What Google’s Q1 Earnings Reveal About the Future of Search and AI

Google’s parent company, Alphabet, reported better-than-expected Q1 2025 results on Thursday, April 24, posting solid performance across Cloud, Ads, and Search. Shares jumped as much as 4.3% as markets reopened Friday, adding $75 billion to the company’s market value.

But the real story wasn’t just in the numbers. It was in what Google chose to highlight.

The company used its Q1 earnings call and report to spotlight its deepening focus on AI: AI Overviews now reach 1.5 billion users a month, the new “AI Mode” is driving longer, more complex search queries, and Gemini 2.5 is being positioned as the industry’s leading model.

Meanwhile, Workspace delivers over 2 billion AI assists monthly, and Google Cloud’s momentum continues, powered by its AI-native stack, including Vertex AI and Gemini-powered agents.

And all this comes just as the DOJ (Department of Justice) pushes to break up Google’s search monopoly, calling for a divestment of Chrome. Add the mounting competition from Microsoft, OpenAI, and Amazon, Google’s Q1 focus was not just about innovation, but reinforcing its control over the platforms and interfaces that define how users interact with the internet.

For SaaS vendors, it’s a signal that discovery, distribution, and product interaction are being rebuilt around AI-native expectations, and Google is positioning itself at the center of that shift.

Google’s AI stack: From models to monetization

If Q1 made anything clear, it’s that Google is no longer treating AI as an overlay. It’s embedding it across every layer of its business, from the user interface to the silicon that powers it.

This comes as AI adoption accelerates across the broader software landscape. As of early 2025, G2 tracks over 4,000 AI products across 38 AI categories, with more than 100,000 reviews in these categories alone – a clear reflection of how deeply AI is becoming embedded in enterprise workflows.

Gemini’s expanding role across Google products

At the heart of Google’s AI strategy is Gemini, the company’s advanced AI models, with the latest Gemini 2.5 Pro model released in March. Gemini models power 15 Google products, each with over 500 million users, including Workspace, YouTube, Android, and Search.

This model integration is already changing how users interact with Google products. Google Workspace now delivers more than 2 billion AI assists per month. Gemini is also replacing Google Assistant on mobile devices, with updates planned for tablets, cars, and other connected devices later in the year.

Google also reported strong developer momentum. Since the start of the year, usage of AI Studio and the Gemini API has grown by over 200%. At the same time, its open-source Gemma models, geared toward flexibility and customization, have been downloaded more than 140 million times.

Perhaps the most important evolution is happening below the surface, at the infrastructure layer. The real flex in Google’s latest earnings call isn’t Gemini or Chatbot Arena rankings. It’s the revelation that Google’s AI infrastructure is now a strategic moat, decades in the making.

The infrastructure layer

In April, Google announced Ironwood, its seventh-generation Tensor Processing Unit (TPU), designed specifically for inference at scale. The chip delivers a 10x improvement in compute performance over its predecessor and is nearly twice as power efficient — advancements that aim to address the cost and scalability challenges of deploying large AI models in production environments.

But this is just one layer. Google’s AI stack sits atop one of the world’s most robust networks of over 2 million miles of fiber optic cables and 33 subsea cable systems. While many LLM startups race to shave off milliseconds of inference latency, Google is quietly optimizing the global movement of massive, multimodal data at scale. That kind of control becomes a serious edge when users expect instant responses from 10-billion-parameter models anywhere in the world.

This infrastructure powers Google’s broader enterprise AI platform, which now includes:

• Vertex AI, offering access to more than 200 foundation models, including Google’s own as well as third-party models like Llama 4 and Anthropic’s Claude.
• Tools like the Agent Development Kit and Agent Designer, which help companies build AI agents with low-code orchestration across 100+ enterprise systems.
• Prepackaged AI agents for customer service, software development, and internal knowledge management.

What does Google’s full-stack AI push mean for SaaS companies?

For SaaS companies, this isn’t just a technical evolution. It’s a fundamental repositioning of Google as both platform and partner, not just in hosting workloads, but in shaping how enterprise software is built and experienced. Here are the key takeaways:

• AI is becoming the baseline expectation, not a premium feature. With Gemini integrated across Google’s core products, users will increasingly expect AI-driven functionality by default. For SaaS vendors, this shifts product strategy from “should we add AI?” to “how do we differentiate when everyone has it?”
• AI assistants are the new integration surface. As Gemini replaces Google Assistant and gets embedded across Android and Workspace, SaaS tools may need to rethink how they integrate — not as standalone apps, but as tasks routed through conversational or intent-based agents.
• Owning the infrastructure stack is Google's long-term game. The release of Ironwood TPUs and the focus on inference efficiency show that Google is investing in long-term control of AI economics. Software companies depending on AI infrastructure should expect more bundling.

Search and discovery are being rewritten

For years, Google Search has been the front door to the internet. Two billion people search on Google every day, and 5 trillion searches are done annually, according to Google. For many software companies, it’s also been the front door to customer acquisition. Q1’s commentary showed that this door is being redesigned in a big way, and AI is shaping the blueprint.

AI Overviews and AI Mode reshape how people search

AI Overviews, the feature that summarizes answers at the top of search results, now reaches 1.5 billion users per month. 

