By Ali Fawaz | Staff Writer
The AI arms race is characterized by a shifting finish line, an antithetical relationship between pace and progress, and a severe intolerance for stagnation. It is a war, not a sprint, and it’s one fought in data centers, not in battlefields, over neither land, nor wealth, but dominance of the meta-computational market itself. In just a year, the world was flooded with AI models – ChatGPT, Claude, Gemini, DeepSeek, and more. Each new release promises to be bigger, faster, and smarter, but not always meaningfully so; the natural question arises: “Why is everyone running so fast?”. We’ve seen frenzied tech battles before – the Space Race, the rise of search engines, the smartphone war – but this time, the stakes are different. There is no clear-cut destination, only an insatiable urge to grow more than the rest.
Two forces are locked in combat, pulling AI in opposite directions. On one hand, private and closed-source AI, represented by companies like OpenAI, Google DeepMind, and Anthropic, fortifies its gates and locks its models behind high garden walls. These companies sell access to the consumer but hoard the keys to improvement. They are
primarily motivated by profit, market safety, and dominance, but their strategy carries the risk of AI centralization, consolidating control in the hands of a few. On the other hand, open-source AI, including Meta with LLaMA, Mistral, and DeepSeek, is working to tear down said walls, ensuring accessibility at low to null costs for everyone. Their goals revolve around decentralization, innovation, and accessibility, but they face the risks of losing control and facing increased security threats. While Silicon Valley locks its gates, China is accelerating at full speed, aggressively releasing AI models with less emphasis on access restrictions — not necessarily to promote openness, but to challenge global standards on its own terms. This isn’t an act of generosity, but a calculated move to flood the market. As The Financial Times put it in March 2025, China is essentially saying: If we can’t win by your rules, we’ll make ‘winning meaningless.’ We speculate that that’s the real twist in this arms race: some aren’t sprinting to cross the finish line; they’re trying to wash it away.
AI progress isn’t just about building better models, but also about outrunning the fear of falling behind. The rush is driven by several factors. First, investor pressure is higher than ever, since startups don’t just need AI that works; they need AI that sells. Second, one might mention the first-mover advantage: whoever locks or has locked in customers now most probably will own the market later. Third, Google’s stance in 2022 might serve as a cautionary tale: their hesitancy to invest cost them the AI lead, and that’s a mistake others are eager to avoid. The result is a never-ending sprint, where even stability is seen as a weakness. As TIME reported in early 2023, Microsoft moved aggressively after ChatGPT’s viral success, with CEO Satya Nadella declaring, “We’re going to move, and move fast.” The urgency was palpable: Google had issued a “code red,” and the AI race was no longer just about innovation, but survival. With billions at stake and reputations on the line, tech giants were no longer building at their own pace; they were sprinting to avoid irrelevance.
Now, as new models flood the market, one might naturally ask: how much of it all is actual progress? Look at the fight between GPT-4 and GPT-4 Turbo: the former set a benchmark for generative AI, and while the latter was painted as an upgrade, it didn’t turn out to be revolutionary, even though it was faster and cheaper. Scaling laws are facing diminishing returns: while larger models often yield improvements, they don’t always translate into significantly better reasoning or usability in real-world tasks. Yet, the hype machine persists; every update must be sold and marketed as groundbreaking, even when it’s not. It thus seems like we’re not merely advancing intelligence, but rather simply sustaining market momentum.
AI today is caught in a paradox: are we loving it all or losing it all? Nearly all the letters of those two words are identical, as only one differs. Loving becomes losing with a single stroke, and revolution tips into chaos. If you refer to this article’s picture, the ‘V’ and the ‘S’ are fighting for a spot, each eager to fill the gap more than the other. But who decides with which we’re filling that gap? We’re loving the breakthroughs, the endless potential, and the feeling of standing at the edge of history. At the same time, we’re losing our ability to keep up, our sense of what’s truly groundbreaking, and our grasp on what progress really means. We’re in fact also losing our critical thinking, according to a 2025 Microsoft study which shows how reliance on AI can hinder independent thought, turning users into passive entities feeding on prebuilt ideas. The more we give machines carte blanche to think for us, the narrower that gap between loving and losing becomes, until creativity is slowly crushed along the way.
So, where are we headed? In this race, stopping doesn’t seem like an option as two possibilities emerge. Either a few dominant AI models will win, crushing competitors (consolidation), or countless niche models will flood the market (explosion). Governments might intervene by regulating safety and controlling access and market structure, but the true shifts will come from the way the infrastructure is shaped. The ability to power AI models, through access to computing resources, storage, and data, will determine who controls the future of technology. In this future, those who own the infrastructure will have the lion’s share of the market, dictating which models stand in the spotlight and which ones fade into obscurity. This leads us to a brutal truth: you can build the smartest model out there, but if you don’t have the power to run it, you’re as good as a paperweight in a world running on computation.
On a final note, models will keep on multiplying, the hype will continue expanding, and the AI race won’t slow down anytime soon. The danger to look out for isn’t that machines are learning too fast, it’s that we aren’t thinking hard enough about where we’re headed: towards true intelligence or just running in circles? As the American psychologist B.F. Skinner put it best: “The real problem is not whether machines think but whether men do.”
References:
1. Yoon, J. (2025, March 19). Why China is suddenly flooding the market with powerful AI models. Financial Times. Retrieved from: https://www.ft.com/content/13df6250-dffb-40fc-bb79-309764fa3905
2. Lee, H.-P., Sarkar, A., Tankelevitch, L., Drosos, I., Rintel, S., Banks, R., & Wilson, N. (2025). The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers. Microsoft Research. https://www.microsoft.com/en-us/research/wp-content/uploads/2025/01/lee_2025_ai_critical_thinking_survey.pdf
3. Chow, A. R., & Perrigo, B. (2023, February 16). The AI Arms Race Is Changing Everything. TIME. https://time.com/6255952/ai-impact-chatgpt-microsoft-google