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Only a yr in the past, the narrative round Google and enterprise AI felt caught. Regardless of inventing core applied sciences just like the Transformer, the tech big appeared perpetually on the again foot, overshadowed by OpenAI‘s viral success, Anthropic‘s coding prowess and Microsoft‘s aggressive enterprise push.
However witness the scene at Google Cloud Subsequent 2025 in Las Vegas final week: A assured Google, armed with benchmark-topping fashions, formidable infrastructure and a cohesive enterprise technique, declaring a shocking turnaround. In a closed-door analyst assembly with senior Google executives, one analyst summed it up. This feels just like the second, he mentioned, when Google went from “catch up, to catch us.”
This sentiment that Google has not solely caught up with however even surged forward of OpenAI and Microsoft within the enterprise AI race prevailed all through the occasion. And it’s extra than simply Google’s advertising and marketing spin. Proof suggests Google has leveraged the previous yr for intense, centered execution, translating its technological belongings right into a performant, built-in platform that’s quickly profitable over enterprise decision-makers. From boasting the world’s strongest AI fashions working on hyper-efficient customized silicon, to a burgeoning ecosystem of AI brokers designed for real-world enterprise issues, Google is making a compelling case that it was by no means really misplaced – however that its stumbles masked a interval of deep, foundational improvement.
Now, with its built-in stack firing on all cylinders, Google seems positioned to steer the subsequent part of the enterprise AI revolution. And in my interviews with a number of Google executives at Subsequent, they mentioned Google wields benefits in infrastructure and mannequin integration that opponents like OpenAI, Microsoft or AWS will wrestle to copy.
The shadow of doubt: acknowledging the latest previous
It’s not possible to understand the present momentum with out acknowledging the latest previous. Google was the birthplace of the Transformer structure, which sparked the fashionable revolution in massive language fashions (LLMs). Google additionally began investing in specialised AI {hardware} (TPUs), which are actually driving industry-leading effectivity, a decade in the past. And but, two and a half years in the past, it inexplicably discovered itself taking part in protection.
OpenAI’s ChatGPT captured the general public creativeness and enterprise curiosity at breathtaking velocity and have become the fastest-growing app in historical past. Opponents like Anthropic carved out niches in areas like coding.
Google’s personal public steps typically appeared tentative or flawed. The notorious Bard demo fumbles in 2023 and the later controversy over its picture generator producing traditionally inaccurate depictions fed a story of an organization probably hampered by inside paperwork or overcorrection on alignment. It felt like Google was misplaced: The AI stumbles appeared to suit a sample, first proven by Google’s preliminary slowness within the cloud competitors, the place it remained a distant third in market share behind Amazon and Microsoft. Google Cloud CTO Will Grannis acknowledged the early questions on whether or not Google Cloud would stand behind in the long term. “Is it even an actual factor?,” he recalled folks asking him. The query lingered: Might Google translate its simple analysis brilliance and infrastructure scale into enterprise AI dominance?
The pivot: a aware resolution to steer
Behind the scenes, nonetheless, a shift was underway, catalyzed by a aware resolution on the highest ranges to reclaim management. Mat Velloso, VP of product for Google DeepMind’s AI Developer Platform, described sensing a pivotal second upon becoming a member of Google in Feb. 2024, after leaving Microsoft. “After I got here to Google, I spoke with Sundar [Pichai], I spoke with a number of leaders right here, and I felt like that was the second the place they have been deciding, okay, this [generative AI] is a factor the {industry} clearly cares about. Let’s make it occur,” Velloso shared in an interview with VentureBeat throughout Subsequent final week.
This renewed push wasn’t hampered by a feared “mind drain” that some outsiders felt was depleting Google. In truth, the corporate quietly doubled down on execution in early 2024 – a yr marked by aggressive hiring, inside unification and buyer traction. Whereas opponents made splashy hires, Google retained its core AI management, together with DeepMind CEO Demis Hassabis and Google Cloud CEO Thomas Kurian, offering stability and deep experience.
Furthermore, expertise started flowing in the direction of Google’s centered mission. Logan Kilpatrick, as an example, returned to Google from OpenAI, drawn by the chance to construct foundational AI throughout the firm, creating it. He joined Velloso in what he described as a “zero to at least one expertise,” tasked with constructing developer traction for Gemini from the bottom up. “It was just like the staff was me on day one… we really don’t have any customers on this platform, we have now no income. Nobody is excited about Gemini at this second,” Kilpatrick recalled of the place to begin. Individuals accustomed to the inner dynamics additionally credit score leaders like Josh Woodward, who helped begin AI Studio and now leads the Gemini App and Labs. Extra not too long ago, Noam Shazeer, a key co-author of the unique “Consideration Is All You Want” Transformer paper throughout his first tenure at Google, returned to the corporate in late 2024 as a technical co-lead for the essential Gemini venture
This concerted effort, combining these hires, analysis breakthroughs, refinements to its database expertise and a sharpened enterprise focus general, started yielding outcomes. These cumulative advances, mixed with what CTO Will Grannis termed “tons of of fine-grain” platform components, set the stage for the bulletins at Subsequent ’25, and cemented Google’s comeback narrative.
Pillar 1: Gemini 2.5 and the period of considering fashions
It’s true {that a} main enterprise mantra has turn into “it’s not simply concerning the mannequin.” In spite of everything, the efficiency hole between main fashions has narrowed dramatically, and tech insiders acknowledge that true intelligence is coming from expertise packaged across the mannequin, not simply the mannequin itself – for instance, agentic applied sciences that permit a mannequin to make use of instruments and discover the online round it.
Regardless of this, to own the demonstrably best-performing LLM is a vital feat – and a strong validator, an indication that the model-owning firm has issues like superior analysis and essentially the most environment friendly underlying expertise structure. With the discharge of Gemini 2.5 Professional simply weeks earlier than Subsequent ’25, Google definitively seized that mantle. It rapidly topped the impartial Chatbot Area leaderboard, considerably outperforming even OpenAI’s newest GPT-4o variant, and aced notoriously troublesome reasoning benchmarks like Humanity’s Final Examination. As Pichai said within the keynote, “It’s our most clever AI mannequin ever. And it’s the finest mannequin on this planet.” The mannequin had pushed an 80 p.c improve in Gemini utilization inside a month, he Tweeted individually.
For the primary time, Google’s Gemini demand was on fireplace. As I detailed beforehand, other than Gemini 2.5 Professional’s uncooked intelligence, what impressed me is its demonstrable reasoning. Google has engineered a “considering” functionality, permitting the mannequin to carry out multi-step reasoning, planning, and even self-reflection earlier than finalizing a response. The structured, coherent chain-of-thought (CoT) – utilizing numbered steps and sub-bullets – avoids the rambling or opaque nature of outputs from different fashions from DeepSeek or OpenAI. For technical groups evaluating outputs for vital duties, this transparency permits validation, correction, and redirection with unprecedented confidence.
However extra importantly for enterprise customers, Gemini 2.5 Professional additionally dramatically closed the hole in coding, which is without doubt one of the greatest utility areas for generative AI. In an interview with VentureBeat, CTO Fiona Tan, the CTO of main retailer Wayfair, mentioned that after preliminary assessments, the corporate discovered it “stepped up fairly a bit” and was now “fairly comparable” to Anthropic’s Claude 3.7 Sonnet, beforehand the popular selection for a lot of builders.
Google additionally added a large 1 million token context window to the mannequin, enabling reasoning throughout total codebases or prolonged documentation, far exceeding the capabilities of the fashions of OpenAI or Anthropic. (OpenAI responded this week with fashions that includes equally massive context home windows, although benchmarks counsel Gemini 2.5 Professional retains an edge in general reasoning). This benefit permits for advanced, multi-file software program engineering duties.
Complementing Professional is Gemini 2.5 Flash, introduced at Subsequent ’25 and launched simply yesterday. Additionally, a “considering” mannequin, Flash is optimized for low latency and cost-efficiency. You possibly can management how a lot the mannequin causes and steadiness efficiency along with your funds. This tiered method additional displays the “intelligence per greenback” technique championed by Google executives.
Velloso confirmed a chart revealing that throughout the intelligence spectrum, Google fashions supply the most effective worth. “If we had this dialog one yr in the past… I might don’t have anything to indicate,” Velloso admitted, highlighting the speedy turnaround. “And now, like, throughout the board, we’re, in the event you’re on the lookout for no matter mannequin, no matter dimension, like, in the event you’re not Google, you’re shedding cash.” Comparable charts have been up to date to account for OpenAI’s newest mannequin releases this week, all exhibiting the identical factor: Google’s fashions supply the most effective intelligence per greenback. See beneath:
For any given value, Google’s fashions supply extra intelligence than different fashions, about 90 p.c of the time. Supply: Pierre Bongrand.
Wayfair’s Tan mentioned she additionally noticed promising latency enhancements with 2.5 Professional: “Gemini 2.5 got here again quicker,” making it viable for “extra customer-facing form of capabilities,” she mentioned, one thing she mentioned hasn’t been the case earlier than with different fashions. Gemini might turn into the primary mannequin Wayfair makes use of for these buyer interactions, she mentioned.
The Gemini household’s capabilities lengthen to multimodality, integrating seamlessly with Google’s different main fashions like Imagen 3 (picture technology), Veo 2 (video technology), Chirp 3 (audio), and the newly introduced Lyria (text-to-music), all accessible through Google’s platform for Enterprise customers, Vertex. Google is the one firm that provides its personal generative media fashions throughout all modalities on its platform. Microsoft, AWS and OpenAI must companion with different corporations to do that.
Pillar 2: Infrastructure prowess – the engine below the hood
The power to quickly iterate and effectively serve these highly effective fashions stems from Google’s arguably unparalleled infrastructure, honed over many years of working planet-scale companies. Central to that is the Tensor Processing Unit (TPU).
At Subsequent ’25, Google unveiled Ironwood, its seventh-generation TPU, explicitly designed for the calls for of inference and “considering fashions.” The dimensions is immense, tailor-made for demanding AI workloads: Ironwood pods pack over 9,000 liquid-cooled chips, delivering a claimed 42.5 exaflops of compute energy. Google’s VP of ML Techniques Amin Vahdat mentioned on stage at Subsequent that that is “greater than 24 instances” the compute energy of the world’s present #1 supercomputer.
Google said that Ironwood gives 2x perf/watt relative to Trillium, the earlier technology of TPU. That is important since enterprise prospects more and more say vitality prices and availability constrain large-scale AI deployments.
Google Cloud CTO Will Grannis emphasised the consistency of this progress. Yr over yr, Google is making 10x, 8x, 9x, 10x enhancements in its processors, he informed VentureBeat in an interview, creating what he referred to as a “hyper Moore’s regulation” for AI accelerators. He mentioned prospects are shopping for Google’s roadmap, not simply its expertise.
Google’s place fueled this sustained TPU funding. It must effectively energy large companies like Search, YouTube, and Gmail for greater than 2 billion customers. This necessitated creating customized, optimized {hardware} lengthy earlier than the present generative AI increase. Whereas Meta operates at an analogous client scale, different opponents lacked this particular inside driver for decade-long, vertically built-in AI {hardware} improvement.
Now these TPU investments are paying off as a result of they’re driving the effectivity not just for its personal apps, however in addition they permit Google to supply Gemini to different customers at a greater intelligence per greenback, every little thing equal.
Why can’t Google’s opponents purchase environment friendly processors from Nvidia, you ask? It’s true that Nvidia’s GPU processors dominate the method pre-training of LLMs. However market demand has pushed up the worth of those GPUs, and Nvidia takes a wholesome minimize for itself as revenue. This passes important prices alongside to customers of its chips. And likewise, whereas pre-training has dominated the utilization of AI chips up to now, that is altering now that enterprises are literally deploying these purposes. That is the place ” inference” is available in, and right here TPUs are thought of extra environment friendly than GPUs for workloads at scale.
While you ask Google executives the place their essential expertise benefit in AI comes from, they normally fall again to the TPU as a very powerful. Mark Lohmeyer, the VP who runs Google’s computing infrastructure, was unequivocal: TPUs are “actually a extremely differentiated a part of what we do… OpenAI, they don’t have these capabilities.”
Considerably, Google presents TPUs not in isolation, however as a part of the broader, extra advanced enterprise AI structure. For technical insiders, it’s understood that top-tier efficiency hinges on integrating more and more specialised expertise breakthroughs. Many updates have been detailed at Subsequent. Vahdat described this as a “supercomputing system,” integrating {hardware} (TPUs, the newest Nvidia GPUs like Blackwell and upcoming Vera Rubin, superior storage like Hyperdisk Exapools, Wherever Cache, and Fast Storage) with a unified software program stack. This software program contains Cluster Director for managing accelerators, Pathways (Gemini’s distributed runtime, now out there to prospects), and bringing optimizations like vLLM to TPUs, permitting simpler workload migration for these beforehand on Nvidia/PyTorch stacks. This built-in system, Vahdat argued, is why Gemini 2.0 Flash achieves 24 instances larger intelligence per greenback, in comparison with GPT-4o.
Google can be extending its bodily infrastructure attain. Cloud WAN makes Google’s low-latency 2-million-mile personal fiber community out there to enterprises, promising as much as 40% quicker efficiency and 40% decrease whole price of possession (TCO) in comparison with customer-managed networks.
Moreover, Google Distributed Cloud (GDC) permits Gemini and Nvidia {hardware} (through a Dell partnership) to run in sovereign, on-premises, and even air-gapped environments – a functionality Nvidia CEO Jensen Huang lauded as “completely gigantic” for bringing state-of-the-art AI to regulated industries and nations. At Subsequent, Huang referred to as Google’s infrastructure the most effective on this planet: “No firm is best at each single layer of computing than Google and Google Cloud,” he mentioned.
Pillar 3: The built-in full stack – connecting the dots
Google’s strategic benefit grows when contemplating how these fashions and infrastructure parts are woven right into a cohesive platform. Not like opponents, which regularly depend on partnerships to bridge gaps, Google controls practically each layer, enabling tighter integration and quicker innovation cycles.
So why does this integration matter, if a competitor like Microsoft can merely companion with OpenAI to match infrastructure breadth with LLM mannequin prowess? The Googlers I talked with mentioned it makes an enormous distinction, they usually got here up with anecdotes to again it up.
Take the numerous enchancment of Google’s enterprise database BigQuery. The database now gives a data graph that permits LLMs to look over knowledge rather more effectively, and it now boasts greater than 5 instances the purchasers of opponents like Snowflake and Databricks, VentureBeat reported yesterday. Yasmeen Ahmad, Head of Product for Knowledge Analytics at Google Cloud, mentioned the huge enhancements have been solely potential as a result of Google’s knowledge groups have been working intently with the DeepMind staff. They labored by use instances that have been arduous to resolve, and this led to the database offering 50 p.c extra accuracy based mostly on widespread queries, at the least in line with Google’s inside testing, in attending to the correct knowledge than the closest opponents, Ahmad informed VentureBeat in an interview. Ahmad mentioned this form of deep integration throughout the stack is how Google has “leapfrogged” the {industry}.
This inside cohesion contrasts sharply with the “frenemies” dynamic at Microsoft. Whereas Microsoft companions with OpenAI to distribute its fashions on the Azure cloud, Microsoft can be constructing its personal fashions. Mat Velloso, the Google government who now leads the AI developer program, left Microsoft after getting annoyed making an attempt to align Home windows Copilot plans with OpenAI’s mannequin choices. “How do you share your product plans with one other firm that’s really competing with you… The entire thing is a contradiction,” he recalled. “Right here I sit aspect by aspect with the people who find themselves constructing the fashions.”
This integration speaks to what Google leaders see as their core benefit: its distinctive capability to attach deep experience throughout the complete spectrum, from foundational analysis and mannequin constructing to “planet-scale” utility deployment and infrastructure design.
Vertex AI serves because the central nervous system for Google’s enterprise AI efforts. And the mixing goes past simply Google’s personal choices. Vertex’s Mannequin Backyard gives over 200 curated fashions, together with Google’s, Meta’s Llama 4, and quite a few open-source choices. Vertex gives instruments for tuning, analysis (together with AI-powered Evals, which Grannis highlighted as a key accelerator), deployment, and monitoring. Its grounding capabilities leverage inside AI-ready databases alongside compatibility with exterior vector databases. Add to that Google’s new choices to floor fashions with Google Search, the world’s finest search engine.
Integration extends to Google Workspace. New options introduced at Subsequent ’25, like “Assist Me Analyze” in Sheets (sure, Sheets now has an “=AI” formulation), Audio Overviews in Docs and Workspace Flows, additional embed Gemini’s capabilities into day by day workflows, creating a robust suggestions loop for Google to make use of to enhance the expertise.
Whereas driving its built-in stack, Google additionally champions openness the place it serves the ecosystem. Having pushed Kubernetes adoption, it’s now selling JAX for AI frameworks and now open protocols for agent communication (A2A) alongside assist for current requirements (MCP). Google can be providing tons of of connectors to exterior platforms from inside Agentspace, which is Google’s new unified interface for workers to seek out and use brokers. This hub idea is compelling. The keynote demonstration of Agentspace (beginning at 51:40) illustrates this. Google gives customers pre-built brokers, or workers or builders can construct their very own utilizing no-code AI capabilities. Or they’ll pull in brokers from the surface through A2A connectors. It integrates into the Chrome browser for seamless entry.
Pillar 4: Deal with enterprise worth and the agent ecosystem
Maybe essentially the most important shift is Google’s sharpened deal with fixing concrete enterprise issues, significantly by the lens of AI brokers. Thomas Kurian, Google Cloud CEO, outlined three causes prospects select Google: the AI-optimized platform, the open multi-cloud method permitting connection to current IT, and the enterprise-ready deal with safety, sovereignty, and compliance.
Brokers are key to this technique. Other than AgentSpace, this additionally contains:
Constructing Blocks: The open-source Agent Improvement Package (ADK), introduced at Subsequent, has already seen important curiosity from builders. The ADK simplifies creating multi-agent methods, whereas the proposed Agent2Agent (A2A) protocol goals to make sure interoperability, permitting brokers constructed with completely different instruments (Gemini ADK, LangGraph, CrewAI, and so forth.) to collaborate. Google’s Grannis mentioned that A2A anticipates the dimensions and safety challenges of a future with probably tons of of hundreds of interacting brokers.
This A2A protocol is admittedly vital. In a background interview with VentureBeat this week, the CISO of a serious US retailer, who requested anonymity due to the sensitivity round safety points. However they mentioned the A2A protocol was useful as a result of the retailer is on the lookout for an answer to differentiate between actual folks and bots who’re utilizing brokers to purchase merchandise. This retailer needs to keep away from promoting to scalper bots, and with A2A, it’s simpler to barter with brokers to confirm their proprietor identities.
Objective-built Brokers: Google showcased professional brokers built-in into Agentspace (like NotebookLM, Concept Technology, Deep Analysis) and highlighted 5 key classes gaining traction: Buyer Brokers (powering instruments like Reddit Solutions, Verizon’s assist assistant, Wendy’s drive-thru), Inventive Brokers (utilized by WPP, Brandtech, Sphere), Knowledge Brokers (driving insights at Mattel, Spotify, Bayer), Coding Brokers (Gemini Code Help), and Safety Brokers (built-in into the brand new Google Unified Safety platform).
This complete agent technique seems to be resonating. Conversations with executives at three different massive enterprises this previous week, additionally talking anonymously as a consequence of aggressive sensitivities, echoed this enthusiasm for Google’s agent technique. Google Cloud COO Francis DeSouza confirmed in an interview: “Each dialog contains AI. Particularly, each dialog contains brokers.”
Kevin Laughridge, an government at Deloitte, an enormous consumer of Google’s AI merchandise, and a distributor of them to different corporations, described the agent market as a “land seize” the place Google’s early strikes with protocols and its built-in platform supply important benefits. “Whoever is getting out first and getting essentially the most brokers that really ship worth – is who’s going to win on this race,” Laughridge mentioned in an interview. He mentioned Google’s progress was “astonishing,” noting that customized brokers Deloitte constructed only a yr in the past might now be replicated “out of the field” utilizing Agentspace. Deloitte itself is constructing 100 brokers on the platform, concentrating on mid-office capabilities like finance, danger and engineering, he mentioned.
The shopper proof factors are mounting. At Subsequent, Google cited “500 plus prospects in manufacturing” with generative AI, up from simply “dozens of prototypes” a yr in the past. If Microsoft was perceived as means forward a yr in the past, that doesn’t appear so clearly the case anymore. Given the PR warfare from all sides, it’s troublesome to say who is admittedly profitable proper now definitively. Metrics fluctuate. Google’s 500 quantity isn’t straight akin to the 400 case research Microsoft promotes (and Microsoft, in response, informed VentureBeat at press time that it plans to replace this public depend to 600 shortly, underscoring the extraordinary advertising and marketing). And if Google’s distribution of AI by its apps is critical, Microsoft’s Copilot distribution by its 365 providing is equally spectacular. Each are actually hitting tens of millions of builders by APIs.
[Editor’s note: Understanding how enterprises are navigating this ‘agent land grab,’ and successfully deploying these complex AI solutions, will be central to the discussions at VentureBeat’s Transform event this June 24-25 in San Francisco.]
However examples abound of Google’s traction:
- Wendy’s: Deployed an AI drive-thru system to hundreds of areas in only one yr, bettering worker expertise and order accuracy. Google Cloud CTO Will Grannis famous that the AI system is able to understanding slang and filtering out background noise, considerably decreasing the stress of reside buyer interactions. That frees up workers to deal with meals prep and high quality — a shift Grannis referred to as “an important instance of AI streamlining real-world operations.”
- Salesforce: Introduced a serious growth, enabling its platform to run on Google Cloud for the primary time (past AWS), citing Google’s capability to assist them “innovate and optimize.”
- Honeywell & Intuit: Corporations beforehand strongly related to Microsoft and AWS, respectively, now partnering with Google Cloud on AI initiatives.
- Main Banks (Deutsche Financial institution, Wells Fargo): Leveraging brokers and Gemini for analysis, evaluation, and modernizing customer support.
- Retailers (Walmart, Mercado Libre, Lowe’s): Utilizing search, brokers, and knowledge platforms.
This enterprise traction fuels Google Cloud’s general progress, which has outpaced AWS and Azure for the final three quarters. Google Cloud reached a $44 billion annualized run charge in 2024, up from simply $5 billion in 2018.
Navigating the aggressive waters
Google’s ascent doesn’t imply opponents are standing nonetheless. OpenAI’s speedy releases this week of GPT-4.1 (centered on coding and lengthy context) and the o-series (multimodal reasoning, device use) show OpenAI’s continued innovation. Furthermore, OpenAI’s new picture technology function replace in GPT-4o fueled large progress over simply the final month, serving to ChatGPT attain 800 million customers. Microsoft continues to leverage its huge enterprise footprint and OpenAI partnership, whereas Anthropic stays a powerful contender, significantly in coding and safety-conscious purposes.
Nevertheless, it’s indeniable that Google’s narrative has improved remarkably. Only a yr in the past, Google was considered as a stodgy, halting, blundering competitor that maybe was about to blow its likelihood at main AI in any respect. As an alternative, its distinctive, built-in stack and company steadfastness has revealed one thing else: Google possesses world-class capabilities throughout your entire spectrum – from chip design (TPUs) and international infrastructure to foundational mannequin analysis (DeepMind), utility improvement (Workspace, Search, YouTube), and enterprise cloud companies (Vertex AI, BigQuery, Agentspace). “We’re the one hyperscaler that’s within the foundational mannequin dialog,” deSouza said flatly. This end-to-end possession permits for optimizations (like “intelligence per greenback”) and integration depth that partnership-reliant fashions wrestle to match. Opponents usually have to sew collectively disparate items, probably creating friction or limiting innovation velocity.
Google’s second is now
Whereas the AI race stays dynamic, Google has assembled all these items on the exact second the market calls for them. As Deloitte’s Laughridge put it, Google hit a degree the place its capabilities aligned completely “the place the market demanded it.” For those who have been ready for Google to show itself in enterprise AI, you’ll have missed the second — it already has. The corporate that invented lots of the core applied sciences powering this revolution seems to have lastly caught up – and greater than that, it’s now setting the tempo that opponents have to match.
Within the video beneath, recorded proper after Subsequent, AI professional Sam Witteveen and I break down the present panorama and rising tendencies, and why Google’s AI ecosystem feels so sturdy:
