Breaking Analysis with Dave Vellante

Forecasting the AI Bubble

We get a lot of questions about whether we're in a bubble & if so when will it burst. Many feel that "Bubble" is a pejorative. I see it differently. To me a bubble is an economic event where asset prices & valuations in a new or growing market rise far above their current value. Bubbles are characterized by media hype, investor exuberance, fast/easy money & FOMO. Bubbles do not always burst in a sudden, catastrophic event. Sometimes they have a "soft landing." AI can be technologically transformative and still produce a capital bubble. Those two ideas are not in conflict.The bubble bursting does not require AI to fail. It only requires deployable supply and capital commitments to grow faster than monetizable demand. When productive, revenue-producing AI capacity takes longer to materialize, pricing will normalize and financing will no longer bridge the gap. That’s when the capital cycle possibly resets and a liquidity crunch ensues.

AMD's Next Reinvention — A New Playbook for the AI Era

AMD's first reinvention rebuilt the company. It was frankly about survival. Its next reinvention must redefine it.The company's resurgence over the past decade came from doing what many thought was impossible: rebuilding its CPU franchise, taking meaningful share from Intel, and restoring credibility through disciplined execution.But in our view, AMD's next chapter is fundamentally different.

Forget AGI…The Prize is Enterprise AGI

Breaking Analysis with Dave Vellante and George Gilbert

Snowflake, Databricks and the Model Makers: The Battle for the Agentic Client and AI Backend

Agentic AI is being misread as a series of separate battles - e.g. Snowflake vs. Databricks, copilots vs. agents, model makers vs. app vendors, etc. We think the real story is that the biggest opportunity in software is converging around who owns the new intelligent client and the AI back end that makes it useful. The new client is the agent-based system of engagement - Snowflake’s CoWork & CoCo, Databricks Genie, Microsoft Copilot, Google Gemini Enterprise, ChatGPT/Codex, Claude/Cowork and others. But that client cannot deliver business outcomes without a new back end - what we call a System of Intelligence - that represents a model of the enterprise in terms of its business rules and tacit knowledge. You can’t build one without the other. We frame this premise using Clay Christensen’s integrated innovation and Jensen’s extreme co-design as applied to enterprise software.That is why Snowflake is the focal point for this Breaking Analysis, but not the whole story. Snowflake is not just competing with Databricks anymore. It is now in the same strategic arena as Microsoft, Google, OpenAI, Anthropic, Salesforce, SAP, ServiceNow, Celonis and others - all trying to define where business users, builders and agents get work done, and where the enterprise context that powers that work gets built.

Personal Agents Light the Fuse In the Age of Data Intelligence

Breaking Analysis with Dave Vellante and George Gilbert

How AI Stacks are Rewriting the Rules of Business

Nvidia, AI factories and the transition to accelerated computing

Breaking Analysis with Dave Vellante

Google’s Agent Platform Takes Pole Position but Work Remains

Breaking Analysis with Dave Vellante and George Gilbert

As AI Powers Google, What’s Next for Google Cloud

The agentic era is forcing a reset in enterprise architecture. Agents taking action go far beyond just analyzing data living in lakehouses. Agents acting on behalf of humans, continuously, at machine scale bring new architectural requirements to the enterprise. The so-called “modern data stack” as most organizations know it, has become a sort of “new legacy.” No longer can organizations rely on stitched-together systems, fragmented governance, batch pipelines, and historical security boundaries. As we move from human-scale dashboards to agent-scale execution, fragmentation becomes an operational and compliance risk.This is where we believe Google has an underappreciated advantage. Our research indicates the winning architectures in the agentic era will be the ones that operate as a coherent, end-to-end system — where the model, the cognitive engine, and the infrastructure are tightly integrated and share a single trusted boundary, consistent security controls, and an efficient cost structure that can generate tokens in volume but doesn’t collapse under thousands of agent interactions per minute. This is the premise behind our Google thesis. We believe Google is in a strong position to build on decades of infrastructure and data excellence and push toward an AI powered cloud that goes beyond a reactive system of intelligence to one that takes action at scale. Essentially we see Google as one of the companies best positioned to execute on our vision of delivering a real-time digital representation of an enterprise. One that blends the power of generative AI with trusted and consistent determinism to deliver real time actions that leverage both structured and unstructured and can execute transactions as scale.

The Agentic Gap: Vendors Sprint, Enterprises Crawl

Geopolitical dislocations are ripping through the stock market and are filtering down to IT budgets in the form of increased uncertainty. It seems that every quarter of budget optimism is followed with some external event that causes organizations to tighten their belts. Specifically, we’ve seen the increased momentum in January CIO sentiment on spending, pull back as war, oil prices, the threat of inflation and even the prospect of Fed tightening now loom larger. While big tech players continue to spend massively on CAPEX, and the genuine enthusiasm from this month’s Nvidia GTC and RSAC events is still being felt, mainstream enterprises are once again expressing caution in their spending intentions. In addition to economic and world affairs, AI success still eludes most mainstream organizations. Our observation is the tech industry is in the third inning of the AI wave, which started in earnest mid last decade with Deep Mind and other significant research milestones that led to the ChatGPT and subsequent moments like Claude Code and OpenClaw. Yet organizations are still in the first inning. The data suggests that while virtually all firms are leaning into AI, those realizing ROI at scale remain the minority. While leading thinkers like Jensen Huang advise not focusing on ROI and letting innovation flourish irrespective of hard dollar returns, the reality is in the land of enterprise customers, tangible returns and risk management remain key governors of spending.