Palantir Says SaaS Is Dead
By: Steve Banker (Forbes)
Software-as-a-Service appealed to venture capitalist investors following the Dot.com crash of the 1990s. The cost efficiency of SaaS created a cookie-cutter mid-size business sector that was essentially strip-mined for financial gains at the expense of resilient diversity. The result has been a hollowing out of middle and consequent abandonment by investors. Palantir is attempting to exploit the hollowing out of the economy by moving away from the SaaS model. However, Palantir is still selling access to proprietary software as a source of revenue, a sort of "Application-as-a-Contract" model, only affordable by large businesses. Palantir is not attempting to fill the hollow middle.
But Danny Lukus also points to evolving entrepreneurial opportunities that could help revive the hollow middle. The Palantir model Lukus describes suggests an evolution toward the need for "deployment engineers" who can adapt existing systems to AI capabilities. This could be a new career path for STEM students who have knowledge of the nuts and bolts of business and industrial systems along with knowledge of AI. Adapting existing systems to AI would be much more cost effective than having to replace systems for AI compliance.
S E E D E D C O N T E N T
May 17, 2026 -- According to Danny Lukus, a deployment strategist at Palantir Technologies, "SaaS is dead." We were talking specifically about Palantir's approach to supplying software for supply chain management.
I had seen the CEO of Advance Auto Parts speak at a UBS Global Consumer and Retail Conference. He mentioned that Advance Auto is working with Palantir for an inventory replenishment and a pricing solution. That surprised me. I did not know Palantir was playing in the SCM realm. That is because Palantir does not provide off-the-shelf software, which are the supply chain software companies I have closely followed for years.
Palantir Technologies (NASDAQ: PLTR) is a data analytics and AI software company best known for providing solutions to the US military. It surprised me to learn that 46% of their revenue last year came from commercial clients rather than the government. Mr. Lukus said their largest commercial vertical was manufacturing and that virtually all of their manufacturing clients were using their solution in their supply chains.
Palantir Technologies' rapid growth has made it one of the top 20 most valuable U.S. firms. Its current market capitalization is $320 billion.
The Palantir Approach
"What Palantir does is a bit different," Mr. Lukus said. He described how decades ago, companies developed their own software. But this was risky; there was a high failure rate. And the total cost of ownership was high.
Hence, enterprise software and, eventually, supply chain software development companies arose. These would include companies like SAP and Oracle on the ERP and supply chain side, as well as best-of-breed supply chain software companies like Blue Yonder and Manhattan Associates.
First, these companies sold in a software license model, then they moved to software-as-a-service. These companies provide standard solutions that they sell to a wide range of customers.
"Palantir has a very different approach," Mr. Lukus explained. First, they are skeptical that the standard solution actually aligns with how companies want to run their businesses. Most companies end up using Excel and other offline workflows, which is an implicit admission that the standard solution does not work for many. The ERP and SCM solutions are just too "rigid." "We have humans doing manual data integration and creating their own logic. There are a lot of bad things that happen as a result of that."
The second thing Palantir argues is that if you use a standard template to run your business, your capabilities are not differentiated from your competitors'. "You have ceded" your strategic differentiation. And every time you ask your software partner to develop functionality that will help you run your business better, you are giving those same capabilities to your competitors.
Palantir uses "forward-deployed engineers" to provide solutions that are specifically tailored to their customers. These engineers determine the capabilities and processes their customers want to develop to fill the gaps left by their standard software. Essentially, the Palantir solution is built on top of solutions like SAP and Oracle.
The Palantir platform is used to build these new company-specific capabilities. This toolkit approach builds custom software. Initially, Palantir engineers do this, but their customers can also use the toolkit for this purpose. These capabilities can be developed without upgrading existing software systems or replacing legacy software, a process that could take years.
Palantir has "an abstraction layer," Mr. Lukus continued. "It's an operating system that allows us to essentially sit on top of whatever the underlying infrastructure is, and the architecture is, and define data integration, both structured and unstructured, define the logic integration, both deterministic and non-deterministic." The platform also defines which decisions can be made by which users and how the data they generate is written back into the underlying systems.
