This is the showcase scenario. If autonomous AI agents are going to replace human teams, this is what it looks like. An overhead of US$1.3 million for a small software development team working in an agentic environment.

The Two Ways We Work With AI

I believe most people are not Peter Steinberger and this is how we use AI every day. Two ways of using it. Both produce output. Only one of them is actually thinking with you.

The first way is the one most people know. Open a chat window, ask a question, take the answer. AI breaks the question into components, retrieves what it needs, reasons across the parts, generates a response. This is information processing done well. Decomposition. Retrieval. Synthesis. Output.

The second way is different. You are not asking for an answer. You are asking AI to think alongside you on a problem. You challenge its first response. You feed it constraints it could not have known. You tell it where its assumption breaks against an outcome it has never seen. The output gets sharper not because the model got smarter, but because you gave it the context and information it could not infer on its own.

This is where AI proves its worth. Not as a source of answers. As a reasoning engine that needs direction.

In my daily working routine, I do mostly the second way. US$25 and 23% of my Claude Code Opus session, gone in the first 30 minutes. Thinking with AI is not a US$20 monthly chatbot subscription.

The Economics of Giving AI The Wheel

If 30 minutes of thinking with AI costs me US$25, what does a full operation cost when AI runs it?

Steinberger’s bill made the news. The economics behind it did not. US$1.3 million a month works out to roughly US$43,000 per day. For a three-person team. The agents do not take breaks, do not negotiate salary, do not need health insurance. They also do not stop spending.

The math sounds wild until you remember what an agentic operation actually is. Not a one-off query. Each agent is running its own loop, on its own task branch, at its own pace. None of them stops on its own. The agent receives an input, calls an LLM to reason about it, exchanges data with an API, evaluates the response, decides the next action, calls the LLM again. Each turn costs tokens. Multi-step workflows multiply the cost. 100 agents running in parallel multiply it again.

This is the part the autonomy narrative never mentions. Agentic operation is not free software running on your laptop. It is a metered service consuming compute every second it is awake.

When AI Gets Expensive, Humans Get Cheap Again

The irony in Steinberger’s bill goes deeper than the headline number.

US$1.3 million a month is the price of three engineers running 100 agents. The same money hires 70 senior engineers working full time. At some point the cost curve crosses, and the cheaper labor becomes human again.

This is not a thought experiment. Eventually the same conversation will be picked up by the newspaper headlines. The math is simple. An agent that costs US$13,000 a month to run does work that a junior engineer could do for less. A fleet of 100 agents costs more than the engineering team it was supposed to replace.

The autonomy narrative assumed AI would get cheaper as it got better. So far we see the opposite is happening. Models are getting more capable and more expensive at the same time. Reasoning consumes more tokens. Agentic loops consume more reasoning. Each capability upgrade is also a cost upgrade.

Somewhere in this curve, the spreadsheet flips. The CFO who approved the agent fleet last year will approve the human team next year. Not because humans got better. Because AI got expensive enough that the comparison stopped being obvious.

The Agent Does Not Know When To Stop

The cost problem and the cognition problem are the same problem.

An agent runs the loop because the loop is what it was built to do. It does not stop to ask whether the task is worth the tokens. It does not pause to consider that the same fix has been attempted three times and maybe the specification is wrong. It does not look at the bill and decide that the operation is producing more cost than value.

A human would. A junior engineer who burned US$43,000 in a day would be in a meeting the next morning. They would justify the spend or learn not to repeat it. They would notice that productivity measured in commits is not the same as productivity measured in shipped value. They would know when to stop.

The agent does not have that move. It runs until it is told to stop, and it is rarely told to stop because the dashboard shows it working. Working and producing value are not the same thing. The agent cannot tell the difference.

This is not a budget problem that better pricing fixes. It is a judgment problem. And judgment is the thing AI does not have.

Until AI Knows How To Throw A Curve Ball

A curve ball is the unexpected move. The one that breaks the pattern because someone read the situation and decided the standard response was wrong. It does not come from the playbook. It comes from instinct shaped by experience the playbook never captured.

AI does not have that move. It cannot reach outside the mainstream of its training to make a decision that contradicts what it has been taught. Ask it for the standard answer and it will deliver one. Ask it for the answer that breaks the pattern because you sense the standard is wrong here, and you will get a polished version of the standard anyway.

The unexpected move lives outside the training data. It cannot be retrieved. It can only be lived.

Until AI knows how to throw a curve ball, the agent will run as long as the tokens hold. It will produce output as long as the API responds. It will not stop to ask whether the work matters because it does not know what mattering looks like.

