Enterprises are rapidly moving from an artificial intelligence that answers questions and generates content to one that performs tasks and takes actions. According to Google Cloud Chief Executive Thomas Kurian, this shift requires a fundamentally different approach to infrastructure and software. Google’s view is that only a tightly integrated portfolio – spanning silicon to applications and everything in between – can effectively support this transition.
A linchpin of this transition is the emergent data and AI platform, what we call the system of intelligence and what Google is initially exposing as its Knowledge Catalog. This capability ultimately abstracts and harmonizes analytics and operational applications. In addition, we see an evolving system of agency – what Google calls a system of action, comprising the Knowledge Catalog and the Agent Platform.
We think measurable business value will ride on top of this infrastructure and that is where the real battle lines will be drawn. Specifically, we see frontier model vendors, of which Google is one, rapidly building out capabilities that will become fundamental to the future of software – which we predict will be the biggest transformation in the history of the software industry.
In this Breaking Analysis, we contextualize the announcements and news from Google Cloud Next 2026 using the framework we’ve been iterating on for the three years, architected by George Gilbert.
The kickoff this week was Google’s big TPU 8 announcement – 8t and 8i – with the Acquired guys hosting. It was positioned as the next big step in Google’s silicon roadmap and part of a broader message that Google wants to be seen as the only hyperscaler with a frontier model, a differentiated data stack and a credible path to delivering agents at scale. There was also a bit of semantic gymnastics around whether a tensor processing unit is an application-specific integrated circuit – Google said “it’s not an ASIC,” rather a more general-purpose chip.
Call it what you want – it is specialized silicon built to run modern AI efficiently, and it is central to Google’s argument that economics and performance are going to matter more than ever. Surprising to us at the TPU pre-announcement was, while there were plenty of “2X, 3X, 9.8X” claims, there was virtually no mention of metrics around performance per watt, arguably the most important measure for operators that are energy-constrained.
TPUs are both impressive and critical to Google’s strategy. Our view, however, is that Nvidia Corp. remains a crucial partner of Google’s (and of other hyperscalers), irrespective of their in-house silicon efforts. In other words, we don’t see TPU as directly competitive to Nvidia, rather we see it as a capability that gives Google differentiable advantage via its ability to integrate hardware and software tightly. It also allows Google to best manage the gap between accelerator demand and supply. Regardless, access to Nvidia’s CUDA ecosystem is fundamental in providing optionality to developers and being able to provide access to the world’s largest and most important AI ecosystem.
The bigger setup in this Breaking Analysis is that Google has been doing yeoman’s work for years in the data platform layer. It’s not just BigQuery. It’s the metadata layer on top, plus integration with the operational database Spanner. In our view, Google is the only hyperscaler that has been meaningfully competitive with Snowflake and Databricks as a data platform – and that work has been a long build toward what’s now showing up as an agent platform story.
This is where the conversation starts to get really interesting. For decades, the industry built silos – analytic data silos and operational application silos. Agents that transform a business – agents that can perceive, reason, decide, act and learn – don’t unlock much value if they’re siloed. You end up with automation, but you don’t change how the business works.
This is the context for the premise Kurian put forward this week. The industry has moved past retrieval-augmented generation-based chatbots – request and receive an answer – and into a world where agents, and teams of agents, act on behalf of humans and take action. That shift pulls a new set of infrastructure requirements into the critical path. It has to be integrated.
Google’s positioning is that it believes it is the only “full stack” hyperscaler that can bring the pieces together – silicon, infrastructure, data, models, applications and services – into a coherent system for agentic workloads. At the same time, many frontier model vendors don’t have a cloud platform, which creates a structural advantage for Google as it tries to turn model capability into enterprise deployment. Kurian (and other executives) presented the stack slide below (and variants), throughout the conference to accentuate this point.
Takeaway: TPU 8 is the headline, but the more important story is Google’s attempt to connect its silicon, data platform and frontier model posture into an integrated agent platform narrative.
In this section we revisit our framework for how the software industry is changing. Later in this research note, we map Google’s model and try to reconcile how it fits into our vision of the future.
The core idea in the slide below is, in our research, we believe the entire software industry model is changing, moving from software-as-a-service to “service-as-software.” When the industry moved from on-premises to SaaS, everything changed – the technology model, the business model and the operating model. Vendors stopped shipping software and started operating it. Value delivery became continuous – and the organization had to be built around that reality.
The shift underway now is broader. SaaS re-architecture primarily changed software companies and the information technology function that consumed them. Service-as-software changes the entire enterprise. Any company can scale with less labor by embedding intelligence into workflows and delivering outcomes through software. Over time, that pushes more businesses toward platform economics – and the markets that reward platforms tend toward winner-take-most, with software-like marginal economics conferring competitive advantage to firms that lean into AI.
Agents are the catalyst. Agents don’t unlock much value if they live inside silos. They add real value when they change business outcomes end-to-end. That’s the point of the middle part of the slide above – the system of intelligence or SoI. Google introduced its Knowledge Catalog, which begins to unlock some of the capabilities of the SoI that we’ve previously described. The point is broader, however, is that this layer connects agents to the operational and analytic reality of the enterprise so they can perceive, reason, decide, act and learn across the business, not inside a single department.
The main constraint we’re trying to resolve is shown at the bottom of the slide. For 60 years, enterprises built silos – analytic data silos, operational application silos and then the organizational silos that formed around them. Each department ends up with its own applications and its own data stores. That structure is not designed for agents that need cross-functional context and permissions to drive outcomes such as “compress the hire-to-onboard cycle,” “reduce quote-to-cash friction” or “cut days out of incident response” — and do so with human language prompts that reimagine the entire workflow, rather than “pave the cowpath” by automating existing processes.
The slide below is the maturity path from departmental reporting to something that starts to resemble a digital twin of the enterprise – the real time digital representation of an organization. When we discuss this concept, we often point out that historically in the data business, we think in terms of “strings” that databases understand. Here we think differently – in terms of concepts that humans understand, such as people, places, things and activities (for example, processes).
We believe this represents a profound shift in software. The system of intelligence is an emerging layer and perhaps the most valuable piece of real estate in the emerging AI software stack. It can’t sit on top of a pile of disconnected metrics and dashboards — business intelligence infrastructure. Rather, it needs a substrate that models the business in a way agents can use – with enough context, timeliness and consistency – to drive decisions and actions that are trusted and repeatable at scale.
At the left edge of the chart, Level 1 is where most companies started – running reports against siloed operational applications. It’s mostly manual, mostly departmental, and there’s little self-service. Level 2 is where the modern data platform story took off – a BigQuery, Snowflake or Databricks-style approach where teams standardize key metrics and dimensions to feed “cubes” from different applications. That improves self-service, but in practice the organization still behaves like departments with their own data and views of the truth.
Level 3 is the first real step toward modeling the business instead of just reporting on it. Real-time events start flowing from operational systems into the data platform, and the data platform enriches those events in return. The entities and the events start reinforcing each other, and that’s where “context” becomes something you can actually compute, not just describe.
Level 4 and Level 5 move into behavioral modeling and prediction. This is where products like Salesforce Data Cloud or SAP’s data cloud are headed – models of processes derived from their application footprints, with richer behavioral patterns and predictive signals. The important nuance is these don’t have to become walled gardens. Think of them as value-add layers that sit on top of today’s data platforms and increase fidelity and actionability.
The north star is the digital twin – a real-time digital representation of the enterprise that captures people, places, things and activities/processes. That is the prerequisite for layering a system of intelligence on top and expecting agents to do more than automate small tasks.
We now move to the handoff that has to happen if agents are going to take action with confidence. The generative layer gives you creativity – for example, tokens, language, synthesis, exploration. But enterprise action requires determinism – the rules, the guardrails and the auditable trail that says what happened, why it happened and what the system should do (and is allowed to do) next. In our view, this is the core bridge between nondeterministic intelligence on top – deterministic execution underneath – tied together tightly enough that you can trust the outcome.
The simplest way to think about it is goals and guardrails. Agents have goals – what they’re trying to accomplish – and guardrails – what they must do and the rules they must follow. These next stages are the deterministic layers that turn “smart” into “safe and operable” (below).
On this slide, the four stages take the maturity model from “analytics helping humans” to “systems coordinating work”:
One reason this is so important in our view is it answers the “How do you get there?” question that kept coming up. Specifically, the feedback from Geoffrey Moore when we first laid out service-as-software in a serious way – the idea is compelling, but enterprises need specific steps to get from point A to point B. The last two slides – the five stages of data platform maturity and these four deterministic stages – are the work that fills in that bridge. It’s also why, candidly, this has been iterative. We thought the model was “done” in January – then it got deeper, because the deterministic layers are where the hard problems live.
The bottom line: Agents don’t dramatically accelerate value in silos. They compound value when they can navigate enterprise state, take actions under rules, and leave an audit trail. This is the bridge we see between generative output and governed execution.
The slide below is the next step past the deterministic layers. The bottom stack – mapping plus rules – is the part that makes agents safe. It defines what’s allowed, what must be true, and how actions get executed without blowing up the business. But that only covers a slice of how companies actually operate. The bigger chunk is tacit knowledge – the stuff people call “tribal knowledge” – what experts do when the rules conflict, when the data is incomplete, and when the situation is ambiguous.
That’s why the slide above separates the deterministic digital twin (the orange/gold “scaffolding”) from the cognitive digital twin (the blue “crystallization”). The deterministic layer is the governed backbone. The cognitive layer is how the organization captures the “why” behind decisions and learns over time.
The viral “context graph” chatter that hit the VC and vendor community late last year was essentially about this problem. Context is what you reach for when deterministic logic breaks down. The logical workflow is:
The important nuance is you can’t cover the enterprise with deterministic rules alone. In the view laid out above, the rules are necessary, but they’re not the whole game. Most of how a company works lives in judgment calls, conflict resolution, prioritization and experience. That’s the 90% problem.
The “gold standard” example cited here is a company called Mercor Inc. The concept is that even if the implementation is difficult, an expert teaches their thinking process, teaches how to grade the reasoning process, and even teaches what wrong reasoning looks like. That “teach and grade” loop is the only reliable way to capture the why behind expert judgment. Other approaches try to do it cheaper because it’s less onerous on the expert, but they lose fidelity.
A simple way to explain the mechanics is punishment and reward: If the agent says one plus one equals three, it gets penalized; if it says one plus one equals two, it gets rewarded. Over time you get compounding behavior improvement. That’s the flywheel.
The key connection back to the earlier slide is that deterministic rules make tacit knowledge capture easier. When the rules are explicit, you narrow the surface area of ambiguity. Humans don’t have to explain everything, only the exceptions – and the system can learn faster because it knows exactly where the rules stopped being sufficient.
This is relevant for Google because the agent platform conversation is starting to show more maturity. It’s still early, but the steps are becoming clearer – and the deep dives this week at Google Cloud Next 2026 reinforced that the path forward isn’t just more capable models. That’s important but it’s the combination of deterministic scaffolding plus a systematic way to capture and refine expert judgment that represents state-of-the art today.
Bottom line: The deterministic twin makes agents safe. The cognitive twin makes them useful at scale. The compounding comes from tightly integrating the two together so exceptions become training data and expertise turns into an asset.
We believe the most useful way to decode Google’s data and AI announcements is to strip away the product names – “Knowledge Catalog” and “Agent Platform” – and map the underlying capabilities to the layers that we described earlier in this research. Google’s terminology is a little inverted relative to ours. Google tends to talk about “system of intelligence” as the modern data stack and “system of action” as the new agent layer. Our view is different – the system of intelligence is the harmonization layer that makes action safe and repeatable, and it is what ultimately feeds the system of agency.
Understanding the different language and mapping Google’s parlance to ours at the functional level helps to highlight both progress and gaps.
Level 1 – Mapping layer (entities and lineage)
Google is doing real work extracting database management system technical metadata and lineage. It is also pulling unstructured, document-oriented tacit knowledge into a knowledge graph – and we acknowledge that’s advanced. The shortfall is unification. Google can extract entities, but it does not yet unify those entities across systems into a single, authoritative reference. “Customer” shows up in many places. Resolving “customer” across all those systems remains the hard part. At least this is our current understanding of where Google is at.
A practical way to say this, as we said earlier, is that databases store strings – the knowledge graph wants to speak in things people understand. The move from strings to things – and then from things to activities and processes – is where the big value realization happens, and it’s also where the work gets hard.
Level 2 – Rules layer (from dimensional semantics to application semantics)
Google’s catalog captures data quality rules, BI metrics and business glossary content – dimensional semantics. That’s useful, but it stops short of full application semantics – the business process rules that are entangled and entombed inside legacy application silos. This is the layer where Palantir Technologies Inc. often comes up as a reference point for our work – not because it covers everything, but because it shows what “process rules as data” looks like when you go deep. We believe Google wants to cover a lot more ground than Palantir can cover at its pace and within its constrained domain — but that doesn’t make the step any easier. Nor does it mean that Palantir, and its CEO Alex Karp, don’t have ambitions to broaden its scope.
Level 3 – Institutional memory (how vs. why)
Google has the substrate to capture agent reasoning and store it. The unified trace viewer (Trace Explorer inside of Google Cloud Trace) is a real step because it shows how an agent got to an outcome. That is not the same as capturing human expert reasoning – the why – which is what drives judgment and confidence. It’s a nuanced gap, but it’s the difference between replaying a path and learning a decision system that can be trusted.
Level 4 – Decision guidance (context synthesis, confidence still thin)
Google can synthesize context and enable complex multi-retrieval. That allows an agent to retrieve more relevant material and make a judgment. The missing piece is confident, scored guidance from the system of intelligence itself – the ability to say “here’s what to do and here’s our confidence score,” grounded in a library of human-grade “why” and in process-aware semantics. Without the “why,” the system can feel closer to static institutional memory than decision guidance.
On the system-of-agency side, the key requirement is the learning loop – every layer needs feedback. Agents do work, get scored, get reinforced, and then you accelerate value. This is where Google’s agent evaluation and optimization work is important.
We heard a consistent theme from customers in that there is real excitement, but it’s still early for most of them when you push past Kurian’s keynote narrative – “in the past year we didn’t just see adoption, we saw transformation.” Skilled employees are being redeployed toward building, deploying and managing agents – that’s where a lot of the near-term “productivity gain” is going. In other words, the productivity story is increasingly coming from the work of creating agentic capability, not just consuming it. So in that sense Kurian’s proclamation holds water. But this is still an elusive reality for the vast majority of enterprises.
This also explains why Palantir keeps coming up in these conversations. Palantir’s forward deployed engineers or FDEs effectively did this work for customers – building the deterministic foundation (mapping plus rules/ontology) and then layering action on top of it. That deterministic foundation is what makes action safe. The open question is how quickly the broader market can build that foundation without needing an army of specialists – and how quickly Google can industrialize the “why,” not just the “how,” so agents can act with confidence.
Bottom line – Google is putting credible pieces on the chess board – for example, lineage + metadata extraction, a graph-oriented approach to unstructured knowledge, multi-turn agent evaluation and failure clustering via optimizer. But gaps exist – unifying entities across systems, moving from dimensional semantics into real process semantics, and capturing the human “why” so decision guidance becomes confident, scored and repeatable.
This is work that remains.
The agent platform discussion has centered around coding. The market is converging on the increasingly obvious fact that to build a universal knowledge-work agent, you start with the coding agent, because the way agents interact with the world is through tools – and tool use increasingly means writing code to call those tools. That’s why Anthropic leaned into coding first and why the coding stack is now the battleground across the frontier model vendors.
We see the competitive pressure showing up in many places. Anthropic’s Claude Code is gaining massive traction, OpenAI is pushing Codex, Grok has to have a credible coding agent capability to be competitive as a frontier model, and Google is taking a different route by building an enterprise agent platform that tries to turn the “harness” into something broader. The news around SpaceX Corp. owner Elon Musk having an option to buy Cursor underscores the point: If you don’t have a first-class coding agent story, you fall behind quickly.
In the simplest terms, the progression on the slide above is:
Anthropic’s early advantage is that “coding first” path is the fastest way into general-purpose agent behavior. The harness gives developers a high-control environment – user interface/command-line interface, orchestration, tool use, context management – and it becomes the training ground for what later turns into broader knowledge-work agents. Their Conway work is the logical next step – an attempt to assemble a fuller platform on top of MCP, with proprietary extensions and enterprise features that go beyond pure coding.
Google’s push is different. It’s trying to build the enterprise control plane for agents – and it has some things the frontier labs can’t easily deliver on their own, at least not yet, even if the frontier labs carve off pieces of the platform experience and live inside enterprise stacks.
Three practical differences stand out to us:
Net-net, we believe the frontier labs are going to keep pushing hard on coding agents because it is the fastest path to credible tool use. Google’s advantage comes from turning that into an enterprise-ready platform layer – governance, agent identity, intent-based policy and shared memory – so customers don’t end up with a new generation of silos and lock-in disguised as “agent progress.” Work remains for Google and others, but the direction is becoming clearer.
Google is betting the farm on an integrated stack – TPU, frontier model, data platform, agent platform and applications as one cohesive system. That’s Google’s differentiation. In our view, it’s directionally right, and it also pulls Google into new territory where the winners will be decided by adoption, operating leverage and the ability to turn “context” into “action” without breaking security boundaries. We see Google as having a strong technical story here, but its vision statement to businesses could be framed in more “wallet” than technical terms.
This brings an interesting question for the likes of Snowflake and Databricks. Those two are climbing “up” from the read path – give me context from the data platform – and then stretching toward richer context. The question is: What happens once they’ve read and reasoned, and how do they operationalize the decision the agent makes? That’s where action becomes the differentiator, and it’s why coding capability matters so much as the starting point for agent maturity. If the agent can’t reliably execute, then all that context is just analysis theater.
Google is moving in the right direction – but it’s also crossing into the arena occupied by Palantir and Celonis SE, and even the integration layer that vendors such as Salesforce Inc.’s MuleSoft helped define. We see this as the hard part. Specifically harmonization across operational systems, data systems and workflows – with enterprise-grade governance. This is where we believe our “system of intelligence” has to emerge as a real product layer – irrespective of what Google calls it.
A few takeaways from this:
Bottom line: Google looks like it’s taking pole position in the agent platform conversation because it can bring the full stack and because its data platform has been doing the yeoman’s work for years. The work that remains is the hard part – turning “read context” into “take action,” building the harmonization layer at enterprise scale, and solving the OEM/perimeter problem so Gemini can be consumed where the data lives, not only where Google runs.
We open and close on the same topic: TPU. The last point is the one that changes the tone of the whole week – Google’s TPU capacity gives it a structural advantage in AI compute, and it will show up in product velocity.
The Epoch AI chart on “H100 equivalent” capacity below suggests Google has more AI compute than any other cloud, driven largely by TPUs – a brute-force approach compared with Nvidia, but one that lets Google control its own destiny on supply and deployment.
This ties to a concept raised on the Latent Space podcast: “GPU-rich” vs. “GPU-poor.” Google looks “GPU-rich,” which helps explain why generative AI and agents are showing up broadly across its product line. Microsoft, by contrast, looks “GPU-poor” in this slide – which helps explain prioritization decisions (Office first), uneven rollout gen AI enablement across its Azure services, and the downstream pressure it has created elsewhere in the stack.
There’s a separate point that needs to be said: None of this is “TPUs are eroding Nvidia’s moat.” The demand for accelerated compute is so large that quality accelerators will sell, in a large part because Nvidia can’t satisfy all demand. Google will use TPUs primarily to power its own services – not as a merchant silicon vendor.
A few takeaways to anchor this analysis:
Net-net, we believe Google’s TPU advantage is less about claiming an “Nvidia alternative” and more about enabling Google’s own stack to move faster – especially at the software layer, where capacity becomes the difference between selective enablement and pervasive agent rollout.
In the next 30 to 60 days, run a structured “agent platform bake-off” with Google, AWS and Microsoft Azure in the mix. Force it into a synthetic production workflow that crosses multiple systems (not a demo). Pick one high-value, end-to-end process, tie it to your actual data and policies, put Apache Iceberg in the mix and score it on the things like security/auditability, semantic consistency across systems, time-to-value and the operational requirements to keep it running.
Your goal should be to answer the following question with proof: Can Google’s integrated stack (data platform agent platform+control plane) become the governed layer your agents run through, or does it remain a strong set of parts that still requires too much custom glue? If it clears that hurdle, we would advise going deeper with Google; if it doesn’t, treat it as tactical capability. Regardless, for now, keep your control-plane strategy vendor-neutral until the business value clarity dramatically outweighs lock-in risk.
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