The public image of the AI race is now a physical one: giant data centers popping up in remote farmland, utilities fielding power requests 100x larger than their existing systems, and hyperscalers treating GPUs, cooling systems, and land parcels as if cognition were just another problem solved by throwing huge amounts of money at it.

If this is all what AI is, Israel is in a big pickle. Israel is a tiny country and will not compete effectively in these matters.

No matter what Israel can do, it is what it is. In early 2025, Microsoft said capital expenditures including finance leases were $22.6 billion in a single quarter, and Alphabet told investors it expected $175 billion to $185 billion of capital spending in 2026. At the same time, utilities were reporting AI-related power requests that could exceed their current generation capacity, and a Reuters Breakingviews analysis estimated Big Tech’s 2026 AI splurge at roughly $630 billion. Numbers are being thrown around that exceed Israel’s GDP.

Let me first say this: this money heavy approach makes sense. It is grounded in real achievements. But there is a difference between respecting the phase that got us here and assuming it is the final map. The question is not whether scale matters. It does. The question is whether scale is the whole story.

Brute force worked because the empirical results were hard to argue with. OpenAI’s 2020 scaling-laws paper found clean power-law improvements as model size, dataset size, and training compute increased. DeepMind’s Chinchilla work then showed that careful co-scaling of parameters and tokens could outperform much larger models at the same compute budget. They found measurable regularities that gave the industry permission to build bigger.

That logic carried into the modern foundation-model era. The GPT-4 technical report described a large multimodal model whose performance could be predicted across scales, and scaling delivered the products that reset public expectations for AI software. These systems were proof that immense clusters and vast corpora could produce models that felt qualitatively new to the world.

The compute and data believers therefore deserve respect. Even before launching SSI, Ilya Sutskever was known as one of the early champions of scaling, and Reuters noted that his ideas helped shape the investment wave in chips, data centers, and energy that now defines frontier AI. Any serious critique of the current race should start with that honesty: the people who bet on scale were right for a long time.

But, scaling may not be enough

Success at one stage does not guarantee sufficiency at the next. Public human text is finite. One influential........

© The Times of Israel (Blogs)