In 2025, AI continues to reshape how startups build, operate, and compete. Google’s Future of AI: Perspectives for Startups report presents a comprehensive roadmap, drawing on insights from infrastructure leaders, startup founders, and venture capital partners. The message is pragmatic: AI is becoming more accessible, but thoughtful application and long-term orientation matter more than speed alone.
Infrastructure Is Evolving—But Startups Can Abstract Complexity
Amin Vahdat of Google Cloud highlights how advances in compute hardware—specialized interconnects, 3D-stacked memory, and liquid cooling—are enabling the next generation of AI workloads. These changes at the systems level are designed to support long-context, multimodal models like Gemini 2.0, which offer startups access to increasingly capable tools without the burden of building infrastructure from scratch.
This evolution benefits startups indirectly. Most won’t need to manage hardware, but they should understand how to leveRAGe what’s available: cloud-based APIs with retrieval-augmented generation (RAG), function calling, and real-time streaming interfaces.
Focus on Usefulness, Not Just Novelty
Many contributors emphasize that AI’s real value lies not in abstraction but in tangible outcomes. Arvind Jain (Glean) advises founders to approach AI as a means to unlock new product capabilities, rather than simply optimizing for cost savings. The goal isn’t to chase hype around agents or automation—it’s to build tools that enable users to do something they couldn’t before.
Startups are also encouraged to be deliberate in how they design AI-powered experiences. Chamath Palihapitiya points out that the future of software lies in doing more with less—streamlining workflows, not multiplying features. Crystal Huang (GV) underscores that if a product is easy to install, it’s just as easy to uninstall. Stickiness will come from deep integration into user workflows.
Agentic Systems: Practicality Over Idealism
AI agents remain a promising but developing area. Leaders like Harrison Chase (LangChain) and Dylan Fox (AssemblyAI) note that success in this space depends on addressing foundational usability issues—latency, context persistence, and hallucination.
Instead of aiming for fully autonomous systems, the consensus is to build domain-specific agents with human oversight and a clear evaluation pipeline. Models are only part of the equation. Defining success, tracking agent behavior, and refining with feedback are core parts of the development process.
Business Model Considerations Matter as Much as Technology
Startups are advised to move away from monolithic product thinking and toward modular design. Jennifer Li (a16z) and Jerry Chen (Greylock) both emphasize that the way an AI product is packaged and sold—usage-based, value-based, or per-seat—can be as strategic as the underlying architecture.
In parallel, proprietary data remains a core differentiator. Companies that can generate or access unique data sources will be better positioned to create defensible models and user experiences. LangChain’s Harrison Chase encourages teams to prioritize internal evaluation tooling early on—not only to measure performance, but to guide development choices.
AI is a Toolset—Not a Business by Default
Several voices caution against confusing model access with sustainable differentiation. David Friedberg points out that wrapping a large language model (LLM) is not a moat. Instead, founders should focus on building what he calls “software factories”—systems that ingest business logic and output working solutions, continuously improved through iteration and feedback loops.
Startups are also advised to anchor their strategy in real-world problems. Whether the application is internal productivity, customer support, or domain-specific automation, the strongest use cases tend to arise from industries with complex, repetitive tasks and under-optimized workflows.
AI’s Value Chain Is Shifting to the Application Layer
As models and infrastructure continue to commoditize, the application layer becomes the locus of value creation. Apoorv Agrawal (Altimeter Capital) sees this as a pivotal shift—away from foundational model development and toward AI-native applications. The recommendation is clear: don’t build a model for its own sake; build tools that solve something end users experience every day.
There’s also a call for intentionality in design. Matthieu Rouif (Photoroom) suggests designing experiences that remove friction rather than adding another prompt. AI should blend into the product, not take over the user interface.
Conclusion
The Google report avoids bold predictions and instead offers grounded guidance: startups that thoughtfully integrate AI into specific workflows, align their business models with value delivered, and invest in evaluating outcomes will be well-positioned for the coming years.
AI will likely continue to evolve faster than infrastructure, regulation, or markets. But by anchoring development in utility and long-term value, startups can make AI a durable advantage—not just a feature.
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