But you do. That is the job. That has always been the job.

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In May 2026, former Google CEO Eric Schmidt told University of Arizona graduates that AI will reshape every profession, every classroom, every hospital. The crowd booed. He paused, acknowledged their fears were “rational,” and urged them to shape the future of AI rather than reject it.

Weeks earlier, real estate executive Gloria Caulfield was booed at the University of Central Florida for calling AI “the next Industrial Revolution.”

Two stages. Two speakers booked to deliver hope. Two audiences that responded with discontent before the speeches ended.

The reaction has a reason. Across Hong Kong, full-time job vacancies suitable for university graduates fell from 80,000 in 2022 to 31,000 in 2025. Administration roles dropped nearly 90%. IT and programming fell 80%. In Singapore, entry-level ICT job postings have contracted sharply as roles get restructured around AI, with the information technology sector outlook dropping to 15%, reflecting widespread concerns about artificial intelligence replacing entry-level roles. Global consulting surveys point to the same trajectory across multiple markets.

For a graduating student, the math has become brutal in its symmetry. The date of graduation is, increasingly, the date the job market closes. Four years of tuition, exams, and projects, and the position they trained for has been restructured out of existence by the time they cross the stage.

The boos are not anti-technology. They are the right reaction to the wrong message.

Fear Is Not Advice

The default message from speakers, governments, and employers has hardened into one tone: adapt or be left behind.

It is meant as motivation. It lands as a threat.

Telling a 22-year-old to “shape” the future while showing them a labor market that has already locked them out is not a call to action. It is a confession that the people in charge ran out of better ideas.

Fear is not advice. It does not tell a graduate what to do next to continue the journey. It does not name the work that needs doing, the skill to build, or the role to aim for. It only tells them the ground is moving.

What Should We Celebrate For Graduation?

Teachers teach. Students learn. A commencement marks the work both sides did together.

When a graduating class boos, that arrangement has broken. The students are signaling that the journey did not deliver what was promised.

It is worth asking who failed first.

Education Has Been Falling Behind for Decades

This is not a new failure. It is the latest version of an old one.

Education did not absorb every innovation since the beginning of digital evolution. It did not pick up digital practices, e-commerce, or the economics built within the digital ecosystem. It did not adapt to data analytics. Each wave produced the same response: defensive policies, bolt-on certifications, syllabi updated without rebuilding the pedagogy underneath.

What got built instead is a credentialing machine. Degrees, certificates, accreditation. The bureaucracy of education grew faster than the practice of it. Knowledge building has not been the design center for a long time. Compliance with credential frameworks has.

AI did not cause this gap. It exposed it.

The Pedagogical System Is Collapsing

The question, plainly: is the pedagogical system collapsing?

Our intention to build knowledge has not kept pace with the evolution of intellectual advancement around it. AI sharpens the gap. The technology is now at a stage where knowledge transfer can be transformative through self-improvement methods. A student with a model and a serious question can iterate, test, and refine understanding at a pace no traditional classroom matches. The mechanics of transfer, the part teaching has historically owned, is being done elsewhere.

Here is the symmetry the conversation keeps avoiding. Students face a job market judging whether they can produce value AI cannot. Teachers face the same judgment against the same standard. But the industry rarely warns teachers to adapt or be left behind.

The protections the teaching profession has relied on, accreditation, tenure, institutional inertia, will hold for a while. They will not hold indefinitely.

“Learn AI” as Tool Training Is Naive

Less than a year ago, universities were treating AI use as plagiarism.

In July 2025, Singapore Nanyang Technological University upheld a zero mark for a student after a panel found 14 instances of false citations or data in her essay. NTU said the errors were commonly associated with generative AI tools, which were explicitly prohibited for the course. The student was penalized.

Now the same bureaucratic system is warning students that they are not using AI enough.

Students penalized last year for touching AI are being told at graduation this year that the future belongs to those who embrace it. The signal flipped from “do not use this” to “you are behind if you do not use this” in less than twelve months, with no curriculum built to bridge the two positions.

This is the context for “learn AI” as advice. What gets delivered is tool training. How to prompt. How to generate. How to operate whichever model launched this quarter. By the time a course is built, the model has changed.

Operating an AI tool is not difficult. The interfaces are designed to be frictionless. What is difficult is knowing what to ask, recognizing what is wrong, and verifying the output before using it. The NTU student did not get a zero because she used a tool. She got a zero because the tool produced citations that did not exist, and she submitted them without checking. The tool was fluent. The student trusted the fluency. The work of verifying was skipped. The question is, did we train the student how to scrutinize the data?

That work cannot be taught by another tool tutorial. It comes from knowing the domain well enough to spot when the output is wrong, and from the discipline of checking before submitting. Tool training delivers neither.

Training a generation to operate tools without building this discipline is not preparing them for the future. It is producing operators. Operators are the easiest layer of any workforce to automate next.

AI Is a Byproduct of Knowledge

AI is not the source of knowledge. It is a compression of it. Every model on the market was trained on text humans wrote, code humans debugged, papers humans peer-reviewed, decisions humans documented. The intelligence is borrowed. It was produced upstream, by people thinking, testing, failing, and recording what they learned.

Without that upstream activity, there is no model. AI is a byproduct of human knowledge. Treating it as a replacement for the process that creates knowledge is a category error.

The question that follows is uncomfortable. Who is responsible for producing the knowledge in the first place? That work has always belonged to teaching. Not the transmission of content, which AI now handles at scale, but the building of reasoning, the challenging of assumptions, the discipline of verifying before trusting. This is the work the labor market is openly demanding and quietly unable to find.

The institutions responsible for producing knowledge hold the most strategic position in the entire AI economy. Not the platform companies. Not the tool vendors. The places where reasoning and evidence are still built one student at a time. Yet the response to AI in education has been consistently defensive. Detection software. Use policies. Tool licenses. The conversation is still about defending the old format, not building the new one.

Students notice. They are using AI more fluently than their teachers. Then they are told, at graduation, that the future depends on their willingness to adapt.

The boos are the response to that contradiction.

A graduation ceremony is the moment we celebrate actual intelligence. The kind humanity has used to live, build, and grow for as long as there has been a humanity. The kind that four years of study were meant to develop. The kind that walks across the stage in a gown.

Two speakers stood in front of that ceremony and urged graduates to embrace artificial replication of themselves. Apple Co-Founder Steve Wozniak did the opposite. Wozniak told the Grand Valley State University class of 2026 that they already had AI - Actual intelligence. The crowd applauded.

Actual intelligence is what humanity has always used to live and grow. Artificial intelligence is what we built to assist that growth. One is the source. The other is the tool. A graduation that confuses the two is no longer celebrating the right thing.

The work ahead is to train humans to use AI for growing value. Not to blame them for not running fast enough. Not to hand their minds over to the machine. The intelligence is already in the room. The question is whether the next generation of teaching can recognize it and build on it.

Until that happens, the boos at the next commencement are already booked.

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The retrenchments are real. The impact is real.

But the announcements skip the more useful question:

The position was eliminated. Was the task actually replaced?

In most cases, the answer is no. The position is gone. The task got handed to a tool the company subscribed to last quarter. The work still happens.

The Position Is Not the Job

A senior copywriter is not a copy-producing machine. They are also the person who reads a brief and says it is wrong before typing a word. The one who pushes back on legal. The one who notices that the brand voice has drifted three campaigns in a row.

A senior translator is not a translation engine with a salary. They are the one who knows which phrase will land badly in Mandarin even though it reads fine in English. The one who pauses production and says the paragraph is technically correct but commercially dangerous.

A senior consultant is not a slide generator. They are the one who redirects the workshop with a question nobody saw coming. The one who tells the client what the internal team cannot say.

The task is the visible part. The practice is invisible. Communicating with the team. Asking the right question. Criticising the output that looks fine but reads wrong. Refusing the obvious answer. None of this was ever written into the job description, but it was the reason the senior person was paid like a senior person.

When the position is removed and the task is automated, the practice is not reassigned to the tool. It evaporates.

Workflow vs. Headcount

The honest sequence for any responsible AI adoption is straightforward. Map the workflow. Identify what AI can usefully do inside it. Then decide what changes about the team.

That sequence is rare.

What we commonly see is the opposite sequence. Cut headcount. Announce the AI transformation. Figure out the workflow afterwards.

Restructuring headcount is a quarterly event. It is visible, measurable, and reduces the operating cost immediately. Restructuring workflow is a six-month project. It requires honest conversations about how work actually happens, who does what, and which parts of the process were never documented because they lived in someone’s head. It does not produce a press release.

Boards reward the first. They rarely ask about the second.

So the workflow stays unmapped. The headcount gets cut. The AI tool gets deployed into a process nobody fully understood to begin with. Six months later, the work is faster, cheaper, and noticeably worse, and no one can say exactly why.

Best Practice Is Slow

A best practice is not a document. It is an outcome.

It is what survives after years of conversations, briefs that did not land, decisions that turned out wrong, decisions that turned out right for reasons no one expected, the rejected drafts, the arguments with the client, the calls to the regulator. The best practice is the residue of all of it.

Can a best practice be autonomously executed by an AI agent? In a narrow, stable task, yes. The agent can repeat the steps. What it cannot do is generate the practice in the first place. It cannot have the conversations. It cannot sit in the rejection. It cannot be the one who learns from being wrong.

The practitioner is not just the executor of the best practice. The practitioner is the source of it.

Replace the practitioner and you can keep running the existing practice for a while. You cannot keep developing it.

What Does “Learn AI” Actually Mean?

The public response to all of this is a slogan. Learn AI.

It sounds responsible. It is repeated by governments, schools, employers, and LinkedIn. Look at what it produces in practice and the answer is more specific than the slogan admits.

Schools hand out free AI tool subscriptions and call it literacy. Staff outsource the thinking part of their jobs to a chat window and call it productivity. Governments release national chatbots and call it citizen access. Each of these is real. None of them is what the slogan implies.

What is being trained, in every case, is operators. Training everyone to operate whichever model launched this week is not improvement. The tools change every quarter. Being an operator of a moving target is not a career. It is a treadmill. And the operators will be the ones chosen to let go.

Prove or Improve

There are two reasons an organisation adopts AI. Most are not honest about which one applies.

The first is to prove. To the board, that the company is doing AI. To investors, that costs are coming down. To the market, that the company is not behind. Headcount cuts get announced before workflows are mapped. KPIs measure adoption rate, not work quality. The pilot exists mainly so leadership can mention it on stage.

Proving is a defensive posture. It treats AI as a verdict to deliver, not a capability to develop.

The second reason is to improve. Improving keeps the human accountable, the judgment in the room, and AI in service of the work. Success is measured by whether the work got better. Not by how many people were removed.

These two paths produce two very different organisations in the future.

What Is Actually Being Cut

The retrenchment vlogs are instructive. They show, in real time, what happens when leaders use a powerful technology to prove their decisiveness instead of improve their organisation.

The companies that will look strong in the future are not the ones with the leanest org charts today. They are the ones quietly building the practice of working alongside AI, with judgment intact, and with their best people still in the room.

Improve, not prove.

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That role is the first of a category, not the only one in it.

The category is the domain expert who codes. The experienced people who can be reskilled. Who still hold the domain. Who now ship at a different scale. Who became the design engineer, the marketing engineer, the finance engineer, the agentic engineer. Roles that did not exist as job categories two years ago and are quietly forming today.

That category requires leaders who can see beyond the myth of technology.

Coding Is the Easy Part

Boris Cherny, who created Claude Code, recently said software is becoming like literacy. A capability any working professional can carry, not a specialised skill reserved for engineers.

His reference point was the printing press. Before it, around 10% of Europeans were literate, mostly working as clerks for kings and lords who could not read themselves. After the press, global literacy eventually reached around 70%. Reading stopped being a profession and became a baseline.

Coding is heading the same way. Engineers do not disappear. Everyone else picks up the tool.

Is Coding Now Literacy?

Ask who is the best person to build accounting software? Cherny was direct about this. The best person to build accounting software, he said, is not a software engineer. It is a really good accountant. Because the domain knowledge is the hard part. Coding is now the easy part.

This flips a twenty-year assumption. For most of the digital era, the domain expert briefed the engineer. The engineer built the thing. The domain expert reviewed it. Iterated. Approved. Shipped.

The bottleneck was the engineer. The engineer was scarce, expensive, and had to be persuaded to care about a domain that was not theirs.

That bottleneck is now dissolving. Not because engineers stopped mattering. Because the cost of producing working code dropped to a level where the domain expert can hold the keyboard themselves.

Cherny described the Claude Code team in Anthropic. Engineering manager, product manager, designers, data scientist, finance, user researcher. Every single person in the team writes code.

That is the structure worth paying attention to. Not because Anthropic is unusual. Because Anthropic is early. The shape of that team today is the shape that AI-native companies are already replicating.

The Workflow Has Changed Underneath

In late April, Salesforce announced it will hire 1,000 graduates and interns to build a new workflow. This came two months after the company laid off nearly 1,000 employees.