This abstraction layer is based on Palantir's Ontology. "The Palantir Ontology, according to Palantir's website, is an operational layer for the organization." The Ontology not only integrates with a wide range of applications, datasets, virtual tables, and models, but also provides the real-world objects and logic needed to run a large enterprise. This includes physical assets - like plants, equipment, and products - to concepts like customer orders or financial transactions. "In many settings, the Ontology serves as a digital twin of the organization, containing both the semantic elements (objects, properties, links) and kinetic elements (actions, functions, dynamic security) needed to enable use cases of all types." Their ontology layer may be the most differentiating feature of this solution.
Palantir and AI
In the "age of AI", Mr. Lukus continued, it "is even easier for me to go and build customer-specific S&OP (sales and operations planning) models or to build scheduling and logistics models that read and write with the underlying systems." Anthropic's Claude or OpenAI's Codex can be used for code generation.
In supply chain management, we often refer to AI in terms of machine learning or optimization. Machine learning is a technique that has been used for over 20 years to improve demand forecasting and inventory planning. Optimization has also been used for decades. Optimization models account for supply chain constraints, such as factory line throughput, and seek to maximize throughput based on their understanding of these constraints and company goals.
AI for Software Coding
But Mr. Lukus was using the term "AI" differently. AI is used as a software development tool that can build code much more quickly than was historically possible. The historical way that software was built was very expensive. Because it was so expensive, consultants and system integrators conducted months-long studies to understand all the user and business requirements. Now, forward-deployed engineers can go in, quickly build models and workflows, get feedback, and iterate until they get a solution that fits.
"The speed of being able to build that type of software is dramatically lower and dramatically cheaper," Mr. Lukus asserted. That is why "SaaS is dead."
But what about the counterarguments? Once the software is configured, the company is far from finished. The software needs to demonstrate that it can be scaled. It also must be extensively tested. Mr. Lukus said generative AI can do this as well.
Furthermore, companies may not have the internal processes to keep up with changes in laws, such as tariff changes. A software company will have domain experts specifically focused on keeping its software up to date with legislative changes. Software agents, he said, could be directed to the relevant websites, and when legislative changes occur, the right managers in a company could be notified.
Mr. Lukus described a complex supply chain workflow they built, which was then utilized by a few workers within two months. By the end of the quarter, it was fully deployed to a large team.
AI Agents
Secondly, AI is used in the sense of agents. Agents mimic the actions of a human with a particular role.
So, I asked, "It's not that the agent is necessarily incredibly intelligent, like some advanced form of optimization. It's that it's part of this end-to-end process, and it can collaborate with other agents. "Exactly right," Mr. Lukus said. For example, "I can have an agent say, 'Hey, is this a new purchase order or not? And if it's a new PO, I should parse it, match it to the master data, and determine, 'Hey, it's this customer, and they want this product.'"
Other agents could be used to determine if the products are in stock or need to be manufactured. And if the product needs to be manufactured, how long would it take? In this case, a scheduling agent using linear programming provides the answer.
Can optimization or forecasting agents be effectively built with code-generation tools? But Mr. Lukus asserted that Claude and Codex can do it quickly.
Final Word
While AI based on large language models can create much code that does not need to scale, some applications still do. I remain doubtful that generative AI could create code that would scale up to handle the incredibly large number of transactions generated in a complex warehouse.
I am also still skeptical about generating optimization agents capable of solving big optimization problems. Some planning software companies have spent decades developing cutting-edge mathematical algorithms to solve various optimization problems. Further, solving large optimization problems requires engines that are able to massively scale.
Governance is also an issue. While you might be able to have an agent monitoring a government webpage to determine a specific change to tariff law, all sorts of laws change all the time in nations around the world.
Mr. Lukus admitted that most manufacturing clients initially used the Palantir solution for Sales & Operations Execution. S&OE is based on sensing exceptions and creating automatic responses. I know that Aera Technology offers a similar solution to solving the S&OE problem and has proven its effectiveness.
With most solution companies, I want to verify the claims the supplier makes by speaking with one or more of their customers before writing about them. But Palantir is a large company generating a lot of buzz. Further, the Palantir website does offer quotations from Wendy's, Tyson's, General Mills, and other companies touting its effectiveness. This is at least partial proof of effectiveness.
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Is Software-as-a-Service really dead? What Danny Lukus describes seems more like a remodel rather than a replacement. But the Palantir approach does provide a clue for future needs and possible career opportunities.
The weak spot in current AI development is adaptation to existing systems. A savvy entrepreneur could exploit this opportunity by becoming a "deployment engineer".