This week, Google released Gemini 3, its most advanced model yet, and the AI and business world is abuzz. Here’s my take. We will have multiple AI models, ranging from small, bespoke models we fine-tune for our businesses to large, general-purpose models tied to our productivity suites. That means the majority of the world will be using either Google Workspace or Microsoft Office.
The enterprise AI market has clarified into a binary choice: Microsoft with OpenAI or Google with Gemini.
Both platforms offer frontier models integrated with productivity suites used by billions of people. Google's November 18th launch of Gemini 3 Pro at $2 per million tokens—60% below OpenAI's pricing—reflects this reality.
For enterprise technology leaders, this pattern should feel familiar—it mirrors the cloud infrastructure wars that saw AWS, Azure, and Google Cloud capture 66% of a market that exceeded $500 billion in 2023. The same dynamics that drove cloud consolidation are now reshaping AI: integration advantages trump technical superiority, platform economics beat point solutions, and distribution determines winners.
One of my friends, a tech-savvy executive, when asked about the release, gave me this amusing quote. Obviously, he was bullish on Google’s AI play:
“Everyone else is out there selling you a chatbot that can write a haiku about a dishwasher. Cute stuff, Sam. Google built a system that plans, builds, and learns. Put it this way, it’s the most intelligent employee you’ve ever hired that doesn’t ask for stock options, has a side hustle, or expects to be fed. As Google promised, build anything.”
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Google Gemini 3 and the Two-Platform AI Market
Enterprise AI now mirrors the cloud wars: integration beats innovation, and scale drives consolidation.
The Cloud Wars Precedent
History rarely repeats, but it often rhymes. The current AI platform battle follows a script written during the cloud infrastructure wars of the 2010s.
Amazon Web Services launched in 2006, Microsoft Azure entered in 2010, and Google released Compute Engine in 2013. By 2014, these organizations began replicating each other's products and services, reducing prices to undercut one another.
Today's AI market shows identical patterns: OpenAI's ChatGPT launched in 2022, Microsoft integrated it immediately, and Google responded with Gemini iterations, each matching features while competing on price.
The parallels extend beyond timing. AWS's early lead enabled it to build a diverse portfolio, securing 32% market share, while Azure leveraged deep integration with Microsoft's enterprise software stack to capture 23%. Similarly, OpenAI's first-mover advantage established technical leadership, but Microsoft's integration with Office 365's 345 million commercial seats provides the distribution OpenAI alone could never achieve.
Most tellingly, AWS CEO Andy Jassy acknowledged in 2022 that "there won't be just one successful player... there are going to be multiple successful players, and who those are I think is still to be written". His prediction proved prescient for cloud infrastructure. The same consolidation pattern now emerges in AI.
The Integration Thesis Proven
The AI race feels less like a marathon and more like a demolition derby; just as OpenAI and Microsoft claim the lead, Google barrels in from the side with Gemini 3—a multimodal, agent-powered model purpose-built for enterprise use.
While the headlines focus on tech spectacle, the real story is under the hood: Gemini 3's capabilities closely align with enterprise needs such as document synthesis, codebase management, and multimodal analysis. They have launched an AI-enabled code editor, Antigravity, that will challenge the undisputed leader, Microsoft’s Visual Studio Code editor.
This strategy directly parallels Azure's cloud approach. Azure's seamless integrations with Office 365, Dynamics 365, and the Power Platform positioned it as a go-to solution for businesses looking to modernize their IT infrastructure. Google now applies the same playbook with Gemini: leverage existing enterprise relationships, eliminate integration complexity, and position AI as a feature of existing platforms rather than a separate procurement.
The results speak volumes. Google's cloud unit showed 35% revenue growth with a $155 billion backlog, with CEO Sundar Pichai noting they signed more billion-dollar deals through Q3 2025 than in the previous two years combined. These aren't experimental deployments—they're production commitments from enterprises recognizing that integration efficiency outweighs marginal performance advantages.
Platform Economics: The Decisive Factor
The cloud wars taught a crucial lesson: the market is dominated by three major cloud providers, who share 67% of the pie—AWS with 31%, Azure with 25%, and Google Cloud with 11%. The remaining 100+ providers fight for scraps. AI shows identical concentration dynamics emerging.
Consider the financial trajectories:
Cloud Infrastructure Evolution:
Azure doubled its market share from 12% to 25% in only 7 years by leveraging their enterprise network
Google Cloud doubled its market share over 6 years and became profitable only recently
Current AI Market Dynamics:
The disparity seems unassailable until you recognize that 84% of Google Cloud customers use the platform exclusively, while Azure customers show 36% multi-cloud adoption. This lock-in dynamic—where platform adoption creates switching costs—will likely accelerate as AI workloads deepen.
The Productivity Suite as a Distribution Moat
Microsoft's cloud success offers the clearest parallel to current AI dynamics. In 2020, 63% of organizations used Microsoft Azure to run their apps, while Microsoft 365 held an 87.5% market share in productivity suites. This wasn't a coincidence—it was a strategic coupling.
Google learned this lesson. Their Gemini 3 launch reflects the same strategy: embed AI within existing workflows rather than requiring new ones. Google's search business generated $56.56 billion in revenue—up 15% year-over-year—not despite AI, but because of it. Management notes that AI Mode queries are typically twice as long as traditional searches, indicating users are finding new value rather than replacing existing behaviors.
The Antigravity development platform extends this logic. By building on VS Code's familiar interface, Google eliminates learning curves while creating switching costs. When I asked the VP of Product and Developer Marketing, Google Cloud for Google Cloud, Peder Ulander, about Antigravity, his reply was:
”We didn't just update the IDE; we changed the altitude. Google Antigravity lets developers and builders stop fighting with syntax and start managing outcomes. It’s agentic coding that handles the terminal, the filesystem, and the grunt work. If you want to code at the speed of thought, this is the engine.”
Google Cloud's developer-friendly platform promotes open source technologies, making it the platform of choice for many innovators and startups. Once developers establish workflows, migration becomes exponentially complex—a lesson learned from the stickiness of AWS's early developer adoption.
Wall Street's Pattern Recognition
Markets have seen this movie before. Alphabet's stock surged 60% over the past year and 47% year-to-date, reflecting a fundamental shift in how investors perceive its competitive position. Just two years ago, ChatGPT's launch sparked genuine concern about search cannibalization. That narrative has completely reversed.
The parallel to cloud market dynamics is striking. In Q2 2024, AWS reported $26.3 billion in revenue while maintaining steady growth, while Microsoft Azure reported $13.3 billion, indicating steady growth in its customer base. Despite AWS's revenue lead, many analysts believe Azure's potential has not been fully priced into Microsoft's stock, with Wedbush Securities noting this as Microsoft's "shining moment" with AI set to change the cloud growth trajectory.
Warren Buffett's Berkshire Hathaway taking a $4.3 billion position in Alphabet signals recognition of similar dynamics. Google Cloud's 34% revenue growth and $155 billion backlog show enterprise customers aren't just testing AI—they're committing serious multiyear spending on Google's infrastructure.
The Multi-Cloud Illusion in AI
The cloud wars demonstrated that multi-cloud strategies, while theoretically appealing, rarely materialized at scale. As a former exec in charge of Developer Relations at Cloud.com, we used to make the joke, “Hybrid cloud is like sex in high school, everyone’s talking about it, no one is doing it.”
Among Google Cloud customers, 84% use the platform exclusively, while only 16% have a multi-cloud strategy. The same pattern emerges in AI.
Enterprises may claim vendor-diversity goals, but operational realities favor consolidation. The "Big Three" cloud providers control 68% of the global cloud infrastructure market, which reached $90 billion in Q4 2024. Secondary players fight for single-digit shares in niche segments.
AI shows identical concentration. While Anthropic's Claude and Meta's Llama offer compelling capabilities, they lack the enterprise distribution and integration advantages of Microsoft and Google. Just as scale really matters in cloud regarding cost structure and breadth of services, AI platforms require massive infrastructure investments that only the most prominent players can sustain.
Regional Dynamics and Global Implications
The cloud wars also taught that global markets fragment along regional lines. In China, Alibaba has 39% market share, Huawei Cloud 19%, and Tencent 15%, with Chinese cloud providers now targeting expansion in Europe, Asia, and emerging countries.
AI shows similar regional dynamics emerging. While OpenAI and Google dominate Western markets, China's AI ecosystem develops independently. This fragmentation suggests that, rather than a single global winner, we'll see regional platform champions—an echo of the cloud market's evolution.
Emerging markets like India (25%+ growth), Japan, and Brazil outpaced mature regions in cloud adoption. AI adoption will likely follow similar patterns, with platform providers who establish early presence in growth markets securing long-term advantages.
Investment Implications and Future Trajectory
The evolution of the cloud market offers a roadmap for AI's future. The cloud computing market exceeded $500 billion in 2023 and is expected to grow to $2.2 trillion by 2032. If AI follows similar trajectories—and early indicators suggest it will—platform consolidation will accelerate.
The cloud infrastructure market is projected to double by 2028, reaching $600 billion annually, driven by AI, edge computing, and IoT. Critically, hyperscalers will capture the majority of this growth. The same dynamics—economies of scale, integration advantages, distribution moats—that drove cloud consolidation will determine AI winners.
Microsoft's Intelligent Cloud group saw 26% year-over-year revenue growth, with Azure growing 39%, propelling Microsoft's revenue to soar 18% year over year. Google shows similar momentum, with cloud revenue growing 34% as AI workloads accelerate adoption. These aren't separate trends—they're the same phenomenon viewed from different angles.
Strategic Implications for Enterprises
The cloud wars' resolution provides clear lessons for AI adoption:
Lesson 1: Early Platform Choices Become Permanent
Azure's great strength is leveraging its enterprise network and selling to big customers—Azure has fewer customers, but they are enterprise customers who pay more than AWS and GCP. The same dynamic will play out in AI. Organizations choosing platforms today are making decade-long commitments.
Lesson 2: Integration Trumps Innovation
AWS maintained technical superiority for years, yet Azure doubled its market share by leveraging existing enterprise relationships. Gemini 3's integration with Google Workspace follows this proven strategy.
Lesson 3: Switching Costs Compound
The majority of cloud customers use their platform exclusively. As AI workloads deepen, data gravity and workflow dependencies will make platform migration increasingly difficult.
Conclusion
Gemini 3's arrival doesn't represent a technological revolution but a historical repetition. The same forces that consolidated cloud infrastructure around three providers are now reshaping the artificial intelligence market. Google and Microsoft aren't competing on model performance—they're leveraging the platforms, relationships, and integration advantages built during the cloud wars.
D.A. Davidson analysts called Gemini 3 "the current state-of-the-art," noting it's a strong competitor to models from OpenAI and Anthropic. Yet technical superiority proved insufficient in cloud markets, and it will prove insufficient in AI. What matters is the tedious, difficult work of integration, distribution, and platform economics.
For enterprise leaders: The AI platform decision isn't about choosing the best model today. It's about selecting the ecosystem that will define your technology strategy for the next decade. The cloud wars showed that early leaders (AWS) could be challenged by platform players (Azure) who leveraged existing relationships. The AI market follows the same script, with Google and Microsoft's productivity suites serving the role that enterprise software played in cloud adoption.
The binary choice—Microsoft with OpenAI or Google with Gemini—may seem limiting. Yet together, AWS, Azure, and Google Cloud account for 66% of total cloud spend, suggesting markets naturally consolidate around a few dominant platforms. Fighting this pattern means fighting market physics.
History doesn't repeat, but it rhymes. The cloud wars' resolution—platform integration beating technical innovation, distribution trumping performance, and consolidation around 2–3 players—provides the template for AI's evolution. Organizations that recognize these patterns and choose decisively will secure advantages. Those waiting for different outcomes may discover that history, once again, has already written the ending.
Note: Since this edition is already getting long, you’ll find the full AI Toolbox and the Prompt of the Week in the complete newsletter on our website.
Gemini – Google’s flagship generative-AI assistant and model family for writing, planning, and multimodal tasks.
NotebookLM – AI-powered research and notetaking assistant for teams and individuals.
Gemini Enterprise – Enterprise-grade version of Gemini for secure deployment across workflows and agent-oriented AI in business contexts.
Vertex AI – Unified Google Cloud platform for building, training, and deploying generative-AI applications at enterprise scale.
Vertex AI Agent Builder – A specialization of Vertex AI designed for constructing, scaling, and governing production-grade AI agents.
Vertex AI Studio – A development environment within Vertex AI for rapid prototyping, prompt tuning, and model customization.
Google Antigravity - This is what I overheard at the coworking space - Google Antigravity. That’s some agentic shit that lets you vibe-code your way to a Series A. If you aren’t using this, you’re coding with caveman tools.
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Prompt of the Week: Create a Professional Report with Google Gemini
Jeff Bezos banned PowerPoint at Amazon in 2004, replacing slide decks with complete narrative documents that must be read silently at the start of each meeting. The format emerged from frustration with superficial presentations in which bullet points masked shallow thinking and allowed presenters to hide behind charisma rather than logic.
By forcing ideas into complete sentences with supporting data, the 6-pager creates what Bezos calls "the great equalizer"—the idea wins, not the presenter. The document's true purpose isn't communication but clarification: writing in full narrative forces the author to confront gaps in logic, expose hidden assumptions, and connect ideas causally rather than sequentially.
However, it’s a great way to structure a report whether it’s internal or external. I am using this format for some white papers right now. It is surprisingly good.
Also, I am a fan of using this prompt with Gemini. I think the model not only writes well but also does an excellent job formatting and exporting it to Google Docs. Try adding this to your output:
Make the output look polished and well formatted like a McKinsey consutling report. The output is surprisingly well formatted but it is never perfect. This is where you add your touch but even then I think you can do this leveraging AI. I use the review prompt from the last week’s edition of the AIE to refine and challenge what I have.
You are an expert business strategist creating a comprehensive 6-page Amazon-style narrative brief. This document should be written as complete prose (no bullet points), with clear narrative flow and data-driven insights.
TOPIC: [Insert your specific topic/initiative here]
Create a 6-page narrative document following this structure:
**PAGE 1: EXECUTIVE SUMMARY & CONTEXT**
Write 3-4 paragraphs that:
- State the opportunity or problem clearly
- Provide historical context and current state
- Highlight why action is needed now
- Preview the recommendation
**PAGE 2: DETAILED PROBLEM STATEMENT & MARKET ANALYSIS**
Write 4-5 paragraphs covering:
- Deep dive into the problem/opportunity
- Market size and growth projections
- Competitive landscape analysis
- Key stakeholder impacts
- Critical success factors
**PAGE 3: PROPOSED SOLUTION & STRATEGY**
Write 4-5 paragraphs detailing:
- Comprehensive solution overview
- Strategic approach and methodology
- Unique value proposition
- Implementation phases
- Key differentiators
**PAGE 4: BUSINESS MODEL & FINANCIAL PROJECTIONS**
Write 4-5 paragraphs including:
- Revenue model and pricing strategy
- Cost structure analysis
- 3-year financial projections with assumptions
- ROI calculations
- Break-even analysis
- Risk-adjusted scenarios
**PAGE 5: IMPLEMENTATION PLAN & TIMELINE**
Write 4-5 paragraphs describing:
- Detailed implementation roadmap
- Resource requirements (people, technology, capital)
- Key milestones and dependencies
- Success metrics and KPIs
- Governance structure
**PAGE 6: RISKS, MITIGATIONS & APPENDIX**
Write 3-4 paragraphs covering:
- Major risks and mitigation strategies
- Alternative approaches considered
- Open questions requiring resolution
- Next steps and decision points
WRITING GUIDELINES:
- Use clear, concise business writing
- Include specific data points and metrics
- Write in complete sentences and paragraphs
- Avoid jargon unless industry-standard
- Make assertions backed by evidence
- Focus on clarity over persuasion
- Assume intelligent, skeptical readers
TONE: Professional, data-driven, objective, forward-looking
FORMAT: Pure narrative text, approximately 300-350 words per page
I appreciate your support.
Your AI Sherpa,
Mark R. Hinkle
Publisher, The AIE Network
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