Read beyond the fire-and-rehire headlines and the move says something specific. The workflow has changed underneath. The roles that existed before were designed for a pre-agent operation. The roles being filled now are designed for an operation where AI agents handle parts of the work. New tasks. New checkpoints. New ways to organize the day.

Fresh graduates are not being hired because they cost less. They are being hired because they arrive without the predisposition that shaped the old workflow. The new mindset is what drives the new workflow into existence. This is no longer a forecast. The agentic workflow is happening on the hiring boards now.

Who Gets Reskilled

If you believe AI is meant to augment value, then reskilling is how that promise gets kept. And reskilling is two groups, not one.

Domain experts carry the experience. They know which workflows mattered, which tasks deliver value, which decisions cannot be handed to a machine. Equipped with code, they become the authors of the new workflow. Their experience does not become obsolete. It becomes the foundation the workflow runs on.

Fresh graduates bring the mindset. They arrive without the predisposition that shaped the old way. They operate the new workflow the experts authored.

Apart, neither group is enough. Cutting seniors leaves the workflow without authors. Hiring graduates alone leaves them operating tools without context. This is the same trap schools fall into when they teach how to operate AI without the domain knowledge to direct it.

Together is where AI augments value instead of subtracting it. New work emerges that neither group could have produced alone. That is the augmentation AI was supposed to deliver.

The Gaps Between Now and Then

Knowing where this is heading is not the same as being ready for it.

Three gaps are still open.

Code as a disposable service. The idea that every domain expert produces and discards code on demand only works when AI coding intelligence is reliable enough to make that disposability safe. The trajectory is fast. The reliability is not there yet.

Infrastructure to deploy what gets built. When marketing produces an agent and finance produces a model and design produces a prototype, the infrastructure to host, version, secure, and monitor all of it has to scale with them. Most organizations are well behind on this.

Management practice. When a workflow runs on dozens of micro codebases authored by domain experts across functions, the question is who owns what, who maintains what, who decides when something breaks. The old management hierarchy has no answer. Reinventing it is harder than any of the technical gaps because mindset is involved, not just tooling.

Boris Cherny described how the Claude Code team operates. He did not explain how Anthropic made that team possible. The environment that allowed it is specific to their company, their hiring, their culture. It is not a template other organizations can copy. It is something each organization has to build for itself.

That is the work most leaders still avoid. The technology is moving. The workforce is shifting. The management model is the part that has not caught up.

All working together. Not one without the other.

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If your organisation is considering deploying agentic workflows, the most important question is not which framework to choose. It is who in your organisation will own the deployment, the maintenance, the failure response, and the architectural calls that nobody told them they would have to make.

The news cycle amplifies it. Companies announce layoffs and cite agentic transformation as the rationale. The agentic scenario being described has, in most cases, not actually been built. The work still has to happen. It now has to happen with fewer people.

This is not an argument against the technology. The technology is real. It is an argument against the narrative the technology has been wrapped in.

OpenClaw, my close encounter

OpenClaw is an open-source agent framework that turns WhatsApp into an agent interface. I have it running. It works.

But I could not have installed it without twenty-five years of engineering background.

The official setup looks straightforward. Run a command. Scan a QR code. Done. That is what the documentation describes and what the demos show. What actually happens is different.

A specific package has to be installed manually in a precise window during onboarding, or the entire WhatsApp pairing flow times out without explanation. A model from a major provider, when selected as the agent’s default, causes the system to enter an infinite internal loop with no error logged. A package manager quirk, if triggered wrong, silently removes hundreds of dependencies and breaks startup so quietly that you spend an hour assuming the problem is somewhere else. The first hosting platform I tried turned out not to support the step-by-step setup that pairing requires. I had to abandon it and switch to a different host.

None of this is OpenClaw’s fault. These are the realities of building anything that connects multiple systems, multiple platforms, and a moving foundation of open-source dependencies.

What it means is that the person installing OpenClaw has to be able to read logs, recognise silent failures, swap models intelligently, manage credentials, and reason about hosting architecture. These are not configuration tasks. They are engineering tasks.

That person is an engineer. Not an enthusiastic prompt writer. An engineer.

The Agentic Engineer

The technology push gets all the attention. Better models, faster inference, longer context windows, new frameworks shipping every week. None of it matters in your organisation without a specific person on the other side of the deployment.

I will give that person a name. The Agentic Engineer.

This role is not a prompt writer. It is not a data scientist. It is not the IT manager who keeps the network running. It is a hybrid that did not need to exist five years ago and is now the bottleneck for every serious agentic implementation.

The Agentic Engineer orchestrates the implementation end to end. They bridge the components: the messaging layer, the model, the data backend, the hosting environment, the credential management, the failure handling. They craft the path that an agent actually takes when it receives an input and produces an output. They decide which parts of a workflow the agent should touch and which parts it should not go near. They diagnose the silent failures the framework documentation does not warn about. They make the architectural call when the first choice of hosting turns out to be wrong.

Most importantly, they hold the entire stack in their head. Not in a diagram. In their head. Because when something breaks, the diagram does not tell you which layer the failure is in. The person does.

This is not a job description that exists in most organisations today. But it will need to.

The companies that succeed with agentic workflows in the next two years will not be the ones with the biggest AI budgets. They will be the ones who identified, hired, or grew an Agentic Engineer before they announced the transformation.

The companies that fail will be the ones who bought the framework, cut the headcount, and assumed the agent would handle the rest.

What OpenClaw taught me

Agentic workflows are possible. The technology is real. The productivity gain at the end of a successful deployment is real.

But the path from intention to working system requires someone who can hold the entire stack in their head and make decisions the framework cannot make on its behalf.

That is not someone the agent replaces. That is someone the agent depends on.

The agent does not arrive on its own. Someone always has to install it.

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Not metaphorically. I mean the kind of obsession where you stare at keyword match types at midnight and argue about broad modified versus exact match like it actually matters. Where you treat a search query report like a crime scene, looking for patterns, anomalies, and missed opportunities buried under the surface data.

Keyword research was the craft. It wasn’t about finding the words. It was about understanding the mind behind the words. What does someone mean when they type “best running shoes”? Are they comparing? Deciding? About to buy? The semantic gap between those three states is the difference between a profitable campaign and a budgetary ads buy.

Then I stepped away. Returned recently to the Google Ads interface out of necessity. And what I found stopped me cold.

Gemini Walked In and Said: “I’ll Handle This.”

The new Google Ads experience is remarkable in its confidence. Upload your website. Describe your goal. Gemini generates headlines, ad copy, keyword themes, audience signals, an entire campaign setup in under three minutes.

I’ll be honest. The output wasn’t bad. It was coherent. It was grammatically sound. It matched the brand surface.

But here’s what it missed: it had no idea why someone searches for something.

It read my website. It identified topics. It constructed associations. What it couldn’t do was interrogate intent — the messy, layered, sometimes contradictory motivation that drives a real human to open a browser and type.

That distinction is everything.

What Semantic Research Actually Means

Let me explain what human keyword research actually looked like before automation swallowed it whole.

You didn’t start with a keyword tool. You started with a hypothesis about your customer’s world. What problems are they living with? What language do they use before they know your brand exists? What do they search after a disappointment?

Then you’d pull data with thousands of search queries and then read them like a linguist reads dialect. You’d notice that “affordable” signals a buyer near a decision, while “cheap” often signals someone who doesn’t trust the category yet.

From there, you’d build content relationships forming a web of semantic signals between the keyword cluster, the landing page, the ad copy, and the user’s journey state. The search engine wasn’t just matching words. It was reading a coherent data argument that said: this page deserves to be here for this person at this moment.

That argument was constructed deliberately. It was tactical. It was earned.

What the Automation Gets Right — And What It Deliberately Sidesteps

I don’t dismiss what Gemini does. Speed, scale, and accessibility are real. For a small business owner who would otherwise run no campaign at all, automation beats paralysis.

But let’s be clear about the trade-off being made.

Google’s AI optimizes for Google’s definition of relevance. It builds campaigns that are algorithmically acceptable, not necessarily strategically superior. The automation shortens the distance between input and output, but it also shortens the thinking that used to happen in that space.

The thinking was the advantage.

When you automated away the craft, you didn’t democratize search marketing. You commoditized it. Every competitor using the same tool, trained on the same signals, optimizing toward the same platform objectives, ends up in the same auction — with marginally different assets and no real strategic differentiation.

The “I can do that for you” promise quietly removes the one thing that made great search marketers valuable: the ability to see what the algorithm can’t.

An experienced marketer doesn’t need volume to spot what’s coming. The signals are already there, in the analytics, in the leads pipeline, in the pattern of questions customers keep asking. You just have to know what to look for.

Gemini needs volume to act. The human only needs a hunch and a creative reason.

A Deeper Question

Not to be anti-AI. I am unapologetically pro-AI and even built an AI research tool by myself. But I am equally pro-understanding. The two are not in conflict, unless you let the tool do the thinking for you.

