Back in May, we argued in The Agentic AI Storage Shock that agents would turn cold enterprise data into warm operating data, and that the enterprise stack was underbuilt for the shift. That report remains one of our most read on this platform. Five months of tracking new information and many hours of meetings with CIO/CTOs and enterprise software executives have sharpened that view and, to be fair, corrected part of it. We had been noting how agentic AI is causing a wholesale rethink of all infrastructure and all software and the data side of this equation remains one of the least talked about externally. Our conviction holds the key constraint for enterprise agents useful actions is access to data, and the companies that own the systems of record have started to charge for it.
Salesforce reported Agentforce ARR above $1.5 billion in its fiscal second quarter, up more than 240% year over year, and it prices an Agentforce action at $0.10 in Flex Credits. In the same quarter, 82 trillion of the 104 trillion records flowing into its Data 360 platform arrived through zero copy, up 731% from a year earlier. ServiceNow said its AI annual contract value passed $1 billion, with a $1.5 billion target for year-end. Vendors are doing two things at once, metering what agents do inside their systems while opening read access to data that lives outside them.
We rebuilt our sizing around this added dynamic. On our base case, recurring enterprise spend on agent data services grows from roughly $3 billion to $4 billion in 2026 to roughly $25 billion to $37 billion in 2030, with our base case near $30 billion. Metered access paid to systems-of-record vendors is about half of that spend today and about 30% by 2030, growing more than fivefold in dollars while losing share to retrieval, memory and write-back services. The larger bill comes first, because one-time spending to make enterprise data agent-ready runs at roughly $20 billion to $40 billion this year and $65 billion to $145 billion by 2030, still about three times the recurring spend in that year on our base case.
The correction (update) to our May piece concerns bytes. When we built the enterprise side from the bottom up, the data agents leave behind in a company turned out to be small: the indexes they search, their working memory and the records and work product they create add up to a little over 2 exabytes across all enterprises by 2030 in our base case scenario. That figure rests on assumptions we have not yet been able to measure, chief among them how much output agents generate and how long enterprises keep it, and both will rise as more autonomous agents get write access. Stressing them together, with ten times the work product, seven-year retention of fuller agent traces, and three times the index footprint and agent memory, takes the total to roughly 13 exabytes. That is a real increase, but it is still less than a tenth of the roughly 180 exabytes of flash our memory model dedicates to inference context, where long conversations and agent sessions spill out of memory onto flash in the data centers running the models. So the storage shock is real, and it lands wherever inference runs: mostly in hyperscale and neocloud fleets, but also in the enterprises that run inference on their own infrastructure. For the enterprise CIO, the bill is for access, governance and readiness, and the question for investors is who gets to collect the toll.
For subscribers
The full report lays out the framework we now use for this market. Every enterprise reaches agent-ready data by one of three roads: it pays the system-of-record vendor’s toll, it federates and queries data in place, or it copies data out into a lakehouse it controls. The mix of those roads decides who captures the spend far more than it decides the size of the spend. We size all three roads, show where read tolls are eroding and write tolls are holding, and map the vendors across the Connect, Manage and Use layers.
Subscribers also get the capture math by scenario. Systems-of-record vendors keep anywhere from 18% to 45% of recurring agent data spend in 2030 depending on the road mix, a range we think investors have not yet modeled. The report closes with the gross-versus-net budget view, the indicators we are tracking quarter by quarter, and what would change our view. Eight exhibits and the model behind them are included.
Inside the full report:
The three roads to warm data, and why the road mix decides who gets paid
Read tolls versus write tolls across Salesforce, ServiceNow, Microsoft, SAP and Workday
The readiness bill: roughly $18 billion to $40 billion this year, built per functional area and checked against the services market
Connect, Manage, Use: where $25 billion to $37 billion of recurring spend lands by 2030
Capture by scenario: why systems of record keep anywhere from 18% to 45%
What it means for Salesforce, ServiceNow, Snowflake, Databricks, Microsoft, MongoDB and the systems integrators
Gross versus net budgets, and the three tests we are tracking each quarter
The report also has a deeper backend. Atlas, the Creative Strategies research portal, holds the companion packet for this report: the scenario outputs behind every exhibit, the toll calibration and the readiness build, with an agent you can question directly, for example on how capture changes if read access stays metered. Subscriptions with Atlas access include it.