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:

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.

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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?

#ArtificialIntelligence #AISafety #AIGovernance #OpenSourceAI #OpenWeights #FrontierAI #AGI #TechPolicy #AIRegulation #AISovereignty #OpenAI #Anthropic #DeepMind #MuratKarakayaAkademi

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Are Closed LLMs Losing Their Lead? Open-Weight Models Are Moving Closer to the Frontier

Inkling, Kimi K3, and Qwen3.8 changed the open-weight LLM landscape within just a few weeks, raising important questions about the future of the AI race.

As we entered the summer of 2026, the strongest players in the large language model race still looked very familiar: OpenAI, Anthropic, and other closed-model providers. Anthropic released Claude Fable 5 on June 9, 2026, while OpenAI made the GPT-5.6 family generally available on July 9 following a limited preview that began on June 26.

Both models were introduced with strong reasoning, coding, and agentic capabilities. Their advanced capabilities and potential security risks, particularly in cybersecurity, also brought them into broader discussions involving the U.S. government.

Then, within only a few weeks, three major developments changed the picture on the open-weight side.

🗓️ A landscape that changed within weeks

🇺🇸 July 15, 2026 — Inkling: Thinking Machines Lab released Inkling, an open-weight model with 975 billion total parameters and 41 billion active parameters per token.

🇨🇳 July 16, 2026 — Kimi K3: Moonshot AI announced Kimi K3, a 2.8-trillion-parameter model. Its full model weights were released on July 27.

🇨🇳 July 19, 2026 — Qwen3.8 preview: Alibaba introduced Qwen3.8-Max-Preview in the 2.4-trillion-parameter class. Qwen3.8-Max was formally introduced on August 3, followed by the open-weight Qwen3.8-2.4T-A95B model on August 12.

🇺🇸 Inkling: A major open-weight move from the United States

The significance of Inkling is not simply that it has 975 billion parameters. The model uses a Mixture-of-Experts architecture with around 41 billion active parameters per token and can reason across text, images, and audio.

It also supports agentic coding, tool use, and context windows of up to one million tokens.

What makes Inkling particularly interesting, in my view, is something else. While Chinese companies had become far more visible in frontier open-weight model development, this time a powerful and genuinely open-weight model came from a new U.S.-based AI laboratory.

🇨🇳 Kimi K3: Open-weight models approach the 3-trillion-parameter class

Immediately after Inkling, Moonshot AI introduced Kimi K3, pushing open-weight model scale into a new range.

The model contains 2.8 trillion parameters. Its MoE architecture includes 896 experts, while only 16 are activated for each token. This allows the total model size to become extremely large without requiring every parameter to participate in every inference step.

Kimi K3 also attracted attention for more than its size. Around the time of its release, it ranked first on Frontend Code Arena, ahead of Claude Fable 5.

That does not mean Kimi K3 is now simply “better than GPT-5.6 or Fable 5.” Closed frontier models still hold an advantage in many broad evaluations. But the fact that an open-weight model can outperform the strongest closed models in selected coding and agentic tasks is a substantial change compared with the situation only a few years ago.

🇨🇳 Qwen3.8: Alibaba also moves into trillion-scale open models

Alibaba’s Qwen3.8 move showed that these developments were not isolated events.

Qwen3.8-Max, previewed in July, reached its formal 2.4-trillion-parameter release in August. The Qwen team then released the weights of Qwen3.8-2.4T-A95B as well.

As a result, within a short period, the open-weight ecosystem saw new models at 975 billion, 2.4 trillion, and 2.8 trillion parameters.

⚖️ The real issue is not parameter count

A 2.8-trillion-parameter model is not automatically better than a 500-billion-parameter model. Training data quality, architecture, post-training, reinforcement learning, tool use, inference infrastructure, and active parameter count can matter just as much as total model size. The real shift is that open-weight models are beginning to operate in the same capability range as frontier systems.

🔓 Are open-weight models still six or seven months behind?

For several years, there was a fairly reasonable rule of thumb: the strongest capabilities first appeared in closed systems from companies such as OpenAI, Anthropic, or Google, while comparable open models followed months later.

That assumption now needs to be reconsidered.

Models such as Kimi K3 can enter the same performance region as GPT-5.6 and Claude Fable 5 on selected tasks. The Qwen family continues to improve rapidly in coding and agentic workloads. Inkling shows that a U.S.-based open-weight frontier model is also possible.

Open models have not yet surpassed closed models across every dimension. But in many areas, the delay no longer looks like “wait for the next model generation.”

To me, this is the more important change.

🇨🇳 Are Chinese companies catching up with U.S. frontier labs?

Recent developments make this question increasingly difficult to dismiss.

Chinese companies are no longer producing only smaller or cheaper alternatives. With Moonshot AI and Alibaba, we are now seeing models at true frontier scale, and some of them are being released with their weights.

This is particularly significant because Chinese companies are operating under restrictions affecting access to some of the most advanced U.S. AI chips. Despite this, they continue to scale through work on model architecture, sparsity, attention mechanisms, quantization, and distributed inference.

The competition may therefore increasingly shift from “Which company has the best model?” to “Which national ecosystem can still develop frontier models?”

🔐 Will Chinese companies eventually close their strongest models?

This may become one of the most interesting questions for the future of the open-weight ecosystem.

Chinese model developers have benefited significantly from open-weight strategies. Their models have spread quickly across the world, developer communities have formed around them, inference platforms have added support, and the companies themselves have gained global visibility.

But would a company follow the same strategy if it genuinely believed it had the most capable model in the world?

Or would it become commercially more attractive to keep the strongest model behind an API while releasing the previous generation as open weights?

The next few model generations may provide an answer.

🌍 Where are the countries outside the U.S. and China?

This may be one of the least discussed aspects of the current AI race.

Two ecosystems now stand out very clearly in frontier model development: the United States and China.

Europe has strong research institutions and important AI companies. Japan and South Korea have advanced semiconductor industries. India has a very large software and engineering talent base. Yet at the trillion-parameter frontier, the competition is increasingly concentrated in the U.S. and China.

The reason is that this race cannot be run with good researchers alone. It requires tens of thousands of accelerators, data-center infrastructure, energy, capital, training data, distributed-systems engineering, and a very large research ecosystem.

One of the strategic questions for the coming years will therefore be whether other countries attempt to build their own frontier-model infrastructure or become increasingly dependent on models developed in the U.S. and China.

❓ Five questions for the next phase

🔹 Can Chinese companies fully catch up with U.S. frontier-model companies technologically?

🔹 Will the performance gap between open-weight and closed models disappear within the next few generations?

🔹 To what extent will independent evaluations confirm the superiority suggested by selected benchmarks from companies such as OpenAI and Anthropic?

🔹 Will Chinese companies continue their open-weight strategies even for their most capable models?

🔹 Can countries outside the U.S. and China re-enter the frontier-model race?

🚀 Is a new era beginning for open-weight LLMs?

It is still too early to say that open-weight models have completely overtaken closed systems.

But something else can now be said with much more confidence: open-weight models are no longer sitting at the edge of the frontier AI race. They are moving closer to its center.

The rapid appearance of Inkling, Kimi K3, and Qwen3.8 is one of the clearest signs of this shift.

If the performance gap between open and closed models continues to narrow, the consequences will extend far beyond benchmark tables. It will affect how AI infrastructure is built, which model providers companies depend on, how countries think about AI sovereignty, and even the future of today’s API-centered business models.

What do you think? When will open-weight models catch closed models in overall performance? Or will the most capable models always remain closed?

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Murat Karakaya Academy

#MuratKarakayaAkademi #ArtificialIntelligence #LLM #OpenWeights #OpenSourceAI #KimiK3 #Qwen38 #Inkling #GPT56 #ClaudeFable5 #AgenticAI #GenerativeAI

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Kapalı LLM’lerin Saltanatı Sarsılıyor mu?

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Kapalı LLM’lerin Saltanatı Sarsılıyor mu? Açık Ağırlıklı Modeller Frontier Seviyeye Yaklaşıyor

Inkling, Kimi K3 ve Qwen3.8 ile birkaç hafta içinde değişen açık ağırlıklı LLM dengesi, yapay zekâ yarışının geleceği hakkında önemli sorular ortaya çıkarıyor.

2026 yazına girerken büyük dil modeli yarışının en güçlü oyuncuları yine oldukça tanıdıktı: OpenAI, Anthropic ve diğer kapalı model sağlayıcıları. Anthropic 9 Haziran 2026'da Claude Fable 5'i, OpenAI ise 26 Haziran'daki sınırlı önizlemenin ardından 9 Temmuz'da GPT-5.6 ailesini genel kullanıma sundu.

Her iki model de yüksek akıl yürütme, kodlama ve ajan yetenekleriyle duyuruldu. Güvenlik riskleri ve özellikle gelişmiş siber yetenekleri nedeniyle ABD yönetimiyle yürütülen değerlendirme süreçleri de modellerin ne kadar güçlü hâle geldiği tartışmasını beraberinde getirdi.

Tam bu sırada, açık ağırlıklı LLM dünyasında birkaç hafta içinde oldukça önemli üç gelişme yaşandı.

🗓️ Birkaç haftada değişen tablo

🇺🇸 15 Temmuz 2026 — Inkling: Thinking Machines Lab, 975 milyar toplam ve 41 milyar aktif parametreye sahip açık ağırlıklı Inkling modelini yayımladı.

🇨🇳 16 Temmuz 2026 — Kimi K3: Moonshot AI, 2,8 trilyon parametreli Kimi K3'ü duyurdu. Model ağırlıkları 27 Temmuz'da kamuya açıldı.

🇨🇳 19 Temmuz 2026 — Qwen3.8 önizlemesi: Alibaba, Qwen3.8-Max-Preview ile 2,4 trilyon parametre sınıfına çıktı. Qwen3.8-Max 3 Ağustos'ta resmen tanıtıldı ve 2,4 trilyon toplam, yaklaşık 95 milyar aktif parametreli açık ağırlıklı Qwen3.8-2.4T-A95B 12 Ağustos'ta yayımlandı.

🇺🇸 Inkling: ABD'den uzun süre sonra güçlü bir açık ağırlık hamlesi

Thinking Machines Lab tarafından yayımlanan Inkling'in önemi yalnızca 975 milyar parametreye sahip olması değil. Model, her token için yaklaşık 41 milyar aktif parametre kullanan Mixture-of-Experts (MoE) mimarisiyle çalışıyor ve metin, görüntü ve ses üzerinde doğal olarak akıl yürütebiliyor.

Ayrıca ajan tabanlı kodlama, araç kullanımı ve bir milyon token'a kadar bağlam desteği sunuyor.

Bence Inkling'i asıl ilginç hâle getiren nokta ise başka: Uzun süredir açık ağırlıklı frontier model geliştirme tarafında Çinli şirketler çok daha görünürken, bu kez güçlü ve gerçekten açık ağırlıklı bir model ABD merkezli yeni bir laboratuvardan geldi.

🇨🇳 Kimi K3: Açık model ölçeği 3 trilyon parametre sınırına dayandı

Inkling'in hemen ardından Moonshot AI tarafından açıklanan Kimi K3, açık ağırlıklı modellerin ölçeği açısından yeni bir sınırı temsil etti.

Model toplam 2,8 trilyon parametreye sahip. MoE yapısında bulunan 896 uzmandan yalnızca 16'sı her token için aktif hâle geliyor. Böylece toplam model büyüklüğü olağanüstü seviyelere çıkarken, bütün parametrelerin her token için çalıştırılması gerekmiyor.

Kimi K3 yalnızca büyüklüğüyle de dikkat çekmedi. Yayınlandığı dönemde Frontend Code Arena'da Claude Fable 5'i geçerek ilk sıraya yerleşmesi özellikle ilgi çekiciydi.

Bu sonuçtan “Kimi K3 artık GPT-5.6 veya Fable 5'ten daha iyi” sonucunu çıkarmak doğru olmaz. Genel değerlendirmelerde kapalı frontier modeller hâlâ birçok alanda avantajlarını koruyor. Ancak belirli kodlama ve ajan görevlerinde açık ağırlıklı bir modelin en güçlü kapalı modelleri geçebilmesi, birkaç yıl önceki tabloyla karşılaştırıldığında oldukça önemli.

🇨🇳 Qwen3.8: Alibaba da trilyon parametre ölçeğinde açık modele geçti

Alibaba'nın Qwen3.8 hamlesi bu gelişmelerin tek seferlik olmadığını gösterdi.

Temmuz ayında önizlenen Qwen3.8-Max, Ağustos ayında 2,4 trilyon parametreli resmî sürümüne ulaştı. Ardından Qwen ekibi Qwen3.8-2.4T-A95B modelinin ağırlıklarını da yayımladı.

Böylece kısa süre içerisinde açık ağırlıklı ekosistemde 975 milyar, 2,4 trilyon ve 2,8 trilyon parametre ölçeğinde üç yeni model ortaya çıktı.

⚖️ Asıl mesele parametre sayısı değil

2,8 trilyon parametreli bir modelin 500 milyar parametreli bir modelden otomatik olarak daha iyi olduğunu söyleyemeyiz. Eğitim verisinin kalitesi, model mimarisi, post-training süreci, reinforcement learning, araç kullanımı, inference altyapısı ve aktif parametre sayısı en az toplam model büyüklüğü kadar önemli. Buradaki asıl değişim, açık ağırlıklı modellerin hem ölçek hem de yetenek açısından frontier modellerin bulunduğu bölgeye girmeye başlaması.

🔓 Açık ağırlıklı modeller hâlâ 6–7 ay geriden mi geliyor?

Birkaç yıl boyunca oldukça makul bir genelleme vardı: En güçlü yetenek önce OpenAI, Anthropic veya Google gibi kapalı model sağlayıcılarında ortaya çıkıyor; benzer seviyedeki açık modeller ise aylar sonra geliyordu.

Bugün bu varsayımın yeniden değerlendirilmesi gerekiyor.

Kimi K3 gibi modeller bazı görevlerde GPT-5.6 ve Claude Fable 5 ile aynı performans bölgesine girebiliyor. Qwen serisi kodlama ve ajan görevlerinde sürekli ilerliyor. Inkling ise ABD'den gelen açık ağırlıklı bir frontier modelin de mümkün olduğunu gösteriyor.

Açık modeller henüz kapalı modelleri bütün boyutlarda geçmiş değil. Ancak aradaki gecikme birçok alanda artık “bir sonraki nesli beklemek” seviyesinde görünmüyor.

Bana göre tartışılması gereken asıl değişim de burada.

🇨🇳 Çinli şirketler Amerikan şirketlerini yakalıyor mu?

Son gelişmeler bu soruyu giderek daha ciddi hâle getiriyor.

Çinli şirketler yalnızca daha ucuz veya daha küçük modeller üretmiyor. Moonshot AI ve Alibaba örneklerinde artık doğrudan frontier ölçeğinde modeller görüyoruz. Üstelik bunların bir bölümü model ağırlıklarıyla birlikte yayımlanıyor.

Bu durum özellikle önemli çünkü Çinli şirketler aynı zamanda ABD'nin en gelişmiş yapay zekâ çiplerine yönelik ihracat sınırlamaları altında çalışıyor. Buna rağmen model mimarisi, sparsity, attention mekanizmaları, quantization ve dağıtık inference gibi alanlardaki mühendislik çalışmalarıyla ölçek büyütmeye devam ediyorlar.

Dolayısıyla önümüzdeki dönemde yarış yalnızca “hangi şirketin en iyi modeli var?” sorusundan ibaret olmayabilir. “Hangi ülkenin yapay zekâ ekosistemi frontier model geliştirebiliyor?” sorusu da giderek daha önemli hâle geliyor.

🔐 Çinli şirketler güçlü hâle geldikçe modellerini kapatır mı?

Bence açık ağırlıklı ekosistemin geleceği açısından en ilginç sorulardan biri bu.

Çinli model geliştiricileri bugüne kadar açık ağırlık stratejisinden önemli faydalar elde etti. Modeller hızla dünya çapında kullanılmaya başladı, geliştirici toplulukları oluştu, inference sistemleri bu modellere destek ekledi ve şirketlerin küresel görünürlüğü arttı.

Ancak bir şirket gerçekten dünyanın en güçlü modeline sahip olduğuna inandığında aynı stratejiyi sürdürür mü?

Yoksa en güçlü modeli API arkasında tutup, bir önceki nesli açık ağırlıklı olarak yayımlamak daha avantajlı hâle mi gelir?

Önümüzdeki birkaç model nesli bu sorunun cevabı açısından oldukça belirleyici olacak.

🌍 Peki ABD ve Çin dışındaki ülkeler nerede?

Bu yarışın belki de en az tartışılan tarafı burada.

Bugün frontier model geliştirme yarışında iki ekosistem çok belirgin biçimde öne çıkıyor: ABD ve Çin.

Avrupa'nın güçlü araştırma kurumları ve önemli yapay zekâ şirketleri var. Japonya ve Güney Kore ileri yarı iletken teknolojilerine sahip. Hindistan büyük bir yazılım ve mühendislik insan kaynağı barındırıyor. Buna rağmen trilyon parametre ölçeğindeki frontier modellerde yarış giderek ABD ve Çin arasında yoğunlaşıyor.

Çünkü bu yarış yalnızca iyi araştırmacılarla yürümüyor. On binlerce hızlandırıcı, veri merkezi altyapısı, enerji, sermaye, eğitim verisi, dağıtık sistem mühendisliği ve çok büyük bir araştırma ekosistemi gerekiyor.

Dolayısıyla önümüzdeki dönemin stratejik sorularından biri de şu olacak: Diğer ülkeler kendi frontier model altyapılarını kurmaya çalışacak mı, yoksa ABD ve Çin tarafından geliştirilen modellere bağımlı mı kalacak?

❓ Önümüzdeki dönem için beş soru

🔹 Çinli şirketler Amerikan frontier model şirketlerini teknolojik olarak tamamen yakalayabilecek mi?

🔹 Açık ağırlıklı ve kapalı modeller arasındaki performans farkı birkaç nesil içinde tamamen ortadan kalkacak mı?

🔹 OpenAI ve Anthropic gibi şirketlerin seçilmiş benchmarklarla oluşturduğu üstünlük algısını bağımsız değerlendirmeler ne ölçüde doğrulayacak?

🔹 Çinli şirketler en güçlü modellerinde de açık ağırlık politikasını sürdürecek mi?

🔹 ABD ve Çin dışındaki ülkeler frontier model yarışına yeniden katılabilecek mi?

🚀 Açık ağırlıklı LLM dünyasında yeni bir dönem mi başlıyor?

Açık ağırlıklı modellerin kapalı modelleri tamamen geçtiğini söylemek için henüz erken.

Fakat artık başka bir şeyi söylemek mümkün: Açık ağırlıklı modeller frontier yapay zekâ yarışının kenarında değil, giderek daha fazla merkezinde yer alıyor.

Inkling, Kimi K3 ve Qwen3.8'in kısa aralıklarla ortaya çıkması bu değişimin güçlü işaretlerinden biri.

Eğer açık modeller ile kapalı modeller arasındaki performans farkı gerçekten kapanmaya devam ederse, bunun etkisi yalnızca benchmark tablolarında görülmeyecek. Yapay zekâ altyapılarının nasıl kurulacağı, şirketlerin hangi modellere bağımlı olacağı, ülkelerin yapay zekâ egemenliği ve hatta bugünkü API merkezli iş modellerinin geleceği de bundan etkilenecek.

Sizce açık ağırlıklı modeller kapalı modelleri genel performansta ne zaman yakalayacak? Yoksa en güçlü modeller her zaman kapalı sistemlerde mi kalacak?

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Murat Karakaya Akademi

#MuratKarakayaAkademi #YapayZeka #LLM #OpenWeights #AcikAgirlik #OpenSourceAI #KimiK3 #Qwen38 #Inkling #GPT56 #ClaudeFable5 #AgenticAI #GenerativeAI #ArtificialIntelligence

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