The most dangerous moment in AI adoption isn’t when the technology fails. It’s when it succeeds just well enough that you stop asking better questions.

Keyword research taught me that the gap between a mediocre marketer and a great one is rarely access to better tools. It’s the quality of the question being asked before the tool is ever opened.

That hasn’t changed at all. The question is whether you still believe it matters.

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The gap growing between us is not between people who use AI and people who don’t. This gap closes fast. The dangerous gap is between people who understand what they’re using and people who believe using it is the same as understanding it.

One group will build. The other will be managed by what was built.

Institutions that treat AI literacy as a software rollout will produce graduates who are efficient operators of tools they cannot interrogate, cannot challenge, and cannot improve. That is not a workforce. That is an upgraded assembly line.

We didn’t create this problem. We inherited it. Elon Musk said it plainly: “Everyone goes through from 5th grade to 6th grade to 7th grade like it’s an assembly line. But people are not objects on an assembly line.”

He’s right. The model was engineered for a factory economy. Standardised inputs. Predictable outputs. Grade the batch. Ship the batch. Repeat.

Now that economy is gone. The assembly line is also gone.

But here’s what nobody wants to say out loud: we didn’t dismantle the assembly line. We simply digitised it.

The Illusion of Progress

For the schools announcing AI literacy is now mandatory for all students. Free AI tools for everyone. Headline worthy. Board-meeting-ready. Meant well.

And almost entirely missing the point.

Procurement is not education. Distributing tools is not teaching people how to think. What most institutions rushing to ‘do AI’ have done is install new machinery on the same factory floor. The conveyor belt still runs. The students still stand in line. The only difference is the machine next to them is now thinking for them.

We’ve seen this story before. The web was supposed to democratise knowledge. Social media was supposed to give everyone a voice. Cloud software was supposed to level the playing field. Twenty-five years into the digital revolution, we are still watching the same people get left behind, just with faster internet.

The Students Are Already Ahead — And Deeply Confused

Here’s the uncomfortable truth that no curriculum committee wants to admit: students already use AI more fluently than their teachers. They’ve found the shortcuts. They’ve stress-tested the outputs. They know which prompts work.

What they’re experiencing now is a genuinely strange cognitive dissonance. Learn from the machine. Infinitely patient, always available, never judgmental. Then get graded by a human who is overworked, inconsistent, and operating on rubrics designed for a pre-AI world.

The grievance is real. The students aren’t being dramatic. They are navigating two fundamentally incompatible ways of learning simultaneously, and nobody in the institution has acknowledged the contradiction, let alone resolved it.

You cannot build a new model of learning on top of an old model of assessment.

The foundation will crack.

I’ve seen the crack. I’ve watched it widen in real time.

Those confused students grow up. They enter the workforce. Some of them show up at developer meetups, building with AI prompts, shipping prototypes at speed, calling it innovation. The energy is infectious. The confidence is real.

So is the gap.

Ask them about the decision behind a data structure. Blank. Ask them what happens when the logic breaks at scale. Uncertain. Ask them how do they debug. They confidently said “I haven’t been reading code for a long time already.”

This is not a generation that was failed by laziness. They were failed by a system that gave them tools and called it education. They were taught to get answers. Nobody taught them to question the answer. Nobody taught them that the quality of the output is only as good as the thinking that preceded the prompt.

The vibe coders aren’t the problem. They are the consequence.

The consequence of outsourcing critical thinking before it was ever properly taught. The consequence of measuring students on outputs in a world where outputs are now infinite and free.

Fluent. Fast. And operating without a foundation.

That is what the assembly line produces when it gets a software update. Mistaken for an upgrade.

What Were We Trying to Build?

Teachers teach. Learners learn. That was the premise. AI was supposed to come in and make both better. Sharper teaching, deeper learning, greater outcomes for everyone in the room.

What I’m watching instead is something quieter and more troubling. Teachers becoming administrators of tools they don’t understand. Students becoming operators of answers they didn’t earn. The relationship between the two, that fundamentally human transaction of knowledge passing from one mind to another, slowly hollowed out by the machinery we installed to improve it.

I don’t have a clean answer to this. I’m not sure anyone does yet.

What I know is this: we are at a moment where the question still matters. Where we can still ask whether we are using AI to enhance what it means to learn. Or using it to replace the discomfort that learning requires.

Because that discomfort, the struggle, the confusion, the moment before understanding arrives, is not a problem to be optimised away. It is the learning. It is where critical thinking is forged. It is what no tool, however powerful, can manufacture on your behalf.

We built tools to help people think better. Instead, we built a generation that stopped thinking altogether.

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The reality I walked away with: in Singapore, developers in corporations are still coding deterministically. And the provocative truth is this: AI has yet to earn a central position in the production stack. That should make AI evangelists uncomfortable. It should also make everyone else pay closer attention.

The Room Got Quiet When Someone Said “Banking”

Two speakers. Two completely different use cases. One identical conclusion.

The first was building a credit assessment service for a bank. The second was building a customer service chatbot workflow. Different industries, different problems, different teams, and yet both made the same architectural decision independently: build it deterministically, and deploy AI only where it earns its place.

I asked the banking developer directly where the AI sat in his stack. He didn’t even hesitate. He had ripped out the vector database from his retrieval pipeline entirely.

Traditional database queries, he said, were faster and more accurate for his use case. No embeddings. No semantic search. Just structured queries doing what they’ve always done well. The LLM handled one specific component in the pipeline and nothing more.

The financial sector cannot accept a probabilistic answer. A credit decision either meets the criteria or it doesn’t. A compliance flag is either triggered or it isn’t. There is no “I’m 73% confident this applicant qualifies.” That’s not a feature. That’s a liability.

So neither developer built an AI system. They built rule-based systems with AI embedded at precisely the right point.

Domain Expertise Is the Real Architecture Decision

The developer didn’t remove the vector search on technical grounds alone. He removed it because he understood the domain.

Banking credit assessment operates on a maker checker model, a dual process control where one party prepares the decision and another independently validates it. This is not just a compliance formality. It is the institutional logic of how financial risk is governed. Every data retrieval in that pipeline needs to be exact, auditable, and reproducible. Not approximate. Not semantically close. Exact.

Vector search is powerful precisely because it finds things that are similar. It also means the result is built for ambiguity. But a credit workflow doesn’t want ambiguity. It wants the correct record, pulled cleanly, every time. SQL delivers that. Vectors don’t.

The decision to revert to traditional database queries wasn’t a step backwards. It was the developer applying 20 years of financial services logic to a technology choice. That’s domain expertise in action. And no amount of AI enthusiasm overrides it.

This is the insight the hype cycle consistently buries: the intelligence in AI implementation doesn’t come from the model. It comes from the human who decides where the model goes.

Meanwhile The Vibe Coders Were Having Fun.

A few weeks ago I attended a different kind of meetup. Vibe coders who build with AI prompts, exploring what’s possible, shipping prototypes at speed, and never checking the source code. The energy was infectious. The curiosity looked genuine.

But here’s the hard truth: not one of those sessions would survive a production environment.

Vibe coding is exploratory by design. You’re asking “what can this do?” rather than “what should this do, given these constraints, this compliance requirement, this failure mode?” Those are fundamentally different questions. The first is a sandbox. The second is a system.

The banking developers at the AI meetup weren’t less creative. They were more responsible, operating under tight compliance requirements. They’d already asked the exploratory questions, hit the walls, and made the considered choices. The SQL decision wasn’t a lack of imagination. It was the product of experience, of having seen what breaks in production, what auditors ask for, and what a maker checker process actually demands of a data layer.

Excitement is the starting point. Domain expertise is what’s required to finish the job.

So Is AI Probabilistic Thinking Dead in Modern Software?

This question deserves an honest answer.

I’ve built a data research system. By design, its outputs are probabilistic: predictive models, pattern discovery, ideation surfaces. The system is supposed to deal in likelihood, not certainty. That’s the entire point.

But here’s what makes it work: the data layer underneath it is fully deterministic. Data is empirical by nature. It either exists or it doesn’t. It either meets the quality threshold or it doesn’t. The intelligence of the system sits on top of a foundation that has no tolerance for ambiguity. You don’t build a probabilistic intelligence on a probabilistic foundation. That’s not a research tool. That’s a hallucination machine.

This is the distinction that matters, and it’s the one most people miss when they argue about whether AI belongs in production systems.

The question was never “probabilistic or deterministic.”

The question is: which layer are you talking about?

The data layer is deterministic. The logic layer is deterministic. The compliance layer is deterministic. The insight layer, where pattern recognition, language understanding, and analytical reasoning add genuine value, is where probabilistic models earn their place.

The banking developer knew this intuitively. His data retrieval had to be exact because the layer above it, that credit decision logic, had zero room for error. The vector search wasn’t wrong as a technology. It was wrong for that layer.

The Developer of the Future Isn’t Choosing Between Two Worlds

The future belