Palantir and Caterpillar results are being read by investors as evidence that ‘real’ AI spending is arriving, analyst says

Shares of data-analytics software maker Palantir were in focus after an investor pointed to the company’s and Caterpillar’s latest results as indicates that spending on artificial intelligence is becoming more concrete. Speaking in an interview circulated by Yahoo Finance, Haworth said the market is not “probably completely past all the fears,” but that the combination of results from more than one company suggests spending is “real and happening.”

Haworth’s remarks were framed around the broader debate that has dogged the AI market for more than a year: whether enterprises are scaling beyond demonstrations and narrowly scoped deployments. In that context, the investor argued that the recent reporting from Palantir and the industrial equipment maker Caterpillar provides incremental confirmation that budgets are translating into measurable activity.

The interview also suggested a timing shift in how investors interpret company performance. Rather than treating AI related revenue as purely speculative or tied only to early-stage projects, Haworth said the pattern of outcomes “we’re getting there,” implying a gradual movement toward more durable adoption.

Palantir is widely viewed by investors as a company positioned to sell software and services that help organizations use data for operational decision-making, with AI capabilities embedded in its customer deployments. The company’s results, and commentary around customer demand, are often read as a proxy for whether enterprise AI use cases are progressing in sectors beyond consumer and cloud-native experimentation.

Caterpillar, meanwhile, is often considered a bellwether for industrial digitization because of its global equipment footprint and maintenance-driven business model. When companies outside the pure-play software cohort report AI-related progress or stabilization in spending, the market typically uses those indicates to judge whether enterprise technology procurement is broadening.

Even with the constructive interpretation, Haworth did not provide new, deal-level specifics in the interview clip that would allow outsiders to quantify how quickly AI deployments are scaling. The remarks, as presented, were focused on the “indicate that spending” thesis rather than on particular customer contracts, revenue amounts, or adoption metrics.

For readers trying to translate the comments into concrete expectations, the main missing element is transparency around the mechanism behind the improvement. Neither Palantir nor Caterpillar was described in the clip with detailed disclosures on AI implementation scope, the duration of contracts, or the percentage of revenue attributable to AI-enabled use cases.

What to watch next is whether Palantir continues to describe customer demand in ways that link directly to AI deployment milestones, and whether Caterpillar and other industrial customers provide similarly specific disclosures. If the market’s “real AI returns” narrative holds, the next reports are likely to be judged not only by directionally better results, but by the clarity of how AI projects are being rolled out and monetized.