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Google's most ambitious AI model yet has passed its first major milestone, with posttraining still ahead before any public release.
Google announced the start of Gemini 4’s pretraining back on July 21, 2026, billing it as the largest pretraining effort in the company’s history. Now, as of early September, the model has shown strong preliminary results from that phase. The catch: posttraining hasn’t happened yet, and that’s where AI models go from impressive spreadsheet entries to products people can actually use.
What Gemini 4 is actually being built to do #
CEO Sundar Pichai spelled out the ambition during Alphabet’s Q2 earnings call on July 23, framing Gemini 4 around two specific priorities: coding and autonomous agents. Pichai also noted that Gemini 4 would rely on what he called “much larger base models” to meet the demands ahead.
The price tag reflects that. Alphabet has set its 2026 capital expenditure guidance as high as $205 billion, a figure that has pushed the company into negative free cash flow territory for the first time.
Where the competitive pressure is coming from #
Gemini 3.5 Pro, the model that preceded this effort, reportedly hit delays tied to coding issues. Gemini 4 is, in part, a response to that. A model explicitly built around coding strength addresses one of the specific areas where competitors have landed the most credible blows against Google’s prior releases.
Analysts tracking Google’s historical training cycles put a late-2026 release window as the most plausible scenario, roughly six months from when pretraining began in July. No formal timeline has been publicly disclosed by the company.
What comes next, and why posttraining is the variable to watch #
As of September 1, 2026, no public benchmarks for Gemini 4’s pretraining or posttraining timelines have been released. For a company that spent much of 2025 and early 2026 trying to rebuild developer confidence in the Gemini product line, the silence reads less like strategic secrecy and more like disciplined expectation management.
The broader tech sector has been watching Alphabet’s capital spending trajectory closely, partly because it signals how much the incumbents believe the current AI investment cycle still has runway. A $205 billion capex year from one company has ripple effects across chip suppliers, data center construction, energy infrastructure, and the startups that compete for the same GPU capacity.
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