Beyond the Hype: Google’s Practical AI Guide Every Startup Founder Should Read

In 2025, Amnesty International continues to reshape how startups, operate and compete. Google The future of artificial intelligence: views of startups The report offers a comprehensive road mapDepending on the visions of infrastructure leaders, founders of startups and investment capital partners. The message is pragmatic: Artificial intelligence has become easier, but a deliberate application and a long -term orientation is more important than speed alone.

Infrastructure is evolving – but startups can complicate abstract

Google Cloud’s Secretary highlights how to progress in the devices-assigned interconnection, 3D memory, liquid cooling-enabling the next generation of artificial intelligence work burdens. These changes are designed at the level of systems to support long -media models such as Gemini 2.0, which provide startups to access to tools that are increasingly capable without the burden of building infrastructure from the zero point.

This development is indirectly benefiting from startups. Most of them will not need hardware management, but they must understand how to take advantage of what is available: applications -based applications for the group of the orbits with the generation of retrieval (RAG), notebooks in the actual time, and the actual flow facades.

Focus on interest, not just the grandmother

Many shareholders emphasize that the true value of AI does not lie in abstraction, but in concrete results. Arvind Jain (Glean) recommends the founders to deal with artificial intelligence as a way to unlock the new product capabilities, rather than just improve cost savings. The goal is to chase the noise around agents or automation – it is to create tools that enable users to do something they have not been able to do before.

Startups are also encouraged to be deliberate in how to design artificial intelligence experiments. Chamath Palihapitiya notes that the future of programs lies in making more effort with less workflow flows, not to double the features. Crystal Huang (GV) emphasizes that if the product is easy to install, it is easy to uninstall it. The sticks will come from deep integration in the functioning of the user’s work.

Agents systems: practical application on idealism

Artificial intelligence agents remain a promising but developing area. Leaders like Harrison Chase (Langchain) and Dylan Fox (Assemblyai) note that success in this field depends on addressing constituent use issues – agency, continuing context, and hallucinations.

Instead of the purpose of full independent systems, consensus is to create factors for the field with human control and a clear evaluation pipeline. Models are only part of the equation. Determine success, agent tracking behavior, and refining with comments, are the basic parts of the development process.

Business model considerations are of interest to technology

Startups are advised to stay away from the thinking of the homogeneous product and towards standard design. Jennifer Lee (A16z) and Jerry Chen (Greylock) emphasizes that the way the AI ​​product is filled and sold-the list on the use of value, or every seat-can be a strategy like basic architecture.

Parallel, royal data remains a basic discrimination. Companies that can create or access unique data sources will be placed in a better position to create models that can be defended and user’s experiences. Harrison Chase of Langchain encourages the difference to giving priority to early internal evaluation tools – not only to measure performance, but to direct development options.

Artificial intelligence is a set of tool – not a virtual action

Several sounds warn against reaching the confusing model and sustainable distinction. David Friedberg notes that wrapping a large language model (LLM) is not a trench. Instead, the founders must focus on building what it calls “software factories” – systems that accommodate the logic of work and output solutions, and have been constantly improved through repetition and comments episodes.

Strain companies are also advised to consolidate their strategy in the problems of the real world. Whether the application is internal productivity, customer support, or automation of the field, the most powerful cases of use tend to be busy with industries with complex and repeated tasks and the functioning of the unprecedented work.

AI value chain turns into the application layer

With the continued models and infrastructure in the escalation, the application layer becomes the subject of value. APOORV AGRAWAL (Altimeter Capital) believes that this is a pivotal transformation from the development of the foundation model and the original applications of artificial intelligence. The recommendation is clear: Do not build a model for its interest; Build the tools that solve something that final users experience every day.

There is also an invitation to the intention in the design. Mattheu Rouif suggests the design of experiments that remove friction instead of adding another claim. Artificial intelligence should be mixed in the product, not to seize the user interface.

conclusion

The Google report avoids bold predictions and instead provides haunting guidelines: startups that carefully integrate artificial intelligence in a specific workflow, their business models are compatible with the value that is delivered, and investing in evaluation of results, will be placed in a good position for the coming years.

Artificial intelligence may continue to develop faster than infrastructure, regulations or markets. But by consolidating the development in interest and long-term value, startups can make Amnesty International a solid-not just feature.

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