“Nearly a year after we launched AI Overviews in the U.S., we continue to see that usage growth is increasing as people learn that Search is more useful for more of their queries.”

Sundar Pichai
CEO of Google and Alphabet

Alongside this, the experimental AI Mode, introduced in Labs, pushes further by adding advanced reasoning and multimodal capabilities. It is built to help users explore questions that require comparisons, deeper context, or more open-ended input.

According to Google, users are already engaging differently. AI Mode queries are twice as long as traditional ones. This shift is not just technical. It reflects a broader change in how people search and how businesses get discovered.

Google has noted that AI Overviews monetize at a rate similar to standard search results. But the nature of these summaries means less scrolling and more direct answers. 

For software companies that depend on organic visibility, this is a meaningful change. The top of the page may no longer be a list of links. Instead, it becomes a curated snapshot, where inclusion depends on how Google's AI interprets your brand, content, or positioning.

Multimodal search is growing fast

At the same time, Google is expanding multimodal search behaviors. Visual queries through Lens have grown by 5 billion since October. The Circle to Search feature, which lets users draw or tap on part of their screen to initiate a query, is now live on over 250 million devices and saw usage grow 40% in Q1.

These changes signal that search is no longer confined to the search bar. Users are discovering through images, gestures, voice, and hybrid interactions that blend inputs across apps and devices.

What does the AI search era mean for SaaS GTM and SEO strategy?

For SaaS marketers, product teams, and GTM leaders, the shifts in Google search are far from abstract. They influence how software products are researched, how alternatives are compared, and how purchase intent is formed. Strategies built around traditional SEO and click-based conversion flows may need to adapt quickly as AI-driven summarization becomes the new default.

Google did not frame these updates as a reimagining of discovery. But when users ask different kinds of questions and receive fewer, faster answers, the downstream effects on how products are surfaced will be hard to ignore.

For software companies, the key takeaways are:

• Visibility is changing. AI Overviews and AI Mode reduce link surfaces and shift the rules of engagement for organic discovery.
• Being part of the answer matters. As summaries replace lists, inclusion in AI-generated responses may become more impactful than traditional rankings. Search behavior is evolving.
• Longer, more conversational queries and new visual search tools mean discovery is happening across more modalities, not just text. Marketers will need to adapt quickly.
• SEO strategies and attribution models built around legacy search patterns will need rethinking as AI reshapes the top of the funnel.

Cloud momentum continues amid doubts

Heading into Q1, the cloud industry was navigating a wave of skepticism. After months of aggressive AI infrastructure investment, there were some questions on whether demand was leveling off. Several hyperscalers have signaled a more measured pace of capital expenditures, amid growing concerns over underutilized compute resources, delayed AI deployments, and uncertain monetization timelines.

Against that backdrop, Google Cloud reported 28% growth, reaching $12.26 billion in revenue for the quarter. While just under analyst estimates, the results still point to steady momentum, especially in a competitive environment where buyers evaluate AI investments more critically.

That scrutiny is warranted, but momentum remains. According to G2’s 2024 Buyer Behavior Report, 52% of buyers expect their software budgets to grow in 2025, and 83% of those who adopted AI platforms in the last three months already report positive ROI.

Steady growth, even as AI hype levels out

Google emphasized that much of this growth is being driven by AI-specific workloads, particularly in deployment and inference, rather than just experimentation.

The company’s investment in AI-native infrastructure, including its Ironwood TPUs and early access to NVIDIA’s Blackwell GPUs, is aimed at supporting this shift as enterprises move beyond pilots into more operational use cases.

The story remains one of gradual integration. Google is positioning itself as a long-term player in the enterprise AI market, supported by tooling and performance capabilities, but real enterprise transformation is still unfolding in phases.

Strategic implications for software vendors

For software companies, the key takeaways are:

• Cloud growth is real, but expectations are resetting. Buyers are under pressure to show ROI from AI, which means SaaS products that drive measurable outcomes will resonate more than those focused on experimentation alone.
• Differentiation is shifting from raw compute to usability. Google’s focus on agents, APIs, and deployment frameworks highlights that the cloud race now includes developer experience and ecosystem alignment.
• Multi-cloud strategies and openness matter. As Google integrates third-party models like Llama 4 and Claude, it signals awareness that customers want optionality, and vendors should think similarly.

Looking forward: The new rules of growth are being written in AI

Google’s Q1 report wasn’t just a performance update. It was a map of where the digital landscape is heading and a preview of how core functions like search, software integration, and enterprise infrastructure are being reshaped by AI.

From Gemini-driven interfaces to evolving cloud economics to regulatory scrutiny, the rules that defined the past decade of software growth are changing. For SaaS companies, product teams, and GTM leaders, now is the time to reassess where your visibility, reach, and platform dependencies sit in a rapidly shifting ecosystem.

At G2, we’re tracking how software buyers and vendors alike are adapting to this new environment, including how AI is influencing product discovery, purchasing behavior, and category dynamics.

Explore real-time software trends and vendor insights on G2. Because when the platforms shift, the strategy should too.

Edited by Brittany K. King

This article provides general information and does not constitute legal, tax, or business advice. Companies should consult with appropriate professionals regarding specific situations.