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When entrepreneurs list their principal reasons for launching a company, small business owners often cite being their own boss, flexibility in setting their working hours, and turning a commercial concept into reality as their main motivations. Now, new data identifies another incentive that may convince future entrepreneurs to take the plunge. According to a recent analysis by the Federal Reserve Bank of Minneapolis, the average self-employed person earns significantly more income during their career than people who work for someone else. However, the reports findings also note the widely varying levels of income among small business owners, and the length of time usually required before stronger earnings start flowing in. Those details may lead some less enterprising prospective entrepreneurs to stick with punching a clock after all. The analysis by the Minneapolis Fed differs from most research on small business owners, which often relies heavily on survey responses. The shifting makeup of participants in those inquiries often produces widely contrasting results, creating what Minneapolis Fed authors likened to the parable of the blind men and an elephant: Each poll was essentially touching only one part of the body, and led to researchers drawing different and incomplete conclusions. To establish a more complete picture of the nations entrepreneurs, the Minneapolis Fed used U.S. tax and Social Security Administration data from 2000 to 2015. That allowed it to determine the income those small business owners collectively generated for themselves, and identify why they stuck it out with companies that were often slow to reach profitability. And that wasnt due to setting their own hours. (W)e find that self-employed individuals have significantly higher income and steeper income growth profiles than paid-employed peers with similar characteristics, the report said, while also refuting frequent survey results that suggest many entrepreneurs stay in business for the perks of not having to answer to a boss. Contrary to earlier studies based on surveys plagued by underrepresentation in the right tail of the income distribution, we find that non-pecuniary benefits of self-employment are not substantial when considering the source of most business income, it said. What that means, in non-economist-speak, is that many entrepreneurs earn up to 70% more than people working for other employers over their careers, with their income increasing considerably faster than paid workers. That winds up vastly outweighing the advantages surveys often identify of founders setting their own work schedules or getting to ask employees to fetch their coffee. The study found that during the 15-year period, a 25-year-old entrepreneur earned on average about $27,000 per year in 2012 dollars, while an employee of the same age made $29,000. About five years later, that income disparity had typically reversed, and then continued growing larger in small-business owners favor. By age 55, our estimate is an average (entrepreneur) income of $134,000 in 2012 dollarsmuch higher than the estimate of $79,000 for the paid employed, the study said. It added that gap was probably even larger before government agencies adjusted small-business income declarations by 14% to 46% to account for presumed underreporting. These dierences in profiles for the self- and paid-employed would be even more striking if we were to (re)adjust reported incomes to account for business income underreporting. Not every small-business owner winds up earning as much as people working for salaries, howeveror as much as their more successful peers. The study said about 80% of the total income of entrepreneurs it identified was generated by people earning $100,000 annually or more. That means a lot of small-business owners fared less well than the more affluent minority at the top. As a result, the authors said in wonky terms, a minority of self-employed people made even less than workers working for someone else. IRS data shows that many of the primarily self-employed earned less over the sample years than paid-employed peers with similar characteristics, but in the aggregate this subgroup has a much lower share of the total income than those that earned more than their peers, it noted. The Minneapolis Fed noted some other interesting observations in its findings. One was that many entrepreneurs continued working salaried jobs, or had other income coming in as they supported their still unprofitable new ventures. Those supporting funds improved the cohorts overall positive revenue figures, even during early lean years. In other words, when starting a new business, owners rely on other sources of labor earnings, through either paid employment or other business enterprises, it said. Thus, even though most businesses have losses, few owners have negative individual incomes. Another significant detail was what the authors said was their use of official data to create a more precise collective financial portrait of entrepreneurscontrasting the results of many surveys that may simplify the motives and activities of limited samples of small-company owners. (T)he literature on entrepreneurship has an array of narratives, describing the typical business owner in many possible ways: as a gig worker seeking flexible arrangements, a misfit avoiding unemployment spells, an inventor seeking venture capital, a tax dodger misreporting income, it said, before noting its own use of official income statistics collected from millions of entrepreneurs. This data provides new insights into the central questions of the entrepreneurship literature and will hopefully prove useful for researchers interested in calibrating models of self-employment and business formation. Bruce Crumley This article originally appeared on Fast Companys sister publication, Inc. Inc. is the voice of the American entrepreneur. We inspire, inform, and document the most fascinating people in business: the risk-takers, the innovators, and the ultra-driven go-getters that represent the most dynamic force in the American economy.
Category:
E-Commerce
Theres a scene in Office Space where Peter sits across from two consultants during a company downsizing. They ask him, What would you say you do here? He hesitates, smirks, and admits he only works about 15 minutes a week. The rest of the time, hes pretending. It was comedy in 1999. Its confession now. That question has come back to us. For years, we filled our calendars, stayed visible, and kept the machine moving. Our worth was measured in hours, output, and presence. It had to be. Humans were the system, and the system required us to keep it running. We didnt question it because that was how things got done. AI has changed that. It can now do many of the things we once did to keep things moving: the summaries, the reports, the follow-ups, the updates, the spreadsheets. It can organize, calculate, write, and execute at a pace we cant match. That realization feels strange at first, but its also freeing. Now we get to hand that part over. We can give the robotic work to the robots and return to the human work. The work of thinking, deciding, designing, and connecting. So what does that look like? For one, it means our conversations are changing. When the noise quiets, the meetings sound different. Theres more space to ask better questions. We can finally talk about what matters: What is the business really trying to accomplish? Whats next? What do we need to build the product, craft the strategy, organize the team, and align around purpose? Its fantastic, really. Because when people stop being buried in repetitive work, they start showing up differently. They bring curiosity. They tell the truth. They collaborate in new ways. Im hearing it everywherein companies that are deep into their AI transformation and in those that are just starting. The tone is changing. The conversations are more human. Were still in the waiting room of this transition. Some are pacing the floor, some are seated patiently, some are already being called in. Wherever a company sits on that curve, the shift has begun. Deloittes 2024 Global Human Capital Trends report describes this moment as a readiness gap. Most leaders recognize that AI and technology will transform their organizations in the coming years, yet few say they feel prepared to lead their people through that change. The tools are ready. The humans are still catching up. For leaders, this is the moment to adjust the focus. The work still needs watching, but the focus of that attention is different. Its no longer about overseeing tasks; Its about overseeing direction. How we design. How we execute. How we build and with whom. Leadership now is about being intentional and accountable for how work is created, not just how it is completed. Many leaders are rebuilding, or at least redesigning, how they lead. The language is changing. The tone is shifting. Its not a different language, but it has a new accent. And those who thrive in this era will be the ones who can translate it. Theyll know how to take complexity and turn it into clarity. Theyll bring forward a sharper vision, a stronger purpose, and a deeper ability to communicate the why. Theyll be what I call full-stack leaders: people who can support the front, the back, and the middle layer. They understand product, people, and process, and they move fluidly across them all. AI has taken the repetitive pieces off our plates and has given us back the chance to think, create, and build with intention. It gives us room to lead.
Category:
E-Commerce
When an X user recently pointed out the eye-popping increase in billionaires wealth since 2015, entrepreneur Mark Cuban, a billionaire himself, responded with his opinion on why, but he urged followers to consider a different question: Why are we not giving incentives to companies to require them to give shares in their companies to all employees, at the same percentage of cash earnings as the CEO? Cuban said. It is the right question to be asking. Because while the debate over wealth inequality continues, the solution has been hiding in plain sight for decades. The top 10% of U.S. households now control 67% of all wealth, while the bottom half holds just 2.5%. The typical American worker approaches retirement with about $4,000 in savings, which is less than the cost of one month in an assisted living facility. That imbalance is not sustainable, economically or socially. The fix does not require new legislation or another corporate responsibility pledge. It lies in a proven model that has been quietly transforming companies and communities for 50 years: employee ownership. From Silicon Valley to Main Street Silicon Valley figured this out long ago. Equity compensation has been the foundation of the tech sectors innovation economy since the 1970s. Stock options allowed startups to attract world-class talent without paying top-tier salaries, align employee incentives with company performance, and build wealth for workers who might otherwise never own an asset. Yet outside of tech, broad-based ownership remains rare. Fewer than 7,000 U.S. companiesmostly in traditional sectors like manufacturing, construction, and distributionoperate under an employee stock ownership plan (ESOP). The results, however, mirror the Valleys success. Employee-owned firms grow more than 2% faster per year than their peers and are half as likely to go bankrupt. During the 2008 financial crisis, they laid off workers at only one-third the rate of conventional firms. For employees, the impact is just as powerful. ESOP participants hold 92% higher median household wealth, twice the retirement savings, and 33% higher median income than comparable workers. This is not philanthropy. It is a durable, market-tested strategy that drives growth, resilience, and equity at the same time. The Timing Could Not Be Better Today, several powerful trends make this the perfect moment to bring ownership to scale. A massive generational handoff is underway. Ten thousand baby boomers retire each day, many of them owners of successful small and midsize businesses with no succession plan. Transferring ownership to employees keeps those businesses rooted in their communities, preserves good jobs, and rewards founders with fair market value. The retirement crisis demands new solutions. With average savings at historic lows, workers need wealth-building tools that go beyond 401(k) plans. Ownership creates an asset base that compounds over time, restoring what traditional pensions once offered. Labor shortages are reshaping industries. As skilled workers grow scarce, companies that offer ownership will win the competition for talent, not only by paying well but by giving people a reason to stay. Economic volatility favors resilience. Employee-owned companies outperform during downturns because people at every level have a stake in the outcome. Ownership builds both financial and cultural strength. Beyond Good Intentions America has no shortage of programs designed to help workers. What it lacks is awareness and adoption of the ownership mechanisms that allow employees to share in the value they create. As long as labor and ownership remain separated, inequality will continue to deepen. When employees have an equity stake, their focus shifts from completing tasks to building lasting value. They think like owners because they are owners, and that mindset fuels innovation, strengthens loyalty, and creates a powerful cycle of trust and accountability. The impact case is clear, and the business case is even stronger. Broad-based ownership builds companies that last. It keeps wealth circulating within communities instead of extracting it, and it turns employees into long-term investors in the enterprise they help build. The Moment to Act We are standing on the edge of a once-in-a-generation opportunity to reimagine capitalism for shared prosperity. Employee ownership will not fix every inequity in our economy, but it addresses one of the most fundamental: who benefits from the value a company creates. Cubans challenge should not disappear into the social media ether. It should become a call to action for policymakers, investors, and business leaders to make employee ownership the standard, not the exception. America does not need another wealth redistribution debate. It needs a wealth participation strategy. Employee ownership represents capitalism at its best: fair, inclusive, and fiercely competitive. It aligns profit with purpose and ensures that the people who build our companies share in their success. If we scale it now, we can turn todays inequality into tomorrows shared prosperity.
Category:
E-Commerce
You’ve heard the gospel: AI is going to change everything. Good, great, grand. But when youre staring down a deadline and 80 unread emails, you don’t need philosophy, you need a cheat sheet. The fastest way to master AI isn’t by watching lectures, it’s by finding a way to replace an hour of your grind with a 10-second prompt. Here are five specific, repeatable ways to automate your most time-consuming professional tasks. Grab your chatbot of choice (Gemini, ChatGPT, Claude, Copilotwhatever floats your boat) and let’s get to work. Writing Staring at a blank page. Tedious, formulaic first drafts. Enough. You are a professional. You shouldn’t be spending an hour drafting a boilerplate email to a client or writing the first three paragraphs of a report. Thats grunt work. Instead, master constraint-based prompting. This is where you tell the AI exactly what to write and how to write it, forcing it to follow your specific, professional rules. Heres a prompt example: “You are a [job title]. Draft a [document type (memo, email, etc.)] to [target audience]. The tone must be [tone]. The three key takeaways are [list three specific bullet points]. The final memo should be around [length in words] and include a subject line. Post-meeting action items Sifting through long transcripts and meeting notes for action items? You’re doing it wrong. Let the AI do the heavy lifting of synthesis. Its time to leverage deliverable-based prompting. Instead of asking for a summary, ask the AI to produce specific, structured outputs from a large body of text, such as a meeting transcript or a dense PDF. For example: “Analyze the following [meeting transcript/document]. Do not summarize the entire text. Instead, produce three distinct outputs: 1) A table listing all action items, the person responsible, and the deadline mentioned. 2) A list of three open questions that were not resolved. 3) A short, two-sentence email subject line for the follow-up.” In less than a minute, you can transform raw data into a clean, actionable task list. Research To turn generative AI into a true, trusted research assistant that can search and cross-reference information scattered across multiple work files requires using tools that let you upload your own content, such as Googles NotebookLM or similar features in other platforms. This is called contextual grounding, and it involves uploading a handful of annual reports, project documents, or extensive research files. Check with your organization first to see if there are any rules against this. Heres a prompt you can use: “Based only on the uploaded documents, what is the biggest discrepancy between the Q4 2024 revenue projection [from Document A] and the actual Q1 2025 marketing spend [from Document C]? Explain the gap in three bullet points, referencing the specific document where the information was found.” This lets you stop relying on the AIs general knowledge and start using it as a hyperefficient analyst for your own private data, generating insights that would take hours to gin up on your own. Brainstorming Thanks to AI, hitting a creative wall or falling victim to groupthink during brainstorming is nothing like it used to be. While your brain thinks linearly, AI can think exponentiallybut you have to force it to show its work. Employ critical reasoning prompting, also called “chain-of-thought.” This forces the AI to debate, critique, and explore alternatives before settling on an answer. A sample prompt formula: “I have an idea for a new product feature: [describe the feature]. Before you propose a name for it, I need you to first: 1) Act as a skeptical customer and list three reasons why this feature is useless. 2) Act as a competitor and list three ways they could easily copy and neutralize the feature. 3) Only after those two steps, propose three distinct, benefit-driven names for the feature.” This forces the AI to act as a constructive adversary, getting you to a better, more robust idea much faster.
Category:
E-Commerce
The low point in Palantirs very first quest for investors came during a pitch meeting in 2004 that CEO Alex Karp and some colleagues had with Sequoia Capital, which was arguably the most influential Silicon Valley VC firm. Sequoia had been an early investor in PayPal; its best-known partner, Michael Moritz, sat on the companys board and was close to PayPal founder Peter Thiel, who had recently launched Palantir. But Sequoia proved no more receptive to Palantir than any of the other VCs that Karp and his team visited; according to Karp, Moritz spent most of the meeting absentmindedly doodling in his notepad. Karp didnt say anything at the time, but later wished that he had. I should have told him to go fuck himself, he says, referring to Moritz. But it wasnt just Moritz who provoked Karps ire: the VC communitys lack of enthusiasm for Palantir made Karp contemptuous of professional investors in general. It became a grudge that he nurtured for years after. [Image: Avid Reader Press] From The Philosopher in the Valley: Alex Karp, Palantir, and the Rise of the Surveillance State by Michael Steinberger. Copyright 2025. Reprinted by permission of Avid Reader Press, an Imprint of Simon & Schuster Inc. But the meetings on Sand Hill Road werent entirely fruitless. After listening to Karps pitch and politely declining to put any money into Palantir, a partner with one venture capital firm had a suggestion: if Palantir was really intent on working with the government, it could reach out to In-Q-Tel, the CIAs venture capital arm. In-Q-Tel had been started a few years earlier, in 1999 (the name was a playful reference to Q, the technology guru in James Bond films). CIA Director George Tenet believed that establishing a quasi-public venture capital fund through which the agency could incubate start-ups would help ensure that the U.S. intelligence community retained a technological edge. The CIA had been created in 1947 for the purpose of preventing another Pearl Harbor, and a half century on, its primary mission was still to prevent attacks on American soil. Two years after In-Q-Tel was founded, the country experienced another Pearl Harbor, the 9 11 terrorist attacks, a humiliating intelligence failure for the CIA and Tenet. At the time, In-Q-Tel was working out of a Virginia office complex known, ironically, as the Rosslyn Twin Towers, and from the twenty-ninth-floor office, employees had an unobstructed view of the burning Pentagon. In-Q-Tels CEO was Gilman Louie, who had worked as a video game designer before being recruited by Tenet (Louie specialized in flight simulators; his were so realistic that they were used to help train Air National Guard pilots). Ordinarily, Louie did not take part in pitch meetings; he let his deputies do the initial screening. But because Thiel was involved, he made an exception for Palantir and sat in on its first meeting with In-Q-Tel. What Karp and the other Palantirians didnt know when they visited In-Q-Tel was that the CIA was in the market for new data analytics technology. At the time, the agency was mainly using a program called Analysts Notebook, which was manufactured by i2, a British company. According to Louie, Analysts Notebook had a good interface but had certain deficiencies when it came to data processing that limited its utility. We didnt think their architecture would allow us to build next-generation capabilities, Louie says. Louie found Karps pitch impressive. Alex presented well, he recalls. He was very articulate and very passionate. As the conversation went on, Karp and his colleagues talked about IGOR, PayPals pioneering fraud-detection system, and how it had basically saved PayPals business, and it became apparent to Louie that they might just have the technical aptitude to deliver what he was looking for. But he told them that the interface was vitalthe software would need to organize and present information in a way that made sense for the analysts using it, and he described some of the features they would expect. Louie says that as soon as he brought this up, the Palantir crew got out of sales mode and immediately switched into engineering solving mode and began brainstorming in front of the In-Q-Tel team. That was what I wanted to see, says Louie. He sent them away with a homework assignment: he asked them to design an interface that could possibly appeal to intelligence analysts. On returning to Palo Alto, Stephen Cohen, one of Palantirs co-founders, then 22 years old, and an ex-PayPal engineer named Nathan Gettings sequestered themselves in a room and built a demo that included the elements that Louie had highlighted. A few weeks later, the Palantirians returned to In-Q-Tel to show Louie and his colleagues what they had come up with. Louie was impressed by its intuitive logic and elegance. If Palantir doesnt work, you guys have a bright future designing video games, he joked. In-Q-Tel ended up investing $1.25 million in exchange for equity; with that vote of confidence, Thiel put up another $2.84 million. (In-Q-Tel did not get a board seat in return for its investment; even after Palantir began attracting significant outside money, the company never gave up a board seat, which was unusual, and to its great advantage.) Karp says the most beneficial aspect of In-Q-Tels investment was not the money but the access that it gave Palantir to the CIA analysts who were its intended customers. Louie believed that the only way to determine whether Palantir could really help the CIA was to embed Palantir engineers in the agency; to build software that was actually useful, the Palantirians needed to see for themselves how the analysts operated. A machine is not going to understand your workflows, Louie says. Thats a human function, not a machine function. The other reason for embedding the engineers was that it would expedite the process of figuring out whether Palantir could, in fact, be helpful. If the CIA analysts didnt think Palantir was capable of giving them what they needed, they were going to quickly let their superiors know. We were at war, says Louie, and people did not have time to waste. Louie had the Palantir team assigned to the CIAs terrorism finance desk. There they would be exposed to large data sets, and also to data collected by financial institutions as well as the CIA. This would be a good test of whether Karp and his colleagues could deliver: tracking the flow of money was going to be critical to disrupting future terrorist plots, and it was exactly the kind of task that the software would have to perfrm in order to be of use to the intelligence community. But Louie also had another motive: although Karp and Thiel were focused on working with the government, Louie thought that Palantirs technology, if it proved viable, could have applications outside the realm of national security, and if the company hoped to attract future investors, it would ultimately need to develop a strong commercial business. Stephen Cohen and engineer Aki Jain worked directly with the CIA analysts. Both had to obtain security clearance, and over time, numerous other Palantirians would do the same. Some, however, refusedthey worried about Big Brother, or they didnt want the FBI combing through their financial records, or they enjoyed smoking pot and didnt want to give it up. Karp was one of the refuseniks, as was Joshua Goldenberg, the head of design. Goldenberg says there were times when engineers working on classified projects needed his help. But because they couldnt share certain information with him, they would resort to hypotheticals. As Goldenberg recalls, Someone might say, Imagine theres a jewel thief and hes stolen a diamond, and hes now in a city and we have people following himwhat would that look like? What tools would you need to be able to do that? Starting in 2005, Cohen and Jain traveled on a biweekly basis from Palo Alto to the CIAs headquarters in Langley, Virginia. In all, they made the trip roughly two hundred times. They became so familiar at the CIA that analysts there nicknamed Cohen Two Weeks. The Palantir duo would bring with them the latest version of the software, the analysts would test it out and offer feedback, and Cohen and Jain would return to California, where they and the rest of the team would address whatever problems had been identified and make other tweaks. In working side by side with the analysts, Cohen and Jain were pioneering a role that would become one of Palantirs signatures. It turned out that dispatching software engineers to job sites was a shrewd strategyit was a way of discovering what clients really needed in the way of technological help, of developing new features that could possibly be of use to other customers, and of building relationships that might lead to additional business within an organization. The forward-deployed engineers, as they came to be called, proved to be almost as essential to Palantirs eventual success as the software itself. But it was that original deployment to the CIA, and the iterative process that it spawned, that enabled Palantir to successfully build Gotham, its first software platform. Ari Gesher, an engineer who was hired in 2005, says that from a technology standpoint, Palantir was pursuing a very ambitious goal. Some software companies specialized in front-end productsthe stuff you see on your screen. Others focused on the back-end, the processing functions. Palantir, says Gesher, understood that you needed to do deep investments in both to generate outcomes for users. According to Gesher, Palantir also stood apart in that it aimed to be both a product company as well as a service company. Most software makers were one or the other: they either custom-built software, or they sold off-the-shelf products that could not be tailored to the specific needs of a client. Palantir was building an off-the-shelf product that could also be customized. Despite his lack of technical trainingor, perhaps, because of itKarp had also come up with a novel idea for addressing worries about civil liberties: he asked the engineers to build privacy controls into the software. Gotham was ultimately equipped with two guardrailsusers were able to access only information that they were authorized to view, and the platform generated an audit trail that indicated if someone tried to obtain material off-limits to them. Karp liked to call it a Hegelian remedy to the challenge of balancing public safety and civil liberties, a synthesis of seemingly unreconcilable objectives. As he told Charlie Rose during an interview in 2009, It is the ultimate Silicon Valley solution: you remove the contradiction, and we all march forward. In the end, it took Palantir around three years, lots of setbacks, and a couple of near-death experiences to develop a marketable software platform that met these parameters. There were moments where we were like, Is this ever going to see the light of day? Gesher says. The work was arduous, and there were times when the money ran short. A few key people grew frustrated and talked of quitting. Palantir also struggled to win converts at the CIA. Even though In-Q-Tel was backing Palantir, analysts were not obliged to switch to the companys software, and some who tried it were underwhelmed. But in what would become another pattern in Palantirs rise, one analyst was not just won over by the technology; she turned into a kind of in-house evangelist on Palantirs behalf. Sarah Adams discovered Palantir not at Langley, but rather on a visit to Silicon Valley in late 2006. Adams worked on counterterrorism, as well, but in a different section. She joined a group of CIA analysts at a conference in the Bay Area devoted to emerging technologies. Palantir was one of the vendors, and Stephen Cohen demoed its software. Adams was intrigued by what she saw, exchanged contact information with Cohen, and upon returning to Langley asked her boss if her unit could do a pilot program with Palantir. He signed off on it, and a few months later, Adams and her colleagues were using Palantirs software. Adams says that the first thing that jumped out at her was the speed with which Palantir churned data. We were a fast-moving shop; we were kind of the point of the spear, and we needed faster analytics, she says. According to Adams, Palantirs software also had a smartness that Analysts Notebook lacked. It wasnt just better at unearthing connections; even its basic search function was superior. Often, names would be misspelled in reports, or phone numbers would be written in different formats (dashes between numbers, no dashes between numbers). If Adams typed in David Petraeus, Palantirs search engine would bring up all the available references to him, including ones where his name had been incorrectly spelled. This ensured that she wasnt deprived of possibly important information simply because another analyst or a source in the field didnt know that it was Petraeus. Beyond that, Palantirs software just seemed to reflect an understanding of how Adams and other analysts did their jobsthe kind of questions they were seeking to answer, and how they wanted the answers presented. She says that Palantir made my job a thousand times easier. It made a huge difference. Her advocacy was instrumental in Palantir securing a contract with the CIA. Similar stories would play out in later deploymentsone employee would end up championing Palantir, and that persons proselytizing would eventually lead to a deal. But the CIA was the breakthrough: it was proof that Palantir had developed software that really worked, and also the realization of the ambition that had brought the company into being. Palantir had been founded by Peter Thiel for the purpose of assisting the U.S. government in the war on terrorism, and now the CIA had formally enlisted its help in that battle. Palantirs foray into domestic law enforcement was an extension of its counterterrorism work. In 2007, the New York City Police Departments intelligence unit began a pilot program using Palantirs software. Before 9/11, the intelligence division had primarily focused on crie syndicates and narcotics. But its mandate changed after the terrorist attacks. The city tapped David Cohen, a CIA veteran who had served as the agencys deputy director of operations, to run the unit, and with the citys blessing, he turned it into a full-fledged intelligence service employing some one thousand officers and analysts. Several dozen members of the team were posted overseas, in cities including Tel Aviv, Amman, Abu Dhabi, Singapore, London, and Paris. The rationale for the N.Y.P.D.s transformation after September 11th had two distinct facets, The New Yorkers William Finnegan wrote in 2005. On the one hand, expanding its mission to include terrorism prevention made obvious sense. On the other, there was a strong feeling that federal agencies had let down New York City, and that the city should no longer count on the Feds for its protection. Finnegan noted that the NYPD was encroaching on areas normally reserved for the FBI and the CIA but that the federal agencies had silently acknowledged New Yorks right to take extraordinary defensive measures. Cohen became familiar with Palantir while he was still with the CIA, and he decided that the companys software could be of help to the intelligence unit. In what was becoming a familiar refrain, there was internal resistance. For the average cops, it was just too complicated, says Brian Schimpf, one of the first forward-deployed engineers assigned to the NYPD. Theyd be like, I just need to look up license plates, bro; I dont need to be doing these crazy analytical processes. IBMs technology was the de facto incumbent at the NYPD, which also made it hard to convert people. Another stumbling block was price: Palantir was expensive, and while the NYPD had an ample budget, not everyone thought it was worth the investment. But the software caught on with some analysts, and over time, what began as a counter terrorism deployment moved into other areas, such as gang violence. This mission creep was something that privacy advocates and civil libertarians anticipated. Their foremost worry, in the aftermath of 9/11, was that innocent people would be ensnared as the government turned to mass surveillance to prevent future attacks, and the NSA scandal proved that these concerns were warranted. But another fear was that tools and tactics used to prosecute the war on terrorism would eventually be turned on Americans themselves. The increased militarization of police departments showed that defending the homeland had indeed morphed into something more than just an effort to thwart jihadis. Likewise, police departments also began to use advanced surveillance technology. Andrew Guthrie Ferguson, a professor of law at George Washington University who has written extensively about policing and technology, says that capabilities that had been developed to meet the terrorism threat were now being redirected on the domestic population. Palantir was part of this trend. In addition to its work with the NYPD, it provided its software to the Cook County Sheriffs Office (a relationship that was part of a broader engagement with the city and that would dissolve in controversy). However, it attracted much of its police business in its own backyard, California. The Long Beach and Burbank Police Departments used Palantir, as did sheriff departments in Los Angeles and Sacramento counties. The companys technology was also used by several Fusion Centers in Californiathese were regional intelligence bureaus established after 9/11 to foster closer collaboration between federal agencies and state and local law enforcement. The focus was on countering terrorism and other criminal activities. But Palantirs most extensive and longest-lasting law enforcement contract was with the Los Angeles Police Department. It was a relationship that began in 2009. The LAPD was looking for software that could improve situational awareness for officers in the fieldthat could allow them to quickly access information about, say, a suspect or about previous criminal activity on a particular street. Palantirs technology soon became a general investigative tool for the LAPD. The department also started using Palantir for a crime-prevention initiative called LASER. The goal was to identify hot spotsstreets and neighborhoods that experienced a lot of gun violence and other crimes. The police would then put more patrols in those places. As part of the stepped-up policing, officers would submit information about people they had stopped in high-crime districts to a Chronic Offenders Bulletin, which flagged individuals whom the LAPD thought were likely to be repeat offenders. This was predictive policing, a controversial practice in which quantitative analysis is used to pinpoint areas prone to crime and individuals who are likely to commit or fall victim to crimes. To critics, predictive policing is something straight out of the Tom Cruise thriller Minority Report, in which psychics identify murderers before they kill, but even more insidious. They believe that data-driven policing reinforces biases that have long plagued Americas criminal justice system and inevitably leads to racial profiling. Karp was unmoved by that argument. In his judgment, crime was crime, and if it could be prevented or reduced through the use of data, that was a net plus for society. Blacks and Latinos, no less than whites, wanted to live in safe communities. And for Karp, the same logic that guided Palantirs counterterrorism work applied to its efforts in law enforcementpeople needed to feel safe in their homes and on their streets, and if they didnt, they would embrace hard-line politicians who would have no qualms about trampling on civil liberties to give the public the security it demanded. Palantirs software, at least as Karp saw it, was a mechanism for delivering that security without sacrificing privacy and other personal freedoms. However, community activists in Los Angeles took a different view of Palantir and the kind of police work that the company was enabling. An organization called the Stop LAPD Spying Coalition organized protests and also published studies highlighting what it claimed was algorithmic-driven harassment of predominantly black and Latino neighborhoods and of people of color. LASER, it said, amounted to a racist feedback loop. In the face of criticism, the LAPD grew increasingly sensitive about its predictive policing efforts and its ties to Palantir. [Photo: Ryoji Iwata/Unsplash] To Karp, the fracas over Palantirs police contracts was emblematic of what he saw as the lefts descent into mindless dogmatism. He said that many liberals now seemed to reject quantification of any kind. And I dont understand how being anti-quantitative is in any way progressive. Karp said that he was actually the true progressive. If you are championing an ideology whose logical consequence is that thousands and thousands and thousands of people over time that you claim to defend are killed, maimed, go to prisonhow is what Im saying not progressive when what you are saying is going to lead to a cycle of poverty? He conceded, though, that partnering with local law enforcement, at least in the United States, was just too complicated. Police departments are hard because you have an overlay of legitimate ethical concerns, Karp said. I would also say there is a politicization of legitimate ethical issues to the detriment of the poorest members of our urban environments. He acknowledged, too, that the payoff from police work wasnt enough to justify the agita that came with it. And in truth, there hadnt been much of a payoff; indeed, Palantirs technology was no longer being used by any U.S. police departments. The New York City Police Department had terminated its contract with Palantir in 2017 and replaced the companys software with its own data analysis tool. In 2021, the Los Angeles Police Department had ended its relationship with Palantir, partly in response to growing public pressure. So had the city of New Orleans, after an investigation by The Verge caused an uproar. But Palantir still had contracts with police departments in several European countries. And since 2014, Palantirs software has been used in domestic operations by U.S. Immigration and Customs Enforcement, work that has expanded under the second Trump administration, and earned criticism from a number of former employees. In 2019, when I was working on my story about Palantir for The New York Times Magazine, I tried to meet with LAPD officials to talk about the companys software, but they declined. Six years earlier, however, a Princeton doctoral candidate named Sarah Brayne, who was researching the use of new technologies by police departments, was given remarkable access to the LAPD. She found that Palantirs platform was used extensivelymore than one thousand LAPD employees had access to the softwareand was taking in and merging a wide range of data, from phone numbers to field interview cards (filed by police every time they made a stop) to images culled from automatic license plate readers, or ALPRs. Through Palantir, the LAPD could also tap into databases of police departments in other jurisdictions, as well as those of the California state police. In addition, they could pull up material that was completely unrelated to criminal justicesocial media posts, foreclosure notices, utility bills. Via Palantir, the LAPD could obtain a trove of personal information. Not only that: through the network analysis that the software performed, the police could identify a person of interests family members, friends, colleagues, associates, and other relations, putting all of them in the LAPDs purview. It was a virtual dragnet, a point made clear by one detective who spoke to Brayne. Lets say I have something going on with the medical marijuana clinics where theyre getting robbed, he said. I can put in an alert to Palantir that says anything that has to do with medical marijuana plus robbery plus male, black, six foot. He readily acknowledged that these searches could just be fishing expeditions and even used a fishing metaphor. I like throwing the net out there, you know? he said. Braynes research showed the potential for abuse. It was easy, for instance, to conjure nightmare scenarios involving ALPR data. A detective could discover that a reluctant witness was having an affair and use that information to coerce his testimony. There was also the risk of misconduct outside the line of dutyan unscrupulous analyst could conceivably use Palantirs software to keep tabs on his ex-wifes comings and goings. Beyond that, millions of innocent people were unknowingly being pulled into the system simply by driving their cars. When I spoke to Brayne, she told me that what most troubled her about the LAPDs work with Palantir was the opaqueness. Digital surveillance is invisible, she said. How are you supposed to hold an institution accountable when you dont know what they are doing? Adapted from The Philosopher in the Valley: Alex Karp, Palantir, and the Rise of the Surveillance State by Michael Steinberger. Copyright 2025. Reprinted by permission of Avid Reader Press, an Imprint of Simon & Schuster Inc.
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