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Google’s Gemini 3.5 Pro AI model remains unreleased, leading to a $425 billion loss in market capitalization. The delay highlights risks of falling behind in AI race despite strong financials.
Google’s highly anticipated Gemini 3.5 Pro AI model has not yet been released, despite previous promises for a July launch, resulting in a loss of approximately $425 billion in market value for Alphabet.
This delay underscores investor concerns over Google’s AI development progress and its ability to maintain leadership in the sector, especially as competitors release new models.
On May 19, 2026, Google announced at I/O that Gemini 3.5 Pro would be available in June. However, as of July 17, the model remains unreleased, with sources indicating it is months behind schedule due to challenges in improving coding capabilities and reliability issues, including high hallucination rates. Google has not officially confirmed these delays or the reasons behind them.
Following reports from Bloomberg on July 16, citing current and former Google employees, Alphabet’s stock dropped 4.4% the next day, equating to roughly $200 billion in lost market capitalization. This decline, combined with a prior $225 billion selloff in late June after senior DeepMind researchers left for competitors, totals approximately $425 billion lost in less than a month. Despite these market reactions, Google’s Q1 2026 financials remain strong, with $109.9 billion in revenue and a 63% increase in Google Cloud revenue to $20 billion, indicating that the financial fundamentals have not changed.
Third-party reports suggest that Google may be discarding a near-ready model and restarting training on a native Gemini foundation, citing reliability issues such as hallucinations. However, Google has not confirmed these claims, and key specifications like the 2-million-token context window or specific release dates remain unverified. Multiple deadlines for Gemini 3.5 Pro have passed without delivery, including the initial June promise, a restated July window, and a recent target of July 17.
Impact of Delays on Google’s AI Leadership and Market Confidence
The delay of Gemini 3.5 Pro highlights how missing critical AI development milestones can lead to massive market valuation losses, even when a company’s financials remain strong. It underscores the importance of timely innovation in maintaining competitive advantage and investor confidence in the rapidly evolving AI landscape. The market’s reaction demonstrates that absence of a flagship product can be as damaging as poor performance, especially when competitors are releasing models and open-weight alternatives at a faster pace.
This situation also raises questions about Google’s internal development processes and the risks of falling behind in the AI race, which could have long-term implications for market positioning and technological leadership.
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Google’s AI Development Timeline and Market Expectations
In 2026, Google aimed to launch Gemini 3.5 Pro as its flagship AI model, following the release of Gemini 3.5 Flash earlier this year. The company had publicly committed to a June release, which was then pushed to July, with multiple reports indicating significant delays. The delay is reportedly due to difficulties in enhancing coding capabilities, an area where competitors like OpenAI and Anthropic have gained an edge. These setbacks come despite Google’s strong Q1 financials, including $109.9 billion in revenue and a 63% growth in Google Cloud, suggesting that the core business remains healthy but that its AI ambitions are lagging behind expectations.
Prior to the delays, Google had been seen as a leader in AI development, but recent reports of internal challenges, including the possible discarding of a near-ready model, have cast doubt on its ability to meet its own deadlines. Meanwhile, competitors like GPT-5.6 Sol and Grok 4.5 have launched publicly, intensifying the pressure on Google to catch up.
“The model is months behind schedule, primarily over efforts to improve its coding capabilities, and a late-June training-data update produced disappointing results.”
— Bloomberg, Julia Love and Davey Alba
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Unconfirmed Details and Ongoing Development Challenges
It is not yet clear whether Google is discarding a near-ready model, re-training from scratch, or facing other technical difficulties. Key specifications, such as the 2-million-token context window and exact release dates, remain unverified, and the company’s internal progress is not publicly confirmed. The true reasons behind the delays and the potential impact on Google’s AI roadmap are still uncertain, with multiple competing reports and no official statements.
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Next Steps in Google’s AI Strategy and Market Reactions
Google is expected to provide an official update on Gemini 3.5 Pro’s status in upcoming weeks. Meanwhile, competitors continue to release and improve models, increasing pressure on Google to deliver a flagship AI product soon. Investors and industry watchers will be monitoring whether Google can recover market confidence through future launches or strategic adjustments. The company may also face increased scrutiny over its internal development processes and timelines, which could influence future innovation and investor trust.
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Key Questions
Why has Google delayed the Gemini 3.5 Pro launch?
According to reports, the delay is due to challenges in improving coding capabilities and reliability issues such as hallucination rates. Google has not officially confirmed these reasons.
How much market value has Google lost due to the delay?
Google has lost approximately $425 billion in market capitalization in less than a month, following delays and negative market reactions.
Will the delays affect Google’s leadership in AI?
The delays could weaken Google’s position if competitors release effective models sooner, but the company’s strong financials suggest it can still compete if it accelerates development.
What are the risks of launching an unreliable AI model?
Launching an unreliable model can lead to reputational damage and financial losses, especially if issues like hallucinations persist. Delaying to improve reliability can be a strategic choice to avoid these risks.
Source: ThorstenMeyerAI.com
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