This post is different from my usual writing. I normally talk to engineers, product teams, CTOs — people who build things. Today, I’m broadening the conversation. If you run a business, sit on a board, manage a portfolio, or advise someone who does — this is for you. And if you’re a tech founder building AI solutions for businesses — this is the argument that makes your offering ten times more compelling than “we’ll save you headcount.” I’m stepping outside my usual tech lane deliberately, because from where I stand, the AI conversation is dominated by consultants selling transformation programs, vendors selling platforms, and media selling headlines. It’s the same transformation playbook they ran for big data, repackaged with an AI label. AI can do things most people haven’t begun to imagine — but you only start seeing what’s possible when you work with it day and night.
So here’s my pitch. I’ve spent 35 years building software systems in Silicon Valley. The last several years, I’ve been helping businesses integrate AI into their operations — not as a science project, but for measurable results. I’ve written extensively about the technical side, but I won’t burden you with jargon today. What I want to share is something I keep seeing from the trenches: most companies are using AI to cut costs when there’s a much bigger fish to fry.
The bigger fish is revenue. Growth. Scale. Not just improving margins on the same business — multiplying the business itself. And the path to it is counterintuitive enough that almost nobody in the media is talking about it.
I won’t be covering basic AI applications like writing emails, summarizing meetings, or generating marketing copy. You’ve figured those out. Shopify’s CEO Tobi Lütke recently told his entire company that AI usage is now a baseline expectation — part of performance reviews, a prerequisite before requesting additional headcount. Shopify grew revenue 30% last year. The basics are settled.
The frontier is Agentic AI — systems that don’t just assist a person in real time, but take on entire workflows autonomously. An AI agent can spend hours on a task. It can make phone calls, review documents, coordinate with vendors, analyze contracts. It doesn’t augment your employee’s work. It displaces a portion of it. And how you handle that displacement is the single most consequential AI decision your business will make in the next twelve months — if you’re lucky enough to have that long.
Premature
Remember the novel you read back in school days? The landowners send tractors to replace tenant farmers. One man on a tractor can do the work of twelve families. Three dollars a day. Nothing personal.
The landowners ran the numbers and won. Except they didn’t. Nobody accounted for the knowledge the farmers carried — when to plant, when to rest the soil, how to read the weather. Nobody accounted for the community that held the local economy together. Twelve families out, one machine in, savings pocketed — and the ecosystem collapsed around them.
Steinbeck. Grapes of Wrath, 1939. That was ninety years ago.
2020. Boeing lost roughly 15,000 workers in Washington state alone — about 21% of its workforce — through retirements, layoffs, and buyouts. Today, only a quarter of Boeing’s factory workforce has more than a decade of experience. New hires navigate production without the generational knowledge that experienced mechanics relied on — the unwritten rules, the judgment calls, the instinct for when something looks wrong before it shows up in a report. The quality crises and safety incidents that followed are well-documented. You can draw a straight line from knowledge loss to failures that cost billions and made global headlines.
2025. NASA shed roughly 4,000 employees — 20% of its workforce — to budget cuts. By March 2026, it launched a massive recruiting drive to bring back the expertise it had just let walk out the door.
The pattern hasn’t changed in a century. Cut the people, lose the knowledge, pay for it later.
Now it’s happening again — this time with AI. Replacing humans with AI dominates the headlines. Some companies aren’t even waiting for the technology to prove itself. According to Harvard Business Review, companies are laying off workers based on AI’s potential — not its performance. A survey of over a thousand executives found that only about 2% of organizations reported layoffs tied to actual AI implementation. The other 98% are cutting staff in anticipation of a future they haven’t tested.
Let those numbers sink in.
And here’s the thing — this isn’t a technology problem. It’s a strategy problem. The knowledge in your employees’ heads is an asset that doesn’t appear on the balance sheet. It’s the judgment calls your operations manager makes at 2 AM when a vendor falls through. It’s the instinct your senior loan officer has for which applications smell wrong. It’s the relationships your account executive built over a decade that no CRM can replicate. Fire those people, and you don’t just lose labor. You lose the very thing that made the labor valuable.
I’m an engineer, and here’s something I can tell you from experience. AI doesn’t have the common sense your staff earned over years on the job. I’ve written about this at length — the short version is that AI was trained on internet data, not on lived experience in your industry. It doesn’t know what your senior people know. It can’t exercise the judgment they exercise. It will confidently handle the 80% of cases that are routine. But the remaining 20% — the edge cases, the exceptions, the situations that require weighing tradeoffs no manual anticipated — that’s where it falls apart. And in most businesses, the 20% is where the real risk lives.
The Success Story That Wasn’t
I covered Klarna’s story in an earlier post. But it’s worth revisiting here, because it’s the clearest example of what happens when you optimize for the cost line and ignore everything else.
Between 2022 and 2024, Klarna replaced roughly 700 customer service agents with AI. They projected $40 million in annual savings. The headlines were glowing. AI handles two-thirds of all conversations. Efficiency through the roof.
Within a year, customer satisfaction had deteriorated. Complaints surged. The CEO, Sebastian Siemiatkowski, publicly admitted: “We focused too much on efficiency and cost. The result was lower quality, and that’s not sustainable.”
Klarna started rehiring. Recruiting, onboarding, training new staff — at costs that exceeded the original savings. And the experienced agents who’d been let go? They were gone. The institutional knowledge they carried about customer patterns, edge cases, and complex disputes walked out the door and didn’t come back.
Klarna isn’t an outlier. According to industry data, 55% of companies that made AI-driven layoffs report regret. A third of them lost critical skills and expertise they’re now scrambling to rebuild.
The problem wasn’t that AI couldn’t handle customer service. It could — for the routine cases. The problem was treating cost savings as the endgame. Klarna solved for the average case and failed on the hard ones. The hard ones are where customer loyalty lives.
Flipping the Script
So if replacing people is a trap, what’s the alternative?
Here’s the approach I advocate, and it’s the one I see working in the field: don’t replace your people. Shift them.
When AI takes over the routine, predictable portion of the work, your experienced employees don’t disappear. They shift to the work AI can’t handle — the complex judgment calls, the edge cases, the situations where common sense and domain expertise matter. The work that AI can’t do reliably.
This isn’t theoretical. Built Technologies deployed an AI agent for construction loan administration. Each loan draw review used to take about 90 minutes — a human checking lien waivers, inspection reports, insurance certificates, compliance documents across multi-page PDF packets. Tedious, high-stakes, and repetitive.
The AI agent now completes those reviews in about 3 minutes. Accuracy above 99%. Risk detection 400% better than human-led reviews. And here’s the part that matters for this conversation: the lenders handling those loans didn’t fire anyone. The same team now processes ten times the volume. The humans shifted to exception handling — the draws that don’t fit neat categories, the vendor disputes that require judgment, the compliance edge cases that require someone who’s seen a thousand loans and knows when something is off.
That shift — from routine to judgment — unlocks four things simultaneously.
First, scale. Your team handles dramatically more volume with the same headcount. Revenue goes up. Payroll doesn’t. That’s not a cost cut — it’s a revenue multiplier. Built’s clients saw 300–500% ROI, and they achieved it by growing throughput, not by shrinking the team.
Second, knowledge retention. Your experienced people stay. The institutional knowledge stays with them. Every judgment call they make on an edge case is an opportunity to capture what they know — gradually, over time, in a way that can eventually drive future AI improvements. You’re not hemorrhaging expertise. You’re concentrating it where it matters most.
Third, pace control. You decide how fast to shift the ratio between human work and AI work. There’s no cliff edge. No big-bang deployment. No artificial deadline. Your employees gradually handle a larger share of complex work and a smaller share of routine work. If the AI stumbles on a category of task, the human is still there. You roll back that piece, fix it, and try again.
Fourth, risk mitigation. You never deploy more automation than you’ve tested and validated. Every step is reversible. The human is the safety net. And because the experienced staff are still in place, you have the people who can evaluate whether the AI is doing its job correctly — because they did that job themselves for years.
That’s the approach. The methods to get there vary widely. On one end, you have developers manually building automations around your workflows — traditional software engineering with AI components wired in by hand. On the other end — and this is the bleeding edge, where my own work is focused — AI observes your operations directly and builds the automations by itself, with minimal human involvement. Most businesses will land somewhere in between, and that’s fine. What matters is the direction, not the starting point.
The industry recently gave the technical architecture behind this a name — “harness engineering,” a term coined by Mitchell Hashimoto and adopted by OpenAI earlier this year. I’ve been building harnesses long before the term existed. If you want the details, talk to my AI twin — it’s read everything I’ve written. What matters at the board level is this: the technology exists to make this migration gradual, controlled, and measurable. It’s not a leap of faith.
Red Pill, Blue Pill
Say you’ve made the shift. AI handles the routine. Your people focus on judgment. Now you’re at a fork — what do you do with that freed capacity?
Option one: cut costs. You keep the current workload and reduce headcount. The math is simple. Payroll drops. EBITDA goes up by whatever you save. This is what most companies reach for, and it’s what dominates the consulting pitch decks.
Option two: grow. You keep the team and use the freed capacity to handle more. More clients. More markets. More products. More volume. Revenue goes up while your cost structure stays roughly flat.
Cost cuts are a legitimate play — sometimes the right one. But from what I’ve seen, they’re the default decision that doesn’t require a new strategy. You just do the same thing cheaper. The growth play takes more work — and it pays off differently.
Growth requires you to ask: what could we do if our team could handle five times the volume? What new markets, segments, or services are now within reach?
Consider language expansion. Your AI agents can operate in any language you need — simultaneously, without hiring local teams. A company that was limited to English-speaking markets can now serve Spanish, Portuguese, French — with the same operational team handling the edge cases that require human judgment. That’s not a theoretical capability. It’s happening today.
Or consider coverage. An AI agent works 24 hours a day, 7 days a week. If your business was previously limited by the hours your team could cover, you’ve just removed that constraint. Same team. Three times the availability. New customer segments that weren’t reachable before.
I’m not going to tell you which growth play fits your business. That depends on your market, your customers, your competitive position. But I will say this: if your entire AI strategy starts and ends with headcount reduction, you’re leaving the bigger opportunity on the table.
Cursor reached $1 billion in annual revenue with about 60 people. Midjourney generates over $500 million with 40 employees and zero external funding. But those are extreme examples — AI-native companies built from scratch with no legacy operations. They’re different animals. What’s relevant is that traditional businesses are finding the same multiplier logic within their existing operations. Construction lenders processing ten times the loan volume. CPG brands recovering millions in revenue from deductions that used to slip through the cracks. Manufacturers squeezing 20% more throughput from the same plant. That’s the signal.
The Ghost in the Machine
But workforce isn’t the only lever. There are ways to use AI for business improvement that have nothing to do with automating people. Let me give you one.
PepsiCo recently partnered with Siemens and NVIDIA to create AI-powered digital twins of their manufacturing facilities. A digital twin is a virtual replica of a physical operation — every machine, every conveyor, every pallet route, every operator path — recreated with physics-level accuracy.
What does that buy you? PepsiCo deployed this at a Gatorade plant. Within three months, they achieved a 20% increase in throughput. Same plant. Same equipment. Same people. The AI simulated changes, identified bottlenecks, and tested solutions in the virtual environment — catching 90% of potential issues before anything was touched in the real world. They also reported 10–15% reductions in capital expenditure by uncovering hidden capacity they didn’t know they had.
Twenty percent more throughput means twenty percent more product out the door. That’s revenue, not savings. And they achieved it without firing a single person or reorganizing a single team. They just used AI to see their own operation more clearly than they could before.
Digital twins aren’t new. But using AI to build and optimize them — that’s a recent development. And it applies far beyond manufacturing. Logistics companies use them to optimize routes. Retailers use them to simulate store layouts. Hospitals use them to model patient flow. If you operate anything physical, the question isn’t whether a digital twin would help. It’s when you’ll build one.
And by the way — digital twins don’t have to clone a physical facility. You can clone knowledge. I mentioned my AI twin earlier. It’s an experiment I find fascinating: a digital replica not of machines and conveyors, but of what I know. If that intrigues you too, try it — ask it a technical question, as long as it’s in my wheelhouse. It’s a small example of a big idea.
A Connecticut Yankee
Bringing AI into a business can be incredible. But it can also be a trainwreck.
Remember Mark Twain’s A Connecticut Yankee in King Arthur’s Court? Hank Morgan, an engineer, travels back to sixth-century England and starts modernizing everything. Electricity, factories, newspapers — the full Industrial Revolution, delivered overnight. For a while, it’s miraculous. Efficiency gains beyond anything Camelot had ever seen.
But Morgan never accounted for the social fabric he was disrupting. The institutions, the relationships, the unwritten rules that held the society together — he steamrolled all of it in the name of progress. The backlash, when it came, destroyed everything he’d built. Twain’s point wasn’t that technology is bad. It was that technology deployed without respect for the human systems around it will fail — spectacularly, and at the worst possible time.
That’s the risk with AI in business, too. Not that the technology doesn’t work. But that you deploy it in a way that tears your organization apart. The tacit knowledge. The judgment. The culture. The relationships between people who’ve worked together for years and can finish each other’s sentences when something goes wrong at midnight.
Finding the right approach is what I’ve been focusing on since AI became usable for business — through building the Rishon platform, advising clients, and learning (often the hard way) what works and what doesn’t. And funny enough, the first answer isn’t always AI. Sometimes a business needs to fix its processes, its data, or its technology before AI can do anything useful. Deploying AI on top of a broken operation just gives you a faster broken operation.
If you have a case to discuss, I’d welcome the conversation. I’ve been wrong enough times to have useful scars, and got it right enough times to spot useful patterns. Ping me on LinkedIn, or use the contact page.
Cheers!