Just a 12 months ago, the narrative around Google and enterprise AI felt stuck. Despite inventing core technologies just like the Transformer, the tech giant seemed perpetually on the back foot, overshadowed by OpenAI‘s viral success, Anthropic‘s coding prowess and Microsoft‘s aggressive enterprise push.
But witness the scene at Google Cloud Next 2025 in Las Vegas last week: A confident Google, armed with benchmark-topping models, formidable infrastructure and a cohesive enterprise strategy, declaring a shocking turnaround. In a closed-door analyst meeting with senior Google executives, one analyst summed it up. This seems like the moment, he said, when Google went from “catch up, to catch us.”
This sentiment that Google has not only caught up with but even surged ahead of OpenAI and Microsoft within the enterprise AI race prevailed throughout the event. And it’s greater than just Google’s marketing spin. Evidence suggests Google has leveraged the past 12 months for intense, focused execution, translating its technological assets right into a performant, integrated platform that’s rapidly winning over enterprise decision-makers. From boasting the world’s strongest AI models running on hyper-efficient custom silicon, to a burgeoning ecosystem of AI agents designed for real-world business problems, Google is making a compelling case that it was never actually lost – but that its stumbles masked a period of deep, foundational development.
Now, with its integrated stack firing on all cylinders, Google appears positioned to guide the subsequent phase of the enterprise AI revolution. And in my interviews with several Google executives at Next, they said Google wields benefits in infrastructure and model integration that competitors like OpenAI, Microsoft or AWS will struggle to duplicate.
The shadow of doubt: acknowledging the recent past
It’s not possible to understand the present momentum without acknowledging the recent past. Google was the birthplace of the Transformer architecture, which sparked the fashionable revolution in large language models (LLMs). Google also began investing in specialized AI hardware (TPUs), which are actually driving industry-leading efficiency, a decade ago. And yet, two and a half years ago, it inexplicably found itself playing defense.
OpenAI’s ChatGPT captured the general public imagination and enterprise interest at breathtaking speed and have become the fastest-growing app in history. Competitors like Anthropic carved out niches in areas like coding.
Google’s own public steps sometimes seemed tentative or flawed. The infamous Bard demo fumbles in 2023 and the later controversy over its image generator producing historically inaccurate depictions fed a narrative of an organization potentially hampered by internal bureaucracy or overcorrection on alignment. It felt like Google was lost: The AI stumbles looked as if it would fit a pattern, first shown by Google’s initial slowness within the cloud competition, where it remained a distant third in market share behind Amazon and Microsoft. Google Cloud CTO Will Grannis acknowledged the early questions on whether Google Cloud would stand behind in the long term. “Is it even an actual thing?,” he recalled people asking him. The query lingered: Could Google translate its undeniable research brilliance and infrastructure scale into enterprise AI dominance?
The pivot: a conscious decision to guide
Behind the scenes, nevertheless, a shift was underway, catalyzed by a conscious decision at the very best levels to reclaim leadership. Mat Velloso, VP of product for Google DeepMind’s AI Developer Platform, described sensing a pivotal moment upon joining Google in Feb. 2024, after leaving Microsoft. “When I got here to Google, I spoke with Sundar (Pichai), I spoke with several leaders here, and I felt like that was the moment where they were deciding, okay, this (generative AI) is a thing the industry clearly cares about. Let’s make it occur,” Velloso shared in an interview with VentureBeat during Next last week.
This renewed push wasn’t hampered by a feared “brain drain” that some outsiders felt was depleting Google. In fact, the corporate quietly doubled down on execution in early 2024 – a 12 months marked by aggressive hiring, internal unification and customer traction. While competitors made splashy hires, Google retained its core AI leadership, including DeepMind CEO Demis Hassabis and Google Cloud CEO Thomas Kurian, providing stability and deep expertise.
Moreover, talent began flowing towards Google’s focused mission. Logan Kilpatrick, as an illustration, returned to Google from OpenAI, drawn by the chance to construct foundational AI throughout the company, creating it. He joined Velloso in what he described as a “zero to 1 experience,” tasked with constructing developer traction for Gemini from the bottom up. “It was just like the team was me on day one… we actually don’t have any users on this platform, we’ve no revenue. No one is interested by Gemini at this moment,” Kilpatrick recalled of the start line. People accustomed to the interior dynamics also credit leaders like Josh Woodward, who helped start AI Studio and now leads the Gemini App and Labs. More recently, Noam Shazeer, a key co-author of the unique “Attention Is All You Need” Transformer paper during his first tenure at Google, returned to the corporate in late 2024 as a technical co-lead for the crucial Gemini project
This concerted effort, combining these hires, research breakthroughs, refinements to its database technology and a sharpened enterprise focus overall, began yielding results. These cumulative advances, combined with what CTO Will Grannis termed “lots of of fine-grain” platform elements, set the stage for the announcements at Next ’25, and cemented Google’s comeback narrative.
Pillar 1: Gemini 2.5 and the era of considering models
It’s true that a number one enterprise mantra has change into “it’s not only concerning the model.” After all, the performance gap between leading models has narrowed dramatically, and tech insiders acknowledge that true intelligence is coming from technology packaged across the model, not only the model itself – for instance, agentic technologies that allow a model to make use of tools and explore the net around it.
Despite this, to own the demonstrably best-performing LLM is a very important feat – and a robust validator, an indication that the model-owning company has things like superior research and essentially the most efficient underlying technology architecture. With the discharge of Gemini 2.5 Pro just weeks before Next ’25, Google definitively seized that mantle. It quickly topped the independent Chatbot Arena leaderboard, significantly outperforming even OpenAI’s latest GPT-4o variant, and aced notoriously difficult reasoning benchmarks like Humanity’s Last Exam. As Pichai stated within the keynote, “It’s our most intelligent AI model ever. And it’s the perfect model on this planet.” The model had driven an 80 percent increase in Gemini usage inside a month, he Tweeted individually.
For the primary time, Google’s Gemini demand was on fire. As I detailed previously, apart from Gemini 2.5 Pro’s raw intelligence, what impressed me is its reasoning. Google has engineered a “considering” capability, allowing the model to perform multi-step reasoning, planning, and even self-reflection before finalizing a response. The structured, coherent chain-of-thought (CoT) – using numbered steps and sub-bullets – avoids the rambling or opaque nature of outputs from other models from DeepSeek or OpenAI. For technical teams evaluating outputs for critical tasks, this transparency allows validation, correction, and redirection with unprecedented confidence.
But more importantly for enterprise users, Gemini 2.5 Pro also dramatically closed the gap in coding, which is one in every of the largest application areas for generative AI. In an interview with VentureBeat, CTO Fiona Tan, the CTO of leading retailer Wayfair, said that after initial tests, the corporate found it “stepped up quite a bit” and was now “pretty comparable” to Anthropic’s Claude 3.7 Sonnet, previously the popular alternative for a lot of developers.
Google also added a large 1 million token context window to the model, enabling reasoning across entire codebases or lengthy documentation, far exceeding the capabilities of the models of OpenAI or Anthropic. (OpenAI responded this week with models featuring similarly large context windows, though benchmarks suggest Gemini 2.5 Pro retains an edge in overall reasoning). This advantage allows for complex, multi-file software engineering tasks.
Complementing Pro is Gemini 2.5 Flash, announced at Next ’25 and released just yesterday. Also, a “considering” model, Flash is optimized for low latency and cost-efficiency. You can control how much the model reasons and balance performance along with your budget. This tiered approach further reflects the “intelligence per dollar” strategy championed by Google executives.
Velloso showed a chart revealing that across the intelligence spectrum, Google models offer the perfect value. “If we had this conversation one 12 months ago… I might don’t have anything to point out,” Velloso admitted, highlighting the rapid turnaround. “And now, like, across the board, we’re, for those who’re on the lookout for whatever model, whatever size, like, for those who’re not Google, you’re losing money.” Similar charts have been updated to account for OpenAI’s latest model releases this week, all showing the identical thing: Google’s models offer the perfect intelligence per dollar. See below:
Wayfair’s Tan said she also observed promising latency improvements with 2.5 Pro: “Gemini 2.5 got here back faster,” making it viable for “more customer-facing form of capabilities,” she said, something she said hasn’t been the case before with other models. Gemini could change into the primary model Wayfair uses for these customer interactions, she said.
The Gemini family’s capabilities extend to multimodality, integrating seamlessly with Google’s other leading models like Imagen 3 (image generation), Veo 2 (video generation), Chirp 3 (audio), and the newly announced Lyria (text-to-music), all accessible via Google’s platform for Enterprise users, Vertex. Google is the one company that provides its own generative media models across all modalities on its platform. Microsoft, AWS and OpenAI should partner with other firms to do that.
Pillar 2: Infrastructure prowess – the engine under the hood
The ability to rapidly iterate and efficiently serve these powerful models stems from Google’s arguably unparalleled infrastructure, honed over a long time of running planet-scale services. Central to that is the Tensor Processing Unit (TPU).
At Next ’25, Google unveiled Ironwood, its seventh-generation TPU, explicitly designed for the demands of inference and “considering models.” The scale is immense, tailored for demanding AI workloads: Ironwood pods pack over 9,000 liquid-cooled chips, delivering a claimed 42.5 exaflops of compute power. Google’s VP of ML Systems Amin Vahdat said on stage at Next that that is “greater than 24 times” the compute power of the world’s current #1 supercomputer.
Google stated that Ironwood offers 2x perf/watt relative to Trillium, the previous generation of TPU. This is important since enterprise customers increasingly say energy costs and availability constrain large-scale AI deployments.
Google Cloud CTO Will Grannis emphasized the of this progress. Year over 12 months, Google is making 10x, 8x, 9x, 10x improvements in its processors, he told VentureBeat in an interview, creating what he called a “hyper Moore’s law” for AI accelerators. He said customers are buying Google’s roadmap, not only its technology.
Google’s position fueled this sustained TPU investment. It must efficiently power massive services like Search, YouTube, and Gmail for greater than 2 billion users. This necessitated developing custom, optimized hardware long before the present generative AI boom. While Meta operates at an analogous consumer scale, other competitors lacked this specific internal driver for decade-long, vertically integrated AI hardware development.
Now these TPU investments are paying off because they’re driving the efficiency not just for its own apps, but in addition they allow Google to supply Gemini to other users at a greater intelligence per dollar, every part equal.
Why can’t Google’s competitors buy efficient processors from Nvidia, you ask? It’s true that Nvidia’s GPU processors dominate the method pre-training of LLMs. But market demand has pushed up the worth of those GPUs, and Nvidia takes a healthy cut for itself as profit. This passes significant costs along to users of its chips. And also, while pre-training has dominated the usage of AI chips up to now, that is changing now that enterprises are literally deploying these applications. This is where ” inference” is available in, and here TPUs are considered more efficient than GPUs for workloads at scale.
When you ask Google executives where their major technology advantage in AI comes from, they sometimes fall back to the TPU as an important. Mark Lohmeyer, the VP who runs Google’s computing infrastructure, was unequivocal: TPUs are “actually a highly differentiated a part of what we do… OpenAI, they don’t have those capabilities.”
Significantly, Google presents TPUs not in isolation, but as a part of the broader, more complex enterprise AI architecture. For technical insiders, it’s understood that top-tier performance hinges on integrating increasingly specialized technology breakthroughs. Many updates were detailed at Next. Vahdat described this as a “supercomputing system,” integrating hardware (TPUs, the newest Nvidia GPUs like Blackwell and upcoming Vera Rubin, advanced storage like Hyperdisk Exapools, Anywhere Cache, and Rapid Storage) with a unified software stack. This software includes Cluster Director for managing accelerators, Pathways (Gemini’s distributed runtime, now available to customers), and bringing optimizations like vLLM to TPUs, allowing easier workload migration for those previously on Nvidia/PyTorch stacks. This integrated system, Vahdat argued, is why Gemini 2.0 Flash achieves 24 times higher intelligence per dollar, in comparison with GPT-4o.
Google can be extending its physical infrastructure reach. Cloud WAN makes Google’s low-latency 2-million-mile private fiber network available to enterprises, promising as much as 40% faster performance and 40% lower total cost of ownership (TCO) in comparison with customer-managed networks.
Furthermore, Google Distributed Cloud (GDC) allows Gemini and Nvidia hardware (via a Dell partnership) to run in sovereign, on-premises, and even air-gapped environments – a capability Nvidia CEO Jensen Huang lauded as “utterly gigantic” for bringing state-of-the-art AI to regulated industries and nations. At Next, Huang called Google’s infrastructure the perfect on this planet: “No company is healthier at each layer of computing than Google and Google Cloud,” he said.
Pillar 3: The integrated full stack – connecting the dots
Google’s strategic advantage grows when considering how these models and infrastructure components are woven right into a cohesive platform. Unlike competitors, which frequently depend on partnerships to bridge gaps, Google controls nearly every layer, enabling tighter integration and faster innovation cycles.
So why does this integration matter, if a competitor like Microsoft can simply partner with OpenAI to match infrastructure breadth with LLM model prowess? The Googlers I talked with said it makes an enormous difference, and so they got here up with anecdotes to back it up.
Take the numerous improvement of Google’s enterprise database BigQuery. The database now offers a knowledge graph that permits LLMs to look over data rather more efficiently, and it now boasts greater than five times the shoppers of competitors like Snowflake and Databricks, VentureBeat reported yesterday. Yasmeen Ahmad, Head of Product for Data Analytics at Google Cloud, said the vast improvements were only possible because Google’s data teams were working closely with the DeepMind team. They worked through use cases that were hard to unravel, and this led to the database providing 50 percent more accuracy based on common queries, no less than in accordance with Google’s internal testing, in attending to the precise data than the closest competitors, Ahmad told VentureBeat in an interview. Ahmad said this form of deep integration across the stack is how Google has “leapfrogged” the industry.
This internal cohesion contrasts sharply with the “frenemies” dynamic at Microsoft. While Microsoft partners with OpenAI to distribute its models on the Azure cloud, Microsoft can be constructing its own models. Mat Velloso, the Google executive who now leads the AI developer program, left Microsoft after getting frustrated attempting to align Windows Copilot plans with OpenAI’s model offerings. “How do you share your product plans with one other company that’s actually competing with you… The whole thing is a contradiction,” he recalled. “Here I sit side by side with the people who find themselves constructing the models.”
This integration speaks to what Google leaders see as their core advantage: its unique ability to attach deep expertise across the complete spectrum, from foundational research and model constructing to “planet-scale” application deployment and infrastructure design.
Vertex AI serves because the central nervous system for Google’s enterprise AI efforts. And the mixing goes beyond just Google’s own offerings. Vertex’s Model Garden offers over 200 curated models, including Google’s, Meta’s Llama 4, and diverse open-source options. Vertex provides tools for tuning, evaluation (including AI-powered Evals, which Grannis highlighted as a key accelerator), deployment, and monitoring. Its grounding capabilities leverage internal AI-ready databases alongside compatibility with external vector databases. Add to that Google’s recent offerings to ground models with Google Search, the world’s best search engine.
Integration extends to Google Workspace. New features announced at Next ’25, like “Help Me Analyze” in Sheets (yes, Sheets now has an “=AI” formula), Audio Overviews in Docs and Workspace Flows, further embed Gemini’s capabilities into day by day workflows, creating a robust feedback loop for Google to make use of to enhance the experience.
While driving its integrated stack, Google also champions openness where it serves the ecosystem. Having driven Kubernetes adoption, it’s now promoting JAX for AI frameworks and now open protocols for agent communication (A2A) alongside support for existing standards (MCP). Google can be offering lots of of connectors to external platforms from inside Agentspace, which is Google’s recent unified interface for workers to search out and use agents. This hub concept is compelling. The keynote demonstration of Agentspace (starting at 51:40) illustrates this. Google offers users pre-built agents, or employees or developers can construct their very own using no-code AI capabilities. Or they’ll pull in agents from the skin via A2A connectors. It integrates into the Chrome browser for seamless access.
Pillar 4: Focus on enterprise value and the agent ecosystem
Perhaps essentially the most significant shift is Google’s sharpened concentrate on solving concrete enterprise problems, particularly through the lens of AI agents. Thomas Kurian, Google Cloud CEO, outlined three reasons customers select Google: the AI-optimized platform, the open multi-cloud approach allowing connection to existing IT, and the enterprise-ready concentrate on security, sovereignty, and compliance.
Agents are key to this strategy. Aside from AgentSpace, this also includes:
Building Blocks: The open-source Agent Development Kit (ADK), announced at Next, has already seen significant interest from developers. The ADK simplifies creating multi-agent systems, while the proposed Agent2Agent (A2A) protocol goals to make sure interoperability, allowing agents built with different tools (Gemini ADK, LangGraph, CrewAI, etc.) to collaborate. Google’s Grannis said that A2A anticipates the dimensions and security challenges of a future with potentially lots of of 1000’s of interacting agents.
This A2A protocol is basically vital. In a background interview with VentureBeat this week, the CISO of a significant US retailer, who requested anonymity due to sensitivity around security issues. But they said the A2A protocol was helpful since the retailer is on the lookout for an answer to tell apart between real people and bots who’re using agents to purchase products. This retailer desires to avoid selling to scalper bots, and with A2A, it’s easier to barter with agents to confirm their owner identities.
Purpose-built Agents: Google showcased expert agents integrated into Agentspace (like NotebookLM, Idea Generation, Deep Research) and highlighted five key categories gaining traction: Customer Agents (powering tools like Reddit Answers, Verizon’s support assistant, Wendy’s drive-thru), Creative Agents (utilized by WPP, Brandtech, Sphere), Data Agents (driving insights at Mattel, Spotify, Bayer), Coding Agents (Gemini Code Assist), and Security Agents (integrated into the brand new Google Unified Security platform).
This comprehensive agent strategy appears to be resonating. Conversations with executives at three other large enterprises this past week, also speaking anonymously as a result of competitive sensitivities, echoed this enthusiasm for Google’s agent strategy. Google Cloud COO Francis DeSouza confirmed in an interview: “Every conversation includes AI. Specifically, every conversation includes agents.”
Kevin Laughridge, an executive at Deloitte, a giant user of Google’s AI products, and a distributor of them to other firms, described the agent market as a “land grab” where Google’s early moves with protocols and its integrated platform offer significant benefits. “Whoever is getting out first and getting essentially the most agents that truly deliver value – is who’s going to win on this race,” Laughridge said in an interview. He said Google’s progress was “astonishing,” noting that custom agents Deloitte built only a 12 months ago could now be replicated “out of the box” using Agentspace. Deloitte itself is constructing 100 agents on the platform, targeting mid-office functions like finance, risk and engineering, he said.
The customer proof points are mounting. At Next, Google cited “500 plus customers in production” with generative AI, up from just “dozens of prototypes” a 12 months ago. If Microsoft was perceived as way ahead a 12 months ago, that doesn’t seem so obviously the case anymore. Given the PR war from all sides, it’s difficult to say who is basically winning straight away definitively. Metrics vary. Google’s 500 number isn’t directly comparable to the 400 case studies Microsoft promotes (and Microsoft, in response, told VentureBeat at press time that it plans to update this public count to 600 shortly, underscoring the extraordinary marketing). And if Google’s distribution of AI through its apps is important, Microsoft’s Copilot distribution through its 365 offering is equally impressive. Both are actually hitting tens of millions of developers through APIs.
(Editor’s note: Understanding how enterprises are navigating this ‘agent land grab,’ and successfully deploying these complex AI solutions, can be central to the discussions at VentureBeat’s Transform event this June 24-25 in San Francisco.)
But examples abound of Google’s traction:
- Wendy’s: Deployed an AI drive-thru system to 1000’s of locations in only one 12 months, improving worker experience and order accuracy. Google Cloud CTO Will Grannis noted that the AI system is able to understanding slang and filtering out background noise, significantly reducing the stress of live customer interactions. That frees up staff to concentrate on food prep and quality — a shift Grannis called “an incredible example of AI streamlining real-world operations.”
- Salesforce: Announced a significant expansion, enabling its platform to run on Google Cloud for the primary time (beyond AWS), citing Google’s ability to assist them “innovate and optimize.”
- Honeywell & Intuit: Companies previously strongly related to Microsoft and AWS, respectively, now partnering with Google Cloud on AI initiatives.
- Major Banks (Deutsche Bank, Wells Fargo): Leveraging agents and Gemini for research, evaluation, and modernizing customer support.
- Retailers (Walmart, Mercado Libre, Lowe’s): Using search, agents, and data platforms.
This enterprise traction fuels Google Cloud’s overall growth, which has outpaced AWS and Azure for the last three quarters. Google Cloud reached a $44 billion annualized run rate in 2024, up from just $5 billion in 2018.
Navigating the competitive waters
Google’s ascent doesn’t mean competitors are standing still. OpenAI’s rapid releases this week of GPT-4.1 (focused on coding and long context) and the o-series (multimodal reasoning, tool use) reveal OpenAI’s continued innovation. Moreover, OpenAI’s recent image generation feature update in GPT-4o fueled massive growth over just the last month, helping ChatGPT reach 800 million users. Microsoft continues to leverage its vast enterprise footprint and OpenAI partnership, while Anthropic stays a powerful contender, particularly in coding and safety-conscious applications.
However, it’s indisputable that Google’s narrative has improved remarkably. Just a 12 months ago, Google was viewed as a stodgy, halting, blundering competitor that perhaps was about to blow its probability at leading AI in any respect. Instead, its unique, integrated stack and company steadfastness has revealed something else: Google possesses world-class capabilities across your entire spectrum – from chip design (TPUs) and global infrastructure to foundational model research (DeepMind), application development (Workspace, Search, YouTube), and enterprise cloud services (Vertex AI, BigQuery, Agentspace). “We’re the one hyperscaler that’s within the foundational model conversation,” deSouza stated flatly. This end-to-end ownership allows for optimizations (like “intelligence per dollar”) and integration depth that partnership-reliant models struggle to match. Competitors often have to stitch together disparate pieces, potentially creating friction or limiting innovation speed.
Google’s moment is now
While the AI race stays dynamic, Google has assembled all these pieces on the precise moment the market demands them. As Deloitte’s Laughridge put it, Google hit a degree where its capabilities aligned perfectly “where the market demanded it.” If you were waiting for Google to prove itself in enterprise AI, you will have missed the moment — it already has. The company that invented lots of the core technologies powering this revolution appears to have finally caught up – and greater than that, it’s now setting the pace that competitors have to match.
In the video below, recorded right after Next, AI expert Sam Witteveen and I break down the present landscape and emerging trends, and why Google’s AI ecosystem feels so strong:
