The 1 Big Compensation Decision Founders Should Never Hand to AI

AI has quickly made itself useful across hiring. It can sift through résumés, draft a job description in seconds, and match a role to market benchmarks. For a small company that never had a compensation team, that’s a genuine unlock. But things get thornier when AI has a say in what the actual salaries that companies pay their employees should be. And that’s the bigger question that founders now have to grapple with—whether they should let AI set the number itself. That pressure is only growing, and this year it found a sharp edge in the law. On October 8, 2025, California outlawed a hiring shortcut that employers had leaned on for years. When Governor Gavin Newsom signed SB 642 into law, effective January 1, 2026, the state redefined what a “pay scale” in a job posting is allowed to be: a good-faith estimate of what an employer expects to pay a new hire. The point, as the law firm Morgan Lewis noted, was to stop employers from posting “meaningless pay scales simply to be in compliance.” So the old dodge is dead. Post a range wide enough to protect yourself, say from $80,000 to $200,000, and you’re not being cautious. You’re non-compliant. California isn’t alone, and these rules don’t only apply to big companies. As of early 2026, more than a dozen states now require employers to disclose pay, and the thresholds reach startups, not just giants. In Colorado, a single employee is enough to trigger disclosure, while New York City draws the line at four; states like California, Illinois, and Washington set it at 15. For founders who have always set pay based on their instincts, that guess is now a public document, and the person most likely to read it closely is the employee who already holds the job. And not just them: everyone doing similar work will see the number too, and measure themselves against it. The problem was always there Improvised pay stays invisible while it works, and it works right until two people doing the same job discover they earn thousands apart, or a strong hire leaves for a competitor who bothered to benchmark the role when you didn’t. Jonas P. Johnson, who works on compensation modeling at ERI Economic Research Institute, a firm that has built salary data for more than three decades, says instinct-based pay produces one of two problems, and they aren’t equally fixable. Underpaying firms struggle with turnover, while overpaying firms struggle to keep prices competitive. Most founders assume overpaying is the safe mistake. Johnson says it’s the harder one to escape. Underpaying is the easier hole to climb out of, because raising pay tends to slow the turnover it caused. Overpaying is the real trap, since you can’t take money back once people are earning it. “Employees won’t accept pay cuts,” Johnson says, so a company that has drifted above market has to hold raises below average for years to come back down, all while trimming costs to survive. The case he sees most is the firm that bought out a competitor and inherited its payroll, absorbing someone else’s guesswork and making it their own. How one raise becomes a chain reaction Transparency rules do more than reveal the gap between what you pay and what the market pays. They reveal the distance between your newest hire and your most loyal one, which is the first comparison employees make. The way it unfolds is easy to trace. When market rates climb, you post a higher range to attract someone new, and that range is now visible to the person who has held the job well for three years, and to their peers in similar roles. Fresh money at the top squeezes the space between new and existing staff, and Johnson warns it “can lead to turnover among existing employees unless the organization re-scales the entire salary structure.” A single number in one posting can force you to reopen everyone’s. Compensation professionals call this pay compression, and it hits morale before it shows up in a budget. Jason Greer, an employee and labor relations expert, says a compressed structure creates “zombie employees” who “show up, clock in, and do the minimum.” They haven’t walked out because they have learned what a newer colleague earns and have reduced their effort. His advice is to understand your local market before circumstances force the issue, because “you might be global, but to your employees, you’re local.” Why the average raise misleads you Guesswork is seductive because there’s always a tidy number to reach for. Heading into 2026, most surveys cluster raises between 3.2 and 3.5 percent, down from post-pandemic highs near 4.4 percent. But the average describes almost no one. Some jobs see no salary growth year over year, Johnson says, while others climb 8 percent. Pay tends to lurch rather than climb, and his clearest example comes from the start of the pandemic, when compensation for forklift operators had been flat for years and then jumped 22 percent in two months as supply chains seized. Budget off the blanket figure, and you underpay the very people you can least afford to lose. The one call you shouldn’t hand to AI None of this complexity means a 30-person company needs the machinery of a Fortune 500. A large firm leans on formal pay grades to keep several hundred jobs consistent, because at that scale nobody can price every role by hand. A 40-person company has maybe 20 distinct roles and can price each against the market directly, without collapsing them into bands. The point isn’t to build an elaborate structure, since none of this needs a complicated setup. You just have to know what a job is really worth before putting a number on it. What makes that reachable now is that the grunt work has gotten cheap. Matching your internal roles to market data once meant hiring a consultant for weeks. Today, the same first pass takes about a day using AI tools. But Johnson is clear that speeding up the work does not mean handing over the decision. Software can widen the field of comparable jobs and flag where your pay drifts out of line, yet “all final decisions should be made by humans,” he says, because an AI model will always return a confident number even when the data behind it is too thin to trust. Workforce strategist Terri Gallagher pushes the point even further, arguing that compression isn’t a pay problem at its root, but instead “a workforce strategy issue.” The number you choose shows what you think the work is worth, and no tool can decide that for you. The answer, then, isn’t to keep AI away from pay. Let it do the first pass, gather the comparisons, and show you where you’ve drifted. Then make the final call yourself, because that judgment is the one part of the job that was always yours. The expensive answer For most of the past decade, “we’ll figure out compensation as we grow” was a reasonable thing to say. The cost of having no system stayed hidden; a little attrition here, a bruised ego there, all of it absorbed into the churn of a growing company. Transparency law takes that hiding place away. The guess is a public document now, the compression is plain to the people it costs, and the tools to do the work properly have never been more affordable. A founder who treats all this as a compliance headache will keep patching it one uncomfortable job posting at a time. But the one who treats it as a reason to finally learn what each role is worth will find the law was never the real issue. The real issue was always whether they knew what they were paying for. EXPERT OPINION BY KOLAWOLE ADEBAYO

Generative AI Is Fading. Here Are 4 Bets on What’s Next in Tech

The thing about AI is you have to be at the forefront of it for a lot of the AI noise to make sense. Like, it’s not that you have to believe in AGI, artificial general intelligence, computers that think. I don’t. You just have to ask yourself what the people who believe in AGI are betting on next. TL;DR: Robots! Finally! Hope they’re not the killer kind! Let’s take a semi-serious look at where AI is heading as we tire of the generative AI phase and settle comfortably into the data-as-moat phase. What comes next? This is what I’m seeing and hearing. One Robot to Rule Them All This was always gonna end in killer robots overthrowing us humans, right? From my college days, I loved robots. The closest I could get to robots was industrial engineering — simulation, robotics, manufacturing lines — really boring robot stuff, but I loved robots the way some kids love trucks and other kids love accounting. What, you think your accountant doesn’t get psyched over a good 1120-S? I keep telling my daughter at Arizona State she needs to kidnap one of those food-delivering Starships and reprogram it to trek all the way across the country to our home, and then do my bidding. But she never does this, she just studies and stuff. What the robot revolution really means is the abstraction of language models into their own layer to do the human interpreting and talking, while the physical or world model takes inputs from its environment and does the spatial thinking. As this gets more mainstream, expect more AI in our physical world. That’s what they’re saying. But let’s read between the lines here. Killer overlord robots are a couple of months away, maybe a year. AI Shopping Finds Somewhere to Land, and Then Shop Agentic commerce is at a crossroads as the next big AI bet, and it has been sitting at that crossroads for some time, at least a couple of years. It needs to figure out what it’s going to be, and a lot of smart people, including several friends of mine, are taking bets. Consumer agentic commerce is a … mess. I wrote a long time ago that the ideal customer profile (ICP) for agentic commerce on the consumer side is the exact person who gets more benefit out of the act of shopping than the spoils that the shopping produces. Seriously, like 79 people read that post and it was damn funny. But I don’t think I’m taking a huge psychological leap here either. That was back when agentic commerce had its first run at the public during the holiday season of 2025. What’s coming next is the revised, newer, better Agentic Commerce 2: Electric Boogaloo. After the fart-in-church of the initial agentic commerce rollout, the major players stopped trying to be the Amazon of agentic commerce (except Amazon, of course), and they’re working on standards and protocols to underpin the experience and make it less scary and chaotic. They’re expecting agentic commerce to not only be a reality, but an expectation, over the next 12 months. So like I said, protect those credit cards, kids. One of the main reasons I believe agentic commerce v1 ate itself like Chris Knight’s laser sample was because none of us were ready to tell the internet to buy whatever it thought we wanted. I feel like we’re all getting more comfortable with that. Except for those habitual shoppers, but tech finds a way to beat down even the hardest of hardcore skeptics. I mean everyone was on Facebook for a minute there, right? The Intelligence Moat Replaces the Data Moat This is probably the bet I’m most interested in, because it’s a direct callback to what we were doing with generative AI back in 2010 with small language models and very tight structured data sets that allowed us to slather unstructured data on top like barbecue sauce. I had, like, five different jokes to put right here but none of them worked. Three of them included the word sticky. And this is also the bet that most directly follows the data-as-moat phase, because data is all ones and zeros, it’s what you do with that data that matters. As LLMs continue to try to be all things to all people and get bigger and stronger, we’re already seeing way too much fluff coming out of their general use. Context matters. It really matters when it comes to data, so a lot more individual work is going to go into the intelligence layer before the first interaction between user and agent (or more likely agent and agent) happens. In other words, it won’t be enough of a moat to just have a beautiful data lake. Once the data is wrangled, the enterprise context engine will need to be built on top of it, before it hits an AI model, large language or otherwise. Governance, Wrangling Agents, and a Bubble Pops Obviously, governance is going to become a bigger factor and get more formalized as more people adopt and integrate more AI. When I was doing governance projects last year and earlier this year, the people I was talking to about the need for governance were all like, “Whaaaaa?” and got glazed-over looks in their eyes. I ended up explaining it like this: “You don’t want those robots to actually kill anybody or accidentally put five-figure charges on someone’s credit card, right?” “Of course not.” “Well, with AI and automation, you have to do something about that preemptively.” With more governance and guardrails comes the wrangling of AI agents. Especially in the near term. We’re already way beyond what humans can successfully orchestrate with agents and, to get a little buffoon technical, even the orchestration layers are getting too complex. We’re moving into “systems of agents” for software models, and my read on this is that agent orchestration will be run by agents, up and up levels to the human at a top level. I think. And then one last word of doom. I’ve been talking about the overspend on AI capex for over a year. If a handful of the BigCos (Amazon, Meta, Google, etc.) are spending over a trillion dollars on infrastructure, and we keep getting pushback on data centers, and we don’t control the materials that make the chips which we’re dangerously short on, well, that ROI bubble is gonna pop at some point. But we’ll burn that bridge when we get to it. In the meantime, I’d encourage you to always be thinking ahead on this AI stuff. You don’t have to be a first mover in any of it, but know where the crazy money is heading, and try to figure out what’s real and what’s not. I’ll be here making fun of all of it if you need a break. EXPERT OPINION BY JOE PROCOPIO, FOUNDER, JOEPROCOPIO.COM @JPROCO

How AI is Transforming Supplier Management

AI in supply chain and procurement has shifted from experimental pilots to operational necessity, transforming workflows and team structures across the supply chain function. Operations that experimented with AI tools a few years ago now face a different reality. The technology has moved from optional pilot projects to standard operational requirements. AI systems are transitioning from copilot assistance to autonomous agents that execute multi-step workflows within procurement platforms. This change affects every area of the function and its connection to supply chain operations. AI applications in procurement Systems now read contracts, assess supplier risk, route requests according to policy and resolve data discrepancies between platforms. These tasks often require minimal human oversight and integrate directly into enterprise systems. Work that previously required days can be completed in hours. Procurement's capacity to identify patterns and anomalies across large volumes of data continues to expand. Supply chain visibility could improve as AI tools process more supplier information and transaction data. The technology may enable faster responses to disruptions and better coordination across procurement and logistics teams. Barriers to implementation A large percentage of procurement leaders are implementing or plan to implement AI agents within the next 12 months. Data quality remains the single biggest barrier to adoption. Privacy and compliance concerns follow as the second obstacle. Technology and process complexity present additional challenges. Internal skills gaps also limit implementation. Teams need new capabilities to work alongside AI systems and interpret their outputs. Legacy systems could hold back organisations that do not invest in data foundations and updated infrastructure. The gap between early adopters and those relying on older platforms may widen. Changing team structures Most industry voices agree AI is not replacing procurement professionals. The technology is elevating them by removing low-value, manual work. AI is expected to increase efficiency and free procurement professionals to focus on higher-impact activity. This includes strategic supplier relationship management and supply chain risk assessment. Teams that build appropriate structures, skills and data foundations now will be better positioned to capture value. Supply chain resilience could benefit from procurement teams equipped to use AI for supplier monitoring and demand forecasting.

This Font Looks Perfectly Normal to Humans but Wreaks Havoc on AI

A new “AI-proof” font was designed to be hard for AI agents to scrape, but you can’t tell by just looking at it. Unlike other anti-AI fonts that use letters that are difficult for bots to read, ShieldFont swaps out words behind the scenes to poison the data that automated scrapers take without permission. ShieldFont was designed as part of a project created by a group of professionals including the Brazilian creative studio Seneda & Abrucio and the Danish type foundry PlayType. It shields text from large language models (LLMs) by garbling sentences in the HTML source code, leaving automated scrapers to sift through sentences filled with decoy words that make a sentence incoherent. Thanks to a custom font on a backend, though, the real text is displayed for a human reader to see. For example: A sentence that originally reads “The knight rode his horse into battle” is altered in the source code so it’s scraped by a bot to read “The knight rode his engine into battle.” Since LLMs group words and phrases that are likely to be used together, the quality of its output is degraded if it scrapes a lot of jumbled text like this. The goal, according to two of ShieldFont’s creators, Isaque Seneda and Gabriel Abrucio, is to push back against unauthorized LMM scraping by making it harder and costlier to do so. How it works ShieldFont works using ligatures, the technical term in typography when two letters next to each other in a word are combined into a single glyph. Ligatures are designed for aesthetics, so letter combinations like fi in “fish” or fl in “flow” look naturally spaced instead of visually cluttered. When a program sees these specific letters next to each other, it swaps two characters for one that combines the letters into a single glyph. ShieldFont works in a similar way, except instead of letters, it swaps out whole words. “We didn’t invent a new font capability, just pointed to an old one that hadn’t been used this way before,” Felipe Petroni, a creative director who was part of the project’s leadership, tells Fast Company. Determining which words to swap out was tricky, since doing so at random results in gobbledygook phrasing that bots reject outright. The key was changing the meaning of sentences and phrases, not just words, so bots would still accept the text, resulting in a “poisoned” version of the scraped data. The thinking goes that if there are enough of these sorts of digital speed bumps, it will raise the cost of illegal scraping and those who build LLMs will opt to pay for what they take instead. To determine which words to swap, ShieldFont’s creators made a do-not-swap list of 113 words that included things like pronouns, articles, conjunctions, prepositions, negations, quantifiers, and every form of be, have and do. Instead, it swaps out adjectives, adverbs, nouns, and verbs. Dates and numbers also get scrambled. Automated scrapers can get around ShieldFont by taking a picture of the text to view it as a human does, but that also raises the price of scraping at scale. Rather than scraping plain text files quickly and cheaply, it has to photograph the page and conduct image recognition. Seneda and Abrucio’s white paper notes that ShieldFont introduces some friction for humans too, as search engines index the decoy text, so publishers would have to use ShieldFont for content that doesn’t depend on search traffic. The substitute words also show up in translation tools and screen readers, and when users copy and paste. Petroni and his team began prototyping ShieldFont in October 2025, and they partnered with the type foundry PlayType this March to develop a ready-to-use typeface with the ligatures called ShieldFont Optik. The protocol for ShieldFont is open-source and type-face agnostic. Petroni says they made implementation for ShieldFont as low-friction as possible, and it comes with three public mappings, so the word “wrote,” for example, becomes either “sang,” “wrought,” or “labeled,” depending on whether it’s using its alpha, beta, or gamma dictionary. “That’s deliberate; a decoder built for one mapping doesn’t work for another, and private mappings have to be identified and reversed one by one,” he says. “Scrapers can’t know in advance whether a site uses ShieldFont or which mapping it uses. Across millions of pages, that added cost is the point.” ShieldFont is a font system designed for human reading, not bots. Instead of using visual tricks to make it harder to read, it confuses the bots in the code and leaves the human reading experience alone. By Hunter Schwarz