<\/p>\n
A Palantir client is assigning a number to it.<\/p>\n
McCarthy Building Companies says an AI-assisted workflow built with Palantir Technologies (PLTR) and Nvidia (NVDA) technology is saving the construction company about 30,000 work hours annually.<\/p>\n
Investors are paying attention to more than just that figure.<\/p>\n
L3Harris Technologies (LHX) says an internally tested AI setup built with Palantir and Nvidia models cut costs by 95% in its test environment.<\/p>\n
Another implementation, according to Tampa General Hospital, reduced latency 22 times while increasing model performance by 7%.<\/p>\n
According to Palantir, the three customers are among several companies that are putting its artificial intelligence platform, Foundry, and Ontology software into real-world operations.<\/p>\n
Those figures could be more important to Palantir investors than any other AI presentation.<\/p>\n
In its most recent reported quarter, the company’s U.S. commercial sales increased 149% year over year.<\/p>\n
Palantir must demonstrate that clients aren’t only experimenting with AI to maintain anything approaching that pace.<\/p>\n
To continue growing it, they must get sufficient economic value.<\/p>\n
Palantir’s 30,000-hour number makes AI easier to understand<\/p>\n
It may seem less obvious to search for cutting-edge AI in the construction industry.<\/p>\n
McCarthy’s case is unique because of its scale.<\/p>\n
The 160-year-old construction business used over 100,000 production requests for information, or RFIs, to train Nvidia’s Nemotron models.<\/p>\n
When plans, specifications, or responsibilities need clarification during a construction project, an RFI serves as a formal query.<\/p>\n
If certain issues are not answered quickly, a project may be delayed or expenses may rise.<\/p>\n
Before a request reaches a project site, McCarthy uses Palantir’s algorithms to determine whether requests can cause scheduling or cost issues.<\/p>\n
Additionally, it developed a subcontractor agreement writing system with AI assistance.<\/p>\n
According to Palantir, that workflow is saving McCarthy around 30,000 hours annually while performing at a lower cost than the general-purpose AI models McCarthy tested against.<\/p>\n
Related: Palantir stock faces brutal test after crushing expectations<\/p>\n
Compared with a benchmark score, that business case is much easier to understand.<\/p>\n
Thirty thousand hours is about the same as more than a dozen full-time workers’ yearly labor hours.<\/p>\n
McCarthy’s ability to kill those jobs is not necessarily the objective.<\/p>\n
It means that thousands of hours that were previously devoted to detecting hazards and converting project information into contracts may be used for other purposes.<\/p>\n
When determining if another AI contract is worthwhile, business purchasers might provide a chief financial officer with this kind of analysis.<\/p>\n
Other Palantir customers are putting numbers on AI<\/p>\n
McCarthy is not the only customer reporting measurable results.<\/p>\n
Defense contractor L3Harris has connected more than 30 enterprise resource planning systems through Palantir, creating roughly 3.5 million data connections and more than 5,000 scheduled data feeds.<\/p>\n
Instead of depending solely on general-purpose models, the business is now evaluating AI models trained on its own exclusive data.<\/p>\n
L3Harris Chief Transformation Officer Heidi Wood said initial internal testing outperformed frontier models in the company’s test environment while cutting costs by 95%.<\/p>\n
Those results are company-reported and have not been independently audited.<\/p>\n
Another example is Tampa General Hospital.<\/p>\n
Since 2021, the hospital has collaborated with Palantir and employs their software for tasks including managing patient flow, staffing nurses, and spotting sepsis symptoms.<\/p>\n
Its most recent initiative makes use of an Nvidia model tailored to Tampa General’s specific risk and privacy needs.<\/p>\n
Palantir says that by fine-tuning the model, performance increased by 7% and the model became 22 times faster.<\/p>\n
Another new client has set similar ambitious objectives.<\/p>\n
Battery materials manufacturer POSCO Future M signed an agreement on Oct. 21 to deploy Palantir’s Foundry and AIP platforms.<\/p>\n
The firm wants to reduce product development time by 50%, enhance production efficiency by 30%, and improve customer response by over 60%.<\/p>\n
Instead of actual outcomes, they are objectives.<\/p>\n
Palantir will be used in sales and marketing at first, according to POSCO Future M, before the company decides whether to extend the technology into production.<\/p>\n
That difference is crucial.<\/p>\n
McCarthy reports an operational savings as of right now.<\/p>\n
POSCO is outlining what it believes Palantir will ultimately be able to provide.<\/p>\n
Investors should not treat the two figures the same way.<\/p>\n
Palantir’s next growth argument comes down to one number<\/p>\n
Finn Gomez \/ Getty Images<\/p>\n
Palantir needs customer ROI to keep extraordinary growth going<\/p>\n
Palantir is already growing at a rate that few major software firms can match.<\/p>\n
Revenue for the second quarter was $1.94 billion, a 93% increase over the same period last year.<\/p>\n
While U.S. government income jumped 90% to $809 million, commercial revenue increased 149% to $764 million.<\/p>\n
In addition, Palantir completed 73 transactions during the quarter, each totaling at least $10 million.<\/p>\n
Palantir reports that by the end of June, the company’s clientele had increased from 849 to 1,049.<\/p>\n
These figures clarify the significance of customer outcomes.<\/p>\n
Palantir does not necessarily need to explain its long-term growth plans in order to continue obtaining experimental contracts.<\/p>\n
When an experiment turns into a deployment, the client spends more money spreading it across the company, and the deployment results in quantifiable savings, the outcome is stronger.<\/p>\n
The significance of such growth is already evident in Palantir’s own filings.<\/p>\n
In the second quarter, the average trailing-12-month revenue from its top 20 clients was $124 million, a 67% increase from the same period last year.<\/p>\n
This implies that current clients are growing their accounts considerably.<\/p>\n
A potential explanation for this increase may be found in the latest AI examples.<\/p>\n
Purchasing additional Palantir software becomes more justifiable if a military contractor can significantly lower the cost of operating specialist AI models or if a construction business can save 30,000 hours of manual labor.<\/p>\n
However, there is still a significant warning.<\/p>\n
Palantir shared most of the latest efficiency statistics with its clients, but it did not provide sufficient financial information to determine the ROI for each deployment.<\/p>\n
The highest productivity figures for POSCO are still objectives.<\/p>\n
Furthermore, a single effective process does not guarantee that every Palantir client will get comparable savings.<\/p>\n
That is the countercase investors should watch.<\/p>\n
Palantir reports third-quarter results after the market closes on Nov. 2.<\/p>\n
The focus will be primarily on revenue growth.<\/p>\n
Beneath that could be the more significant long-term question: Are clients getting enough quantifiable value from Palantir’s AI to continue implementing more of it?<\/p>\n
Palantir has a convincing response thanks to McCarthy’s 30,000-hour estimate.<\/p>\n
It must now make such outcomes reproducible.<\/p>\n
Related: Palantir stock faces brutal test after crushing expectations<\/p>\n
#Palantir039s #pitch #30000hour #proof #point<\/p>\n","protected":false},"excerpt":{"rendered":"
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