Machine & Matter: Issue 02

🤖 In today's Machine & Matter:

The AI landscape is rapidly evolving. Google's Gemini 2.5 Pro just got a coding and access boost ¹ . Anthropic is giving Claude web search powers via its API ² . Apple is eyeing AI search engines like Perplexity for Safari, potentially shaking up its long-standing Google deal ³ . And new research like Alibaba's ZERO SEARCH explores training LLMs for search without costly live engine interactions ⁴ .

I see these developments pointing to a significant trend: AI is embedding itself as foundational infrastructure. It's moving beyond standalone apps to become the underlying layer for many digital experiences. What we're seeing isn't just a series of unrelated updates, but a concerted push towards AI as a utility, as fundamental as the cloud itself.

This pattern of AI becoming deeply integrated into our existing tools and platforms reveals a crucial direction for technology. The most impactful AI isn't always the flashiest chatbot, but the one that silently enhances the systems we rely on daily.

This invites a shift in perspective: instead of solely asking "What new things can AI do?" we should also ask, "How is AI becoming a fundamental part of the tools I already use, and what does that enable?"

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🧠 AI as Infrastructure: The Invisible Foundation

AI systems are transitioning from being distinct applications we consciously use to becoming an invisible, foundational infrastructure layer within our digital environment. This isn't just about smarter tools; it's about a new technological bedrock.

This matters because it changes how we build, integrate, and rely on digital services. When AI becomes infrastructure, its capabilities are assumed, expected, and built upon, much like we build upon cloud computing or internet protocols today. It stops being a feature and starts being the foundation.

The most valuable aspect of this shift is the potential for enhanced efficiency, deeper integration, and the emergence of entirely new capabilities that are only possible when AI is a ubiquitous, reliable utility. Research into methods like ZERO SEARCH⁴ highlights the drive to make even the creation of this AI infrastructure more efficient and scalable.

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## 🔍 Gemini 2.5 Pro & Flash: Powering the Developer Stack

Google's recent updates to Gemini 2.5 Pro and the capabilities of Gemini 2.5 Flash highlight a strategic push to dominate the developer software stack¹ . Pro's enhanced coding support and large context window, combined with Flash's speed and cost-effectiveness for "glue" tasks, position Gemini as a versatile toolkit for developers building AI-powered applications.

For you, this means that the AI integrated into various tools (like Cursor or Windsurf) is becoming more powerful and economically viable. Google is focusing on making Gemini an indispensable part of the backend, influencing the operational and infrastructure layers of software development.

This development signals a broader industry pattern: AI companies are competing not just on model intelligence but on their ability to become integral, cost-effective components of the development lifecycle. The value multiplies when the AI is not just a smart endpoint but a deeply embedded part of the creation process.

What's happening here is the commodification of advanced AI capabilities into developer- consumable infrastructure, making it easier to build sophisticated AI features into a wider range of applications.

Read the full story from Google 

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## 🔍 Anthropic's API Gains Web Search: Claude Gets Real-Time Aware

Anthropic has equipped its API with a web search tool, allowing Claude models to access and utilize current information from the internet² . This enables developers to build Claude-powered applications that can deliver up-to-date insights and cite their sources, moving beyond the limitations of their static training data.

This matters because it directly addresses one of the key challenges for LLMs: accessing and reasoning over real-time information. For developers, this means AI agents can be built for tasks like market analysis, referencing new API documentation, or legal research with greater accuracy and timeliness, without the need to build or manage separate web search infrastructure.

The pattern emerging is that AI models are increasingly being augmented with direct access to external data and tools, transforming them from isolated brains into connected, world-aware platforms. This makes them more akin to dynamic infrastructure components than static knowledge repositories.

We're seeing AI platforms evolve to natively include capabilities that were previously complex add-ons, simplifying the development of more powerful and relevant AI solutions.

Read the full story from Anthropic

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## 🔍 Apple Explores New AI Search Horizons for Safari

Apple is reportedly considering integrating AI search engines, potentially including Perplexity, OpenAI, or Anthropic, into Safari³ . This move is underscored by Apple executive Eddy Cue's testimony that traditional search queries in Safari declined for the first time in 22 years, hinting at a potential shift in user behavior towards AI-driven information discovery.

This development is significant because it could disrupt the lucrative search market, long dominated by Google. If a major platform like Safari alters its default search or prominently features AI-powered alternatives, it could fundamentally change how billions of users find information online and impact existing revenue streams tied to search advertising.

The trajectory revealed is that core internet utilities, like search, are now being reimagined through the lens of AI. This isn't just about adding AI features but potentially re-architecting foundational aspects of our digital experience to be AI-native.

This exemplifies how AI is becoming an infrastructure-level concern for even the largest tech companies, forcing them to reconsider long-standing strategies and partnerships in light of AI's transformative potential.

Read the full story from The Verge

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## 🛠️ Practical Application

To leverage AI's shift towards infrastructure, consider these aspects:

- Integration over Isolation: Prioritize AI tools and models that integrate seamlessly into your existing workflows and tech stack. The less friction, the more likely it is to become a true utility for you (e.g., Gemini in developer tools, Claude's API with search).

- Underlying Capabilities: Look beyond the surface application. Understand how AI is enhancing the foundational layers of your tools. Is it making search better (Apple/Safari)? Is it making development more efficient (ZERO SEARCH, Gemini)?

- Future-Proofing: As AI becomes infrastructure, dependence on specific AI-enhanced platforms will grow. Consider the long-term viability, cost, and flexibility of the AI infrastructure you're implicitly or explicitly adopting.

To determine relevance, assess if an AI development helps consolidate or streamline existing processes by becoming an underlying capability, rather than just adding another standalone tool. For example, if an API like Anthropic's can simplify your information retrieval pipeline by offering built-in search, it's acting as improved infrastructure.

Start small: Identify one core digital process in your work that feels inefficient due to siloed information or tools. Investigate if newer AI-infused platforms or APIs offer a more integrated, infrastructure-level solution to that specific bottleneck.

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## 💡 Research Spotlight

### ZERO SEARCH Framework

What it does: A reinforcement learning framework by Alibaba Group that trains LLMs to develop effective search capabilities without interacting with live search engines, instead using a simulated environment⁴ .

Why it matters: This research addresses a key challenge in making LLMs more knowledgeable and reliable—efficiently teaching them to find and use external information. By reducing reliance on costly and unpredictable live search APIs during training, it could make advanced search-augmented AI more accessible and scalable to develop, contributing to the overall AI infrastructure.

Try it (Conceptually): While not a directly usable tool for end-users, understand its implication: the methods to build powerful AI infrastructure are becoming more sophisticated. This means future AI tools you do use will benefit from such underlying advancements in training efficiency and capability.

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## 🔮 Looking Forward

The deeper integration of AI as infrastructure will likely lead to more resilient, adaptable, and intelligent systems. Expect to see AI not just as a feature you toggle on or off, but as an inherent characteristic of the digital platforms you use, much like security or connectivity today.

As AI becomes a fundamental utility, what new classes of applications or services become possible when developers can assume sophisticated AI capabilities are readily available and cost-effective at the infrastructure level?

The evolution towards AI as infrastructure is about making artificial intelligence less artificial and more integral to every interaction and process in our digital lives.

Until next time…

– Atomic & Matter

1  : Tulsee Doshi (2025, May 6). Build rich, interactive web apps with an updated Gemini 2.5 Pro. Retrieved from https://blog.google/products/gemini/gemini-2-5-pro-updates/

2  : Anthropic. (2025, May 7). Introducing web search on the Anthropic API. Retrieved from https://www.anthropic.com/news/web-search-api

3  : Roth, E., & Feiner, L. (2025, May 7). Apple is looking at adding Perplexity and other AI search engines to Safari. The Verge. Retrieved from https://www.theverge.com/news/662656/apple-ai-search-alternatives-eddy-cue-testimony

4  : Sun, H., Qiao, Z., Guo, J., Fan, X., Hou, Y., Jiang, Y., Xie, P., Huang, F., & Zhang, Y. (2025). ZERO SEARCH : Incentivize the Search Capability of LLMs without Searching. arXiv:2505.04588. Retrieved from https://arxiv.org/pdf/2505.04588