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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 surprising 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, targeted 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 operating 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 growth.
Now, with its built-in stack firing on all cylinders, Google seems positioned to steer the subsequent section 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 battle to duplicate.
The shadow of doubt: acknowledging the current previous
It’s unattainable to understand the present momentum with out acknowledging the current previous. Google was the birthplace of the Transformer structure, which sparked the fashionable revolution in giant 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 enjoying protection.
OpenAI’s ChatGPT captured the general public creativeness and enterprise curiosity at breathtaking pace 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 inner forms 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 individuals asking him. The query lingered: May Google translate its plain analysis brilliance and infrastructure scale into enterprise AI dominance?
The pivot: a aware determination to steer
Behind the scenes, nonetheless, a shift was underway, catalyzed by a aware determination 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. “Once 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, inner 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 targeted mission. Logan Kilpatrick, as an illustration, returned to Google from OpenAI, drawn by the chance to construct foundational AI inside 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 workforce was me on day one… we really haven’t any customers on this platform, we have now no income. Nobody is desirous about Gemini at this second,” Kilpatrick recalled of the start line. Folks aware of the interior 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 know-how and a sharpened enterprise focus total, 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 pondering fashions
It’s true {that a} main enterprise mantra has develop into “it’s not simply in regards to the mannequin.” In any case, the efficiency hole between main fashions has narrowed dramatically, and tech insiders acknowledge that true intelligence is coming from know-how 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 crucial feat – and a strong validator, an indication that the model-owning firm has issues like superior analysis and probably the most environment friendly underlying know-how structure. With the discharge of Gemini 2.5 Professional simply weeks earlier than Subsequent ’25, Google definitively seized that mantle. It shortly topped the impartial Chatbot Area leaderboard, considerably outperforming even OpenAI’s newest GPT-4o variant, and aced notoriously tough reasoning benchmarks like Humanity’s Final Examination. As Pichai acknowledged 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 % improve in Gemini utilization inside a month, he Tweeted individually.
For the primary time, Google’s Gemini demand was on hearth. As I detailed beforehand, apart from Gemini 2.5 Professional’s uncooked intelligence, what impressed me is its demonstrable reasoning. Google has engineered a “pondering” 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 essential 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 checks, the corporate discovered it “stepped up fairly a bit” and was now “fairly comparable” to Anthropic’s Claude 3.7 Sonnet, beforehand the popular alternative for a lot of builders.
Google additionally added an enormous 1 million token context window to the mannequin, enabling reasoning throughout whole codebases or prolonged documentation, far exceeding the capabilities of the fashions of OpenAI or Anthropic. (OpenAI responded this week with fashions that includes equally giant context home windows, although benchmarks recommend Gemini 2.5 Professional retains an edge in total 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 “pondering” mannequin, Flash is optimized for low latency and cost-efficiency. You may management how a lot the mannequin causes and stability 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 provide the very best worth. “If we had this dialog one yr in the past… I might don’t have anything to point out,” Velloso admitted, highlighting the speedy turnaround. “And now, like, throughout the board, we’re, for those who’re on the lookout for no matter mannequin, no matter dimension, like, for those who’re not Google, you’re shedding cash.” These charts have been up to date to account for OpenAI’s newest mannequin releases this week. See beneath:
For any given value, Google’s fashions provide extra intelligence than 90 % of the workforce. Supply: Pierre Bongrand.
Wayfair’s Tan mentioned she additionally noticed promising latency enhancements with 2.5 Professional: “Gemini 2.5 got here again sooner,” making it viable for “extra customer-facing kind of capabilities,” she mentioned, one thing she mentioned hasn’t been the case earlier than with different fashions. Gemini might develop 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 by way of 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 need to companion with different corporations to do that.
Pillar 2: Infrastructure prowess – the engine below the hood
The flexibility to quickly iterate and effectively serve these highly effective fashions stems from Google’s arguably unparalleled infrastructure, honed over a long time of operating planet-scale providers. 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 “pondering fashions.” The size 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 Programs 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 acknowledged that Ironwood affords 2x perf/watt relative to Trillium, the earlier technology of TPU. That is important since enterprise prospects more and more say power 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 know-how.
Google’s place fueled this sustained TPU funding. It must effectively energy large providers like Search, YouTube, and Gmail for greater than 2 billion customers. This necessitated growing customized, optimized {hardware} lengthy earlier than the present generative AI growth. Whereas Meta operates at an identical shopper scale, different opponents lacked this particular inner driver for decade-long, vertically built-in AI {hardware} growth.
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 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 lower 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 thus far, 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.
If you ask Google executives the place their fundamental know-how benefit in AI comes from, they normally fall again to the TPU as an important. John 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 know-how breakthroughs. Many updates have been detailed at Subsequent. Vahdat described this as a “supercomputing system,” integrating {hardware} (TPUs, the most recent 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 obtainable 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 greater intelligence per greenback, in comparison with GPT-4o.
Google can also be extending its bodily infrastructure attain. Cloud WAN makes Google’s low-latency 2-million-mile personal fiber community obtainable to enterprises, promising as much as 40% sooner 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} (by way of a Dell partnership) to run in sovereign, on-premises, and even air-gapped environments – a functionality Nvidia CEO Jensen Huang lauded as “totally gigantic” for bringing state-of-the-art AI to regulated industries and nations. At Subsequent, Huang referred to as Google’s infrastructure the very best on this planet: “No firm is healthier 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 elements are woven right into a cohesive platform. In contrast to opponents, which regularly depend on partnerships to bridge gaps, Google controls almost each layer, enabling tighter integration and sooner 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 affords a data graph that enables LLMs to look over knowledge way 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 doable as a result of Google’s knowledge groups have been working intently with the DeepMind workforce. They labored by way of use circumstances that have been arduous to unravel, and this led to the database offering 50 % extra accuracy based mostly on frequent queries, a minimum of in line with Google’s inner testing, in attending to the fitting knowledge than the closest opponents, Ahmad informed VentureBeat in an interview. Ahmad mentioned this kind of deep integration throughout the stack is how Google has “leapfrogged” the {industry}.
This inner cohesion contrasts sharply with the “frenemies” dynamic at Microsoft. Whereas Microsoft companions with OpenAI to distribute its fashions on the Azure cloud, Microsoft can also be constructing its personal fashions. Mat Velloso, the Google govt 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 affords 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 inner 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” components), 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 also 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 affords customers pre-built brokers, or staff or builders can construct their very own utilizing no-code AI capabilities. Or they’ll pull in brokers from the surface by way of A2A connectors. It integrates into the Chrome browser for seamless entry.
Pillar 4: Deal with enterprise worth and the agent ecosystem
Maybe probably the most important shift is Google’s sharpened give attention to fixing concrete enterprise issues, notably by way of 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 give attention to safety, sovereignty, and compliance.
Brokers are key to this technique. Apart from AgentSpace, this additionally contains:
Constructing Blocks: The open-source Agent Improvement Equipment (ADK), introduced at Subsequent, has already seen important curiosity from builders. The ADK simplifies creating multi-agent programs, whereas the proposed Agent2Agent (A2A) protocol goals to make sure interoperability, permitting brokers constructed with completely different instruments (Gemini ADK, LangGraph, CrewAI, and so on.) to collaborate. Google’s Grannis mentioned that A2A anticipates the size and safety challenges of a future with probably tons of of hundreds of interacting brokers.
This A2A protocol is basically essential. In a background interview with VentureBeat this week, the CISO of a significant 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 tell apart between actual individuals and bots who’re utilizing brokers to purchase merchandise. This retailer desires to keep away from promoting to scalper bots, and with A2A, it’s simpler to barter with brokers to confirm their proprietor identities.
Goal-built Brokers: Google showcased knowledgeable brokers built-in into Agentspace (like NotebookLM, Thought 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 giant enterprises this previous week, additionally talking anonymously because 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 govt at Deloitte, a giant person 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 provide important benefits. “Whoever is getting out first and getting probably 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 features like finance, danger and engineering, he mentioned.
The client 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 manner forward a yr in the past, that doesn’t appear so clearly the case anymore. Given the PR conflict from all sides, it’s tough to say who is basically 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 rely to 600 shortly, underscoring the extreme advertising and marketing). And if Google’s distribution of AI by way of its apps is important, Microsoft’s Copilot distribution by way of its 365 providing is equally spectacular. Each are actually hitting hundreds of thousands of builders by way of 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 places in only one yr, enhancing 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 employees to give attention to meals prep and high quality — a shift Grannis referred to as “a fantastic instance of AI streamlining real-world operations.”
- Salesforce: Introduced a significant 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 total progress, which has outpaced AWS and Azure for the final three quarters. Google Cloud reached a $44 billion annualized run price 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 (targeted on coding and lengthy context) and the o-series (multimodal reasoning, instrument use) exhibit 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 robust contender, notably 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 seen as a stodgy, halting, blundering competitor that maybe was about to blow its probability at main AI in any respect. As a substitute, its distinctive, built-in stack and company steadfastness has revealed one thing else: Google possesses world-class capabilities throughout the complete spectrum – from chip design (TPUs) and world infrastructure to foundational mannequin analysis (DeepMind), utility growth (Workspace, Search, YouTube), and enterprise cloud providers (Vertex AI, BigQuery, Agentspace). “We’re the one hyperscaler that’s within the foundational mannequin dialog,” deSouza acknowledged flatly. This end-to-end possession permits for optimizations (like “intelligence per greenback”) and integration depth that partnership-reliant fashions battle to match. Opponents typically must sew collectively disparate items, probably creating friction or limiting innovation pace.
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.” If you happen to have been ready for Google to show itself in enterprise AI, you could have missed the second — it already has. The corporate that invented most 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 must match.
Within the video beneath, recorded proper after Subsequent, AI knowledgeable Sam Witteveen and I break down the present panorama and rising tendencies, and why Google’s AI ecosystem feels so robust:
