates a very important structural problem:
A safety requirement that is relatively easy for a trillion-dollar company to satisfy can become an impossible barrier for a startup, university laboratory or independent research group.
Imagine that developing a frontier model eventually requires extremely expensive licenses, mandatory certification, multimillion-dollar safety infrastructure, audits that can only be conducted by a small number of approved organizations, or special government authorization.
On paper, the rules might apply equally to everyone.
In practice, however, only a very small number of organizations might still be able to develop advanced AI.
Would such regulation reduce competition?
Would it make it harder for new companies to emerge?
Would it restrict frontier AI research at universities?
Would it ultimately strengthen the position of the companies already dominating the field?
I believe these questions deserve much more attention.
Why the Nuclear Technology Analogy Keeps Appearing
Artificial intelligence is frequently compared with nuclear technology.
The analogy has obvious limitations.
An AI model is not a nuclear reactor. Software can be copied, modified and deployed in many different parts of the world.
Yet when we look specifically at frontier AI, one important similarity is becoming increasingly difficult to ignore.
Developing the most advanced systems requires rapidly increasing amounts of computing power, energy, data-center infrastructure, advanced semiconductors, highly specialized human capital and enormous financial resources.
If heavy regulatory requirements are added on top of these existing barriers, frontier AI development may naturally become concentrated in the hands of fewer and fewer actors.
And that creates an uncomfortable relationship between safety regulation and the centralization of technological power.
Why Open-Weight AI Is at the Center of This Debate
One of the most important battlegrounds in this discussion will be open-source AI and, more precisely, open-weight models whose model weights are available for others to access and use.
Open models offer important advantages.
Universities can conduct independent research. Startups can develop products without becoming completely dependent on large API providers. Organizations can run models on their own infrastructure without sending sensitive data to third parties. Countries can develop systems tailored to their own languages, cultures and strategic needs.
At the same time, openness can also make it easier to remove safety mechanisms or modify highly capable models for malicious purposes.
Therefore, there is no simple answer to the question of whether AI should be open or closed.
But one distinction is essential.
Discussing the risks of open models is one thing.
Using those risks as a justification for creating an AI ecosystem in which everyone becomes dependent on a handful of closed platforms is something entirely different.
But There Is Also Important Counter-Evidence
If we want to evaluate this issue seriously, we should not only look for evidence that supports our concerns.
We should also look for evidence that challenges them.
At present, there is no clear evidence that the major AI companies are collectively pursuing a strategy to eliminate open AI.
Anthropic CEO Dario Amodei has stated that his company does not advocate banning open-weight models and has described open models without dangerous capabilities as a public good.
More importantly, the current U.S. AI Action Plan explicitly includes policies aimed at supporting open-source and open-weight AI.
The plan recognizes the value of open models for startups, academic research, organizations working with sensitive data, and the broader international influence of the U.S. technology ecosystem.
Therefore, it would not be justified to claim that there is already a proven plan to shut down open-source AI.
My concern is different.
We might reach the same outcome even without any secret plan.
Poorly designed incentives, excessively expensive safety obligations, concentration of computing power and national-security concerns could gradually create a system in which only a small number of actors are capable of developing frontier AI — even if nobody originally intended to create such a monopoly.
The Next Stage: AI Sovereignty
For me, this issue extends far beyond competition between companies.
The larger question concerns countries.
In the future, having strong AI researchers may not be enough to develop the most capable AI systems.
Countries may also need access to advanced GPUs, massive data centers, energy infrastructure, leading-edge semiconductor technologies, significant capital and international supply chains.
If international licensing systems, export controls or authorization mechanisms for frontier AI are added to this structure, technological sovereignty will become an even more important issue.
At that point, we may have to ask:
If a country needs another country's permission to develop one of the most important general-purpose technologies of the future, can it really be considered technologically independent?
This question will become increasingly strategic for every country that wants to be not merely a user of artificial intelligence, but also a producer of it — including Türkiye.
So Is the Solution “No Regulation at All”?
No.
I believe both extremes in the AI debate are problematic.
The first extreme is to allow technology to advance without meaningful oversight and simply expect the market to solve serious safety problems.
The second extreme is to use safety concerns to create a system in which the ability to develop advanced AI is effectively controlled by a handful of companies and governments.
What we need is a governance model somewhere outside these two extremes.
Regulation should be based on measurable capabilities and risks rather than company names or model size alone.
Audit criteria should be transparent.
Universities and independent researchers should retain the ability to conduct meaningful AI safety research.
Compliance costs should not automatically push startups and smaller research organizations out of the field.
Not every open model should be treated as belonging to the same risk category.
A small language model and a frontier system capable of advanced biological design or sophisticated cyber operations should not necessarily be governed by identical rules.
And perhaps most importantly:
The rules governing the future of artificial intelligence should not be written only by the AI companies themselves.
When the companies being regulated also become the primary architects of the regulation, a natural conflict of interest may emerge.
The Real Issue May Be Bigger Than Safety
The developments of the past three years have made one thing increasingly clear to me.
AI safety is a real issue and deserves serious attention.
But from now on, it will not be enough to ask whether AI models themselves are safe.
We also need to ask how economic, technological and political power over artificial intelligence is distributed.
Because an AI ecosystem that is safe but completely controlled by a very small number of actors may not be an ideal outcome for humanity either.
I believe one of the most important technology-policy debates of the coming years will therefore be:
How should we control artificial intelligence?
And more importantly:
Who will control those who control AI?
My concern, therefore, is not that we should avoid controlling artificial intelligence.
Quite the opposite.
We should build serious, scientific, transparent and democratically accountable mechanisms for governing advanced AI.
But while trying to make AI safer, we should be careful not to turn one of the most powerful technologies in history into the permanent privilege of a few governments and a few giant corporations.
Because one day, the biggest problem may not be that artificial intelligence has escaped our control.
It may be that control over artificial intelligence has become far too concentrated.
What do you think?
Do frontier AI systems require stronger global oversight? Or could the mechanisms created to ensure safety eventually produce an even greater risk by concentrating technological power in the hands of a few countries and corporations?
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