Market Intelligence · Palantir Technologies (PLTR)

What Is Palantir Quietly Building?

A trail of AI patent filings offers a surprisingly clear clue: Palantir appears to be designing pieces of an AI workforce for the enterprise.

Something interesting is showing up in Palantir's patent filings. Look at the applications together and they begin to resemble pieces of the same system: build an AI agent, connect it to a company's real operations, control what it is allowed to access, let agents work with tools and one another, and then evaluate whether they actually did the job correctly.

That is a much bigger idea than another chatbot. Palantir appears to be working toward AI that can function inside an organization — using approved data, interacting with business systems and carrying out tasks while remaining inside defined security and permission rules.

» Three Patent Views · Tap Any to Open Full Size in New Tab

Agent operations: managing AI agents, models, tools and enterprise data.

Agent evaluation: checking performance, steps, results and problems.

Ontology: connecting AI to real business objects, permissions and operations.

Three patent views, presented as an interactive visual brief.

The emerging system

One filing describes an Agent Ops Framework. In plain English, it is about managing AI agents as workers: giving them tasks, connecting them to models and tools, allowing them to use resources and coordinating how the work gets done. Instead of one AI answering one question, the architecture points toward specialized agents handling parts of a larger job.

From AI worker to enterprise system

Another filing focuses on evaluation. If companies are going to trust AI agents with real work, they need a way to judge them. Palantir's Agent Evaluation Framework describes technology for evaluating an agent's behavior and results, comparing configurations and identifying problems. In effect, Palantir is not only thinking about the AI worker; it is also thinking about the supervisor.

Then comes Palantir's Ontology. This is where the pieces start to connect. An ontology can tell the AI what the company's data actually represents — which machine belongs to which factory, which order belongs to which customer, which employee has permission to do what, and how those pieces relate to one another. That gives an AI agent something far more useful than a pile of data: context about how the business works.

There is another piece that makes these filings particularly interesting: NVIDIA. Palantir and NVIDIA are already working together on operational AI, combining NVIDIA's accelerated computing and Nemotron AI models with Palantir's AIP and Ontology. In simple terms, NVIDIA can provide the engine while Palantir provides the system that connects that intelligence to the actual business. That makes the patents around agents, evaluation and Ontology look less like isolated filings and more like pieces of a larger enterprise AI architecture.

Put the filings together and a possible roadmap emerges: create the agent, connect it to the enterprise, give it controlled access and tools, allow agents to cooperate, and evaluate the work they produce. Patent applications do not guarantee that Palantir will win this market, but they can reveal where engineering resources are going and what the company believes may be important enough to protect.

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Market Perspective · Math Makes Money

Wall Street's Assumptions

You know the old saying about assumptions. They make an... The S&P 500 keeps making records while strategists keep moving their targets. Maybe the better question is not where the market will finish in December, but what earnings, margins and productivity are telling us right now.

A forecast is an educated guess. The market is the final score.

Corporate profits remain strong, margins are near historic highs, and every year Wall Street strategists still try to tell us exactly where the market will be in December.

The problem is those targets are often wrong. Jason Zweig of The Wall Street Journal has described the annual forecasting exercise as a “ritual of wrong.” One review found the S&P 500's actual return landed outside the entire range of strategist forecasts in seven of eight years studied.

That raises a fair question: where is the accountability? In most businesses, if someone repeatedly makes important projections that miss badly, eventually somebody wants answers. On Wall Street, a target can miss by a mile, get adjusted along the way, and then the following year begins with another new prediction.

Strategists still have value. Their research, economic work and company analysis can be excellent. But a year-end target should be treated for what it is: an educated estimate, not a fact.

And when the forecasts keep missing, maybe the better question is not what Wall Street predicts next, but whether some of the assumptions behind those predictions need to change.

Maybe the Market Changed

There is another possibility: maybe the strategists are not just missing the market.

Maybe some of the old assumptions are no longer working as well.

S&P 500 profit margins are near historic highs, and companies are finding ways to turn more of every revenue dollar into profit. AI could push that even further. If companies can produce more without costs rising just as fast, margins expand, profits grow faster, and some traditional growth and valuation models may start to look outdated.

That is what could make AI bigger than another tech boom. If it truly changes the economics of corporate America, Wall Street may be trying to value a new economy with an old calculator.

And we have seen a version of that before. In the late 1990s, Alan Greenspan faced an economy that was growing faster than many economists thought it could without creating inflation. Productivity was rising, companies were producing more efficiently, and some of the old assumptions about growth and inflation were no longer working quite the same way.

That is where the comparison gets interesting.

Greenspan Has Seen a Version of This Before

Greenspan noticed that productivity was rising much faster than expected. Labor-productivity growth had averaged less than 1% in the early 1990s. By 1999, it was running at roughly 3%. Companies were producing more without labor costs rising at the same pace, which helped keep inflation under control.

In fact, Fed officials acknowledged that stronger productivity had caused economists to underestimate economic growth and overestimate inflation.

AI raises a similar question today: what if companies can produce more, lower costs and grow profits through AI? The economy may be able to grow faster than the old models assume without automatically creating the same inflation pressure.

That does not mean the 1990s are repeating themselves. But it does raise a very interesting possibility: What if AI is changing the speed limit of the economy?

The Market Undercurrent · Thursday Aug 13, 2026

Deep in the Earnings Woods

Deep in earnings season: the market-moving reports still ahead and what traders should watch next.

We are well into earnings season, but the calendar is not finished with us yet. The next wave could reshape retail, consumer and AI sentiment — and guidance may be the thing lurking behind the headline numbers. Dates and times are based on company calendars available at publication.

» Still Lurking on the Calendar

Dates, leadership changes and company developments were checked against company materials. Consensus figures, options availability and liquidity can change. Prepared Aug. 13, 2026. For education and market awareness only. Not investment advice.

The Week Ahead

Earnings Calendar · Aug 10–14

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