Palantir 2Q26 Earnings: The Institutional Compiler

AI models are learning to understand the world.

Palantir is trying to do something more valuable: teach those models how a specific institution works.

2Q26 suggests that this layer may now be compounding. Revenue growth reached 93%, net dollar retention rose to 157%, and incremental operating margins approached 78%, even as customer growth remained far lower.

The implication is not simply that Palantir is selling more software. It is that each workflow may be making the next one faster, broader, and more profitable.

My latest on Palantir’s “institutional compiler,” why cheaper AI could strengthen its economics, and the race between Palantir’s depth and the incumbents’ distribution.

AI models understand the world. Palantir is compiling the firm and Q2 suggests each workflow is making the next one faster, broader, and more profitable.

A compiler is invisible when it works. A programmer writes in human-readable language; the machine requires exact instructions. The compiler resolves definitions, links libraries, enforces rules, and turns intention into execution. The tenth program reuses what the first nine compiled.

Large organisations face the same problem. Their operating knowledge is scattered across software, employees, policies, permissions, exceptions, and accumulated judgment. A foundation model may understand supply chains in general; it does not know which substitution this company permits, who may approve it, what SAP must record, or whether the decision worked.

The model understands the world. It does not understand the firm.

Palantir calls the translation layer between the two the Ontology. It maps an institution’s objects and relationships, connects them to live systems, encodes who may act, and records what follows when an action is taken. I think a better description is an institutional compiler: Palantir turns the messy logic of an organisation into an executable model that employees, applications, and AI agents can use.

The big fundamental question is: can each compiled workflow increase the speed, scope, and value of the next before Microsoft, ServiceNow, AWS, Google, and SAP compile enough of the firm through systems they already distribute?

Q2 matters because the accounts now look like the compiler has begun to compound.

From Coordination to Compounding

Our view of Palantir has changed in stages. In October, I described the Ontology as the modern equivalent of MS-DOS: a common language across incompatible enterprise systems. After Q4, the argument became stronger. Once an institution had mapped its objects, permissions, and operating relationships into Palantir, replacement was no longer a normal software switch; it required reconstructing the institution’s operating model.

Q1 moved the thesis from coordination to absorption. Workflows that once lived inside individual applications could increasingly move into the Ontology, while the applications became interfaces. Our Q1 article also applied the Jevons idea to enterprise AI: as inference prices fall, firms use more intelligence, which creates more agents, tool calls, and governed actions. The force commoditising models increases demand for the layer controlling what those models may see and do.

What I missed was the effect on Palantir’s own growth curve. I understood why cheap models should create more demand. I had not fully understood why every successful workflow might reduce the difficulty of the next one, allowing Palantir to accelerate even as its base became larger.

The Ontology does not merely govern more work. It preserves what was learned while that work was being compiled.

The Acceleration That Should Not Be Possible

Palantir’s year-over-year growth has accelerated for eleven consecutive quarters. The percentage progression is striking; the absolute additions are harder to explain away.

Source: Palantir quarterly earnings materials; calculations.

Palantir added roughly $326 million to its quarterly revenue base in the year ending Q2 2025. It added about $932 million during the following year. Q2’s sequential addition alone was larger than the entire US commercial business one year earlier.

A temporary AI spending surge could produce a few strong quarters. It would be less likely to produce accelerating percentage growth, larger dollar additions, higher retention, expanding contracted demand, and wider margins at the same time. Q2 revenue grew 93% to $1.935 billion; US commercial grew 149% to $764 million; US government grew 90% to $809 million. Adjusted operating margin reached 62%, and adjusted free cash flow was $1.22 billion at a 63% margin.

The most revealing comparison is revenue against customer count. Total customers rose 24% over the year while revenue rose 93%. US commercial customers increased 35%, from 485 to 653, while US commercial revenue rose 149%. Sequentially, customer count grew 6% and revenue grew 28%. The system is going deeper much faster than the customer base is becoming broader.

Net dollar retention confirms that interpretation. It was 107% in Q3 2023 and reached 157% in Q2 2026. Existing cohorts are not merely renewing; they are becoming much larger relationships.

The forward indicators trace the same progression:

Source: Palantir quarterly presentations. Figures may not sum perfectly because of rounding.

Total RPO has increased almost fivefold; long-term RPO more than sixfold. Late 2025 brought a surge in multi-year commitments. Since then, short-term RPO has accelerated toward recognition while the long-term bucket keeps refilling. Billings have nearly quadrupled. Palantir is converting backlog without emptying it.

RPO also understates part of the government business because contracts with initial terms of twelve months or less, and obligations outside termination-for-convenience clauses, are excluded. Management says the reported balance is therefore primarily commercial.

The cost structure completes the argument. From Q2 2025 to Q2 2026, Palantir added about $932 million of revenue and $730 million of adjusted operating income, an incremental adjusted margin near 78%. Revenue grew 93%; adjusted expenses grew 37%. Those are not the economics of a business that repeats the same amount of labour for every new workflow. They imply reuse.