Aside from the hype: Google’s practical AI guide, every startup should read

In 2025, AI continues to reshape how startups are built, operated and compete. Google’s The Future of AI: The Startups’ Perspective Report provides a comprehensive roadmapleverages insights from infrastructure leaders, entrepreneurial founders and venture capital partners. The information is pragmatic: AI is becoming increasingly accessible, but thoughtful application and long-term orientation are more important than using it alone.

Infrastructure is growing, but startups can abstract complexity

Google Cloud’s Amin Vahdat highlights how advances in computing hardware (specific interconnects, 3D stacked memory and liquid cooling) make the next-generation AI workloads. These system-level changes are designed to support long textual, multi-models of Gemini 2.0 (Gemini 2.0) that provide startups with access to increasingly capable tools without the burden of building infrastructure from scratch.

This evolution indirectly benefits startups. Most people don’t need to manage hardware, but they should understand how to take advantage of what is available: a cloud-based API with retrieval generation (RAG), feature calls, and a live streaming interface.

Focus on usefulness, not just novelty

Many contributors emphasize that the actual value of artificial intelligence is not abstract, but tangible results. Arvind Jain (Glean) advises founders to engage with AI to unlock new product features rather than simply optimizing cost savings. The goal is not to chase agents or automation, but to build tools that allow users to do things they could not have done before.

Startups are also encouraged to intentionally design AI-driven experiences. Chamath palihapitiya points out that the future of software lies in less work – extending workflows rather than adding functionality. Crystal Huang (GV) stresses that if the product is easy to install, it is just as easy to uninstall. Stickiness will come from deep integration into user workflows.

Agent system: Practicality to idealism

AI agents are still a promising but developing region. Leaders such as Harrison Chain and Dylan Fox (Convention) pointed out that success in this field depends on solving basic usability issues – effort, contextual persistence and hallucination.

Consensus is not about building a completely autonomous system, but about establishing agents in specific areas with human supervision and clear evaluation pipelines. The model is only part of the equation. Defining success, tracking agent behavior, and improving through feedback are central parts of the development process.

Pay attention to business models and technology are important

Startups are advised to get rid of overall product thinking and modular design. Both Jennifer Li (A16Z) and Jerry Chen (Greylock) emphasize that the way AI products are packaged and sold (based on USAGE, based on value or per capita seat) is as strategic as basic buildings.

At the same time, proprietary data remains the core difference. Companies that can generate or access only data sources will be better positioned to create defensible models and user experiences. Langchain’s Harrison Chase encourages teams to prioritize internal assessment tools early on, rather than measuring performance, but guide development choices.

AI is a tool set – not business by default

Several sound warnings do not confuse model access with sustainable differentiation. David Friedberg notes that packaging large language models (LLMs) are not moats. Instead, the founder should focus on building what he calls a “software factory” – a system that ingests business logic and outputs working solutions, constantly improving through iterative and feedback loops.

Startups are also advised to incorporate their strategies into real-world issues. Is the application internal productivity, customer support or domain-specific automation, and the most powerful use cases tend to come from industries with complex, repetitive tasks and optimized workflows.

AI’s value chain is moving to the application layer

As models and infrastructure continue to be commoditized, the application layer becomes the source of value creation. Apoorv Agrawal (Hydrometer Capital) sees this as a key shift – from basic model development to AI-native applications. The advice is clear: don’t build models for your own sake; build tools that can solve the things that end users experience every day.

Also called for design intentions. Matthieu Rouif (Photoroom) suggests design experience to eliminate friction instead of adding another tip. AI should be integrated into the product, not taking over the user interface.

in conclusion

Instead of bold predictions, Google reports provide rooted guidance: startups that integrate AI into specific workflows, deliver their business models with value, and invest in evaluating results in the coming years.

AI may continue to grow faster than infrastructure, regulations or markets. But by anchoring the development of utilities and long-term value, startups can make AI a durable advantage, not just features.

download Guide here. Also, don’t forget to follow us twitter And join us Telegram Channel and LinkedIn GrOUP. Don’t forget to join us 90K+ ml reddit.

🔥 [Register Now] Minicon Agesic AI Virtual Conference: Free Registration + Certificate of Attendance + 4-hour Short Event (May 21, 9am-1pm) + Hands-On the Workshop