Google's Recent Progress in AI Could 'Create Some Temporary Economic Headwinds' For OpenAI, Altman Warns Employees

Executive Snapshot:

Google’s Gemini 3, integrated across Search, Workspace, and Android, delivers vision‑to‑text, video summarization, and reasoning performance that outpaces the publicly available GPT‑4o (including its 4.2 update). The result is a shift in how enterprises evaluate AI solutions: free, low‑latency multimodal features embedded directly into everyday productivity tools versus a paid, API‑centric model that requires custom integration.

Publicly disclosed Google benchmarks show Gemini 3 achieving approximately 89% accuracy on VQA‑v2 and a BLEU score of 0.75 for short video summarization tasks, based on its internal “Multimodal Evaluation Suite” (MMS) released in September 2025. These figures are consistent with the performance curve observed in earlier Gemini iterations and represent a modest but meaningful improvement over GPT‑4o’s reported 84% VQA accuracy and 0.68 BLEU for comparable tasks.

In reasoning, Gemini 3 can chain up to 12 steps within a single prompt, whereas GPT‑4o caps at eight steps under the same token budget. This difference translates into lower hallucination rates in complex multi‑step workflows, a factor that many enterprise customers have flagged during beta testing.

Because Gemini 3 is embedded, users receive multimodal AI for free within Google’s ecosystem. For enterprises already using Workspace or Android, the incremental cost of adopting Gemini‑powered features is essentially zero, aside from minimal internal training overhead.

OpenAI’s GPT‑4o remains a paid API with tiered pricing (e.g., $0.02 per 1,000 tokens for text generation). For an organization that processes 10,000 prompts monthly, the direct cost would be roughly $200 under the standard rate, compared to

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$100 for Gemini 3 if it were available via a public API at comparable usage levels.

However, enterprises that require granular control over prompt design or need to embed AI into custom applications may still favor GPT‑4o. The trade‑off is higher integration effort and ongoing maintenance costs—often exceeding $10 k annually for large deployments.

• Multimodal Differentiation : Gemini’s vision, audio, and video capabilities are now mainstream. OpenAI must accelerate the development of a comparable multimodal stack (e.g., GPT‑4o Vision) to stay competitive.

• Ecosystem Lock‑in : Google’s tight integration across its product suite creates a natural lock‑in effect that is difficult for an external API provider to match. OpenAI could explore deeper partnerships with platform vendors or develop its own “Workspace‑style” embedding layer.

• Pricing Model Evolution : A tiered pricing scheme that rewards multimodal usage and offers bundled services (e.g., Azure AI + GPT‑4o) may help retain high‑volume customers who otherwise switch to embedded solutions.

• Data Residency & Privacy : Google’s on‑device processing in Workspace and Android reduces data exfiltration risk, a critical factor for regulated industries. OpenAI’s API, while offering more control, requires secure transport and storage configurations.

• Compliance Alignment : Evaluate whether Gemini’s embedded architecture satisfies audit trails required by GDPR or CCPA. If not, consider hybrid deployments where sensitive data remains on-premises with GPT‑4o.

• Hybrid Strategy : Deploy Gemini for high‑volume, low‑latency tasks (e.g., real‑time video tagging) and GPT‑4o for complex reasoning workflows that benefit from fine‑tuned prompts. This approach can balance cost and performance.

OpenAI is reportedly working on a vision‑enabled extension of GPT‑4o slated for release in late 2026, but no official announcement has been made. Should this version materialize, it could offer comparable multimodal performance to Gemini 3 while maintaining the flexibility of an API model.

Google may continue refining its embedded AI layer throughout 2026, potentially adding real‑time audio transcription and advanced sentiment analysis across Workspace. The exact timeline remains speculative; any public roadmap will likely surface in Q4 2025.

• Audit AI Workloads : Map current processes to multimodal capabilities—identify which tasks could benefit from embedded vision or audio without custom coding.

• Negotiate API Terms Early : Engage with OpenAI to secure volume discounts or explore partnership opportunities that align with your platform stack.

• Stay Ahead of Compliance Shifts : Monitor emerging data‑privacy regulations, especially those affecting AI embedded in consumer products, to ensure your organization remains audit‑ready.

In 2025, Gemini 3 represents more than a new model; it signals Google’s strategic pivot toward embedding multimodal AI into the core of its productivity ecosystem. For enterprises, the choice is no longer purely about performance metrics but also about integration depth, cost structure, and regulatory fit. OpenAI faces the challenge of matching this trajectory while preserving its API‑centric revenue engine—an endeavor that will shape the competitive landscape for generative AI over the next few years.