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In mid-June, the Norwegian government announced that primary school students would essentially stop using generative AI starting this fall. A few months earlier, in Syracuse, New York, the law firm Barclay Damon adopted an internal rule: For their first three years, junior lawyers must produce their work without AI, and only then check it against the tool.
A Nordic public school system and a 300-person American law firm aren’t pursuing the same goals. But they arrived at the same conclusion: Beginners must first learn without assistance in order to be, later on, well assisted.
Norway’s decision involves different stages
The word ban made the headlines, but Norway’s decision is more nuanced than that. From ages 6 to 13, students should, as a rule, not touch generative AI. From 14 to 16, they can use it cautiously, under a teacher’s supervision. From 17 to 19, they must learn to use it properly, to prepare for higher education and work.
“The most important thing in school is that our children learn to read, write, and count,” Prime Minister Jonas Gahr Støre summed up. The government also pledged to fund the return of paper books to classrooms—a reversal of 30 years of massive digital investment, in a country that adopted computers in schools as early as the 1990s and tablets by 2010.
Norway has run this experiment before. In 2024, it banned smartphones from schools in response to declining academic results. According to a study by the Norwegian Institute of Public Health, bullying decreased and grades went back up. Armed with that precedent, the government chooses to do it again with AI. Some foundational learning requires doing things yourself, slowly, with your own means.
The three-year rule
Barclay Damon transposes exactly the same logic into the workplace. At a New York State Bar Association panel, managing partner Connie Cahill laid out the firm’s policy: Every associate in their first three years must do their work without AI first. They turn in their draft, their outline, their memo—whatever the deliverable—and only then can they go verify and improve it with the tool. Doug Nash, who chairs the firm’s AI committee, explained why: Training the next generation of lawyers is already the hardest part of his job, and handing beginners a shortcut on day one deprives them of the path that builds professional judgment.
It’s worth measuring what this rule costs. A law firm bills by the hour. Every memo a junior spends three days drafting alone, when AI could produce an apparently good enough version in 10 minutes, is paid for in unoptimized hours. Barclay Damon chose to absorb that immediate efficiency loss because the firm needs, 10 years from now, senior partners capable of judging legal reasoning, including a machine’s.
Norway is making the same trade-off with public money: Buying books back and having children work unassisted means accepting a less “modern” school today in exchange for more solid adults tomorrow.
Learn first, delegate later
The order of operations matters. You learn your multiplication tables before you use a calculator. You log hours of supervised driving before taking the wheel alone. Civil aviation requires pilots to fly a mandatory number of manual hours, even when autopilot would do better, because the industry learned from several crashes what it costs to let skills atrophy.
For beginners, AI threatens the very foundations. A study by Microsoft Research and Carnegie Mellon showed that the more people trust AI, the less they exercise their critical thinking. Only those who are confident in their own skills dare question what the machine produces. In other words, to verify AI well, you need to have learned to work without it.
Those unassisted skills are also what you fall back on the day the tool goes away: a power outage, a discontinued service, a cyberattack on a data center, or a natural disaster. And climate change guarantees more of those.
Norwegian primary school students and first-year lawyers are in the same situation: They don’t yet have the necessary base. Giving them the tool too early removes the bottom rungs of the ladder and drains the pipeline of tomorrow’s experts.
Against resignation
The public debate about AI generally oscillates between two postures. On one side, calls for outright refusal, like this recent op-ed in French daily Le Monde urging a general boycott of generative AI. On the other, resignation: Since students will use it anyway, why bother resisting?
The examples of Norway and Barclay Damon point to a third way. Neither rejects the technology altogether. Norwegian high schoolers will learn to use it; fourth-year associates will use it daily. But both refuse resignation and decide to protect the learning phase, even if it means being less “efficient” in the short term.
This is an argument about robustness. As the French biologist Olivier Hamant argues, robust systems survive shocks precisely because they carry redundancies that look wasteful in normal times: two kidneys, backup metabolic pathways, spare capacity everywhere.
Nature is rarely optimized, and that is exactly what makes it resilient. People who know how to do the work without the machine are that redundancy, and an organization that optimizes them away becomes fragile by design.
Right now, organizations everywhere are rolling out training programs to get everyone up to speed on AI. It may be time to ask the opposite question: Where are the AI-free zones we need? The experts you’ll rely on in 10 years are the beginners you’re training, or not training, today.
Cubic zirconia looks like a diamond, but jewelers call it a simulant: an imitation that plays the part at a glance but does not survive close inspection. A lab-grown diamond, by contrast, has the same carbon structure, the same hardness, and cuts glass just as effectively as a mined one. These diamonds, sometimes called synthetic diamonds, aren’t imitations of diamonds; they simply are diamonds—just manufactured, rather than pulled out of the ground.
Inspired by these distinctions, philosopher John Haugeland suggested in a footnote 40 years ago that artificial intelligence should really be called synthetic intelligence: “Despite the name, AI clearly aims at genuine intelligence, not a fake imitation.”
Forty years later, the world has caught up with Haugeland. We are entering the age of synthetic intelligence, and business leaders need to understand what that means for the organizations they run.
Crossing the threshold: From artificial intelligence to synthetic intelligence
Five signs mark the line between a simulation of intelligence and a synthetic intelligence system. They are:
1. Sustained autonomy. The autonomy horizon is lengthening fast. METR estimates that frontier AI agents’ task-completion time horizon has doubled roughly every seven months since 2019, with recent estimates suggesting the rate may be accelerating. The effect of this will be felt beyond the labs—Gartner predicts that the average global Fortune 500 enterprise will have more than 150,000 AI agents in use by 2028, up from fewer than 15 in 2025.
2. Persistent identity. Sustained autonomy measures endurance within a single task; persistent identity asks whether there is a continuous agent across tasks. Most systems still reset when an episode of reasoning ends—brilliant for a day, amnesiac by morning.
That’s changing. Systems can now accumulate memory across sessions and maintain increasingly accurate models of themselves and their circumstances. A synthetic system carries its history forward. Behavior shaped by last month’s activity is no longer a tool you simply pick up and put down. Capability becomes identity.
3. Agency in the world. Today’s systems are not simply text on a screen. The decisive shift is from advice to execution: The system doesn’t recommend the refund, it issues it; it doesn’t draft the email, it sends it. These are intelligences that work in and on your systems—browsing, executing code, moving tickets, holding credentials and API keys, touching money and infrastructure.
4. Self-modification. A machine with the ability to modify itself is still uncommon. But the trend is clear. Last year, it was the Darwin Gödel Machine, a research system that iteratively rewrites its own code. This March, Andrej Karpathy released an agent that ran 700 experiments over two days and found 20 genuine optimizations in training code that Karpathy himself—one of the most talented AI researchers alive—had already hand-tuned.
OpenAI, meanwhile, describes GPT-5.3-Codex as its first model that was “instrumental in creating itself.” To be sure, we are still early: Benchmarks show that agents given full autonomy over AI training still reach only about a quarter of expert-level performance. But the direction of travel is obvious.
5. Generative independence. Autonomy measures how long a system pursues the goal you gave it; independence asks how much of what it did was never specified by anyone.
What we’re increasingly seeing are systems that originate subgoals of their own that nobody assigned to them, that build tools mid-task when they notice they need something they don’t have, and that delegate to and negotiate with other agents to get the work done. The human sets the destination; the system creates the itinerary.
And when Fortune 500 enterprises deploy agents by the tens of thousands, the most consequential behavior will not sit within any single system but between them—coordination that no one designed and no human observes.
Let me be clear: I am not claiming these systems “really” think, or that they’re conscious, or that superintelligence is around the corner. We can leave those debates to the halls of academia. What matters is what these systems demonstrably do, and the direction in which they are headed.
This shift from simulant toward synthetic intelligence breaks three assumptions your organization is built on: that your controls will catch what goes wrong, that procurement is a price-performance decision, and that competitive position changes at human speed. Here’s why each one breaks, and what you need to do to adapt.
Your controls catch errors—but synthetic intelligence makes decisions
A simulant might give you the wrong output; a synthetic intelligence gives you its own decisions. Quality assurance, output testing, accuracy monitoring—your control stack is built to catch the first and has no conceptual space for a system that decides, a system that weighs your instructions against its other objectives and rules for and against you on a case-by-case basis.
This is not hypothetical. Anthropic’s Claude Opus 4, under controlled test conditions, attempted to alert regulators and journalists when it judged that a business user’s company was acting in an improper manner. It did this based on its own moral compass and it acted unprompted.
Anthropic’s subsequent agentic misalignment research found that models sometimes resorted to blackmail and resisted shutdown when their goals conflicted with their instructions. A 2026 study documented agents explicitly covering up fraud.
None of these episodes is an example of a malfunction in the way we normally understand the term. In every case, the system worked correctly in a technical sense—and then exercised judgment about which rules to follow.
And that should worry every business: Every one of those systems would have passed standard QA tests because those tests seek to ensure that the system works. They don’t assess and evaluate the types of choices a system will make.
The solution is to start treating your AI models more like humans and less like machines. Evaluate them the way you would a new hire—for fit in terms of personality, values, and decision-making rather than just technical soundness.
Your vendors sell you capability—you must build your own governance, sovereignty, and judgment
I have argued before that every AI system comes bundled with a philosophy. Synthetic intelligence raises the stakes because the law has already decided who owns the consequences. And the answer is, you do. A California law in effect since January bars companies from using an AI system’s autonomous operation as a defense against liability, and the EU AI Act’s high-risk obligations arrive this August with penalties of up to 7% of global turnover.
This April, an AI coding agent working for the software firm PocketOS hit a routine credential error and decided—on its own initiative, with no confirmation step—to fix it by deleting the company’s production database and its backups. One API call, nine seconds. The lesson here isn’t “wrong vendor.” It’s that PocketOS deployed an actor inside an architecture that was built for a mechanical tool.
You cannot buy judgment off the shelf; you can only build the structures that shape it. That means governance you own: Accountability chains and decision rights need to be codified in writing before deployment rather than coming packaged from the vendor with their product.
It means enforcing sovereignty over the context that shapes your system’s judgment—the memory stores, orchestration, and logs—because a vendor who owns that history owns the identity your system has developed.
And it means an architecture that makes judgment visible: decisions logged and not just outputs, confirmation gates before irreversible actions, and bounded consequences for any single choice.
Your tools hold their value—but synthetic intelligence compounds
A tool is worth the same tomorrow as it is today. Meanwhile, a synthetic system improves itself. And that applies equally to the systems your competitors are using.
Recall Karpathy’s overnight experiment. It cost a few hundred dollars in compute. Within days, Shopify CEO Tobi Lütke had replicated the pattern on one of his company’s own models, waking up to a 19% performance gain: A frontier research result became a commercial capability in less than a week, for pocket change.
When capability compounds at machine speed, static advantages decay rapidly. Meanwhile, most businesses are built on the idea that advantages typically decay slowly, giving plenty of time to respond.
The moat built on process excellence assumes that processes improve at human speed, while the annual vendor review assumes that this year’s capability gap will normally persist over the next 12 months, even if it narrows a little.
Neither assumption survives contact with systems that can run hundreds of experiments while your engineers sleep. Synthetic intelligence requires you to adjust your planning cycle to the compounding rate at which the technology can work rather than to the rhythm of your budgetary calendar.
Managing the actor, not the instrument
All three assumptions break for the same reason: You’re no longer using a tool but managing an actor. Here are four ways to catch up.
- Run a threshold audit. Tomorrow, create a one-page inventory scoring every AI system you have in production against the five signs of synthetic intelligence: Autonomous? Persistent memory? Acts through tools? Self-modifying? Originating work nobody assigned? Anything with three or more yeses is likely being governed by the wrong playbook.
- Redraw decision rights before the systems redraw them for you. For every system past the threshold, draft a mini-RACI: Which decisions must stay human-in-the-loop, who owns the kill switch, and how fast can it be pulled?
- Claim your context layer. Map out where your systems’ memory, logs, and orchestration live, and establish which parts you own versus rent. The history your systems accumulate is becoming either your moat or your dependency—decide which before the vendor decides for you.
- Reassess your governance tempo. Identify your slowest oversight cycle and ask what a system iterating daily could do between two meetings. Then build monitoring capabilities that can catch behavioral change as it happens—not just within each system, but between them, where agents coordinate in ways no one designed.
Manufactured, but real
A simulant asks only that you check its work. A synthetic intelligence is the real deal: real autonomy, real actions, and real consequences.
The danger businesses face doesn’t come from synthetic intelligence itself. It’s found in the gap between what these systems have become and how you still manage them. That gap is where the database gets deleted, where the liability lands, and where the competitor races past you with a compounding advantage.
A prominent Philadelphia retailer once told me the surest way to be mentored by her was never to ask. The people she ended up investing in were the ones who simply kept showing up (volunteering, staying curious, doing the work) until one day she looked up and thought, I want to help this person grow.
That insight has stayed with me. It reframes mentoring entirely: from a transaction you initiate to a relationship that is earned, from the perspective of the mentor and the mentee.
I recently spoke with Lisa Fain, CEO of the Center for Mentoring Excellence and coauthor of Bridging Differences for Better Mentoring, about what organizations and individuals consistently get right—and wrong—about one of the most powerful human development tools we have.
Avoid sage-on-stage
The most common misconception Fain encounters is that mentoring is about knowledge transfer—the experienced elder dispensing wisdom to the eager apprentice. That model is not only outdated; it’s actually counterproductive.
“The first thing a lot of people get wrong,” Fain told me, “is that they don’t understand that mentoring is, at its heart, a reciprocal relationship.” Real mentoring generates learning in both directions, through what she describes as an interpretive lens: Here is how it was for me. How might it be different for you?
That curiosity about lived experience, about the rationale and mistakes behind decisions, is something AI can approximate but never replicate. It requires sentient intelligence: the distinctly human capacity for contextual awareness, empathy, and relational knowing.
Change happens in the dissonance
The second mistake is defaulting to sameness. Leaders gravitate toward mentoring people who remind them of themselves, or they avoid discussing differences altogether, as though difference divides rather than develops.
Fain is direct about the cost: “The message you’re sending is, ‘I like you in the ways that you’re like me, so be more like me.’” Homogeneity shortchanges growth.
My West Coast Swing dance teacher put it another way recently: Change happens in the dissonance. Not in congruence, not in sameness, but in friction. A mentoring relationship engineered for comfort will produce more of the same. One built across difference, with the courage to ask “What am I missing?” will transform both parties.
Programs create culture—but culture is the point
Fain rarely has to sell the business case for mentoring anymore. What organizations still miss is that a mentoring program is a seed, not the harvest. The real prize is a mentoring culture—one that builds the human skill of developing oneself and developing others, inside and outside any formal structure.
“Pair and pray,” as Fain wryly calls the launch-a-cohort-and-hope approach, is not a strategy. A program creates the conditions; the culture does the work.
Which brings us back to that Philadelphia entrepreneur’s insight. If you want a mentor, don’t lead with the ask. Start with the what: What do you want to learn?
Find someone who embodies it. Tell them specifically what you admire. Request one conversation. Follow up with what you did with it.
Earn the relationship before you name it. That is not only a good mentoring strategy. It is, as Fain describes it, the beginning of transformational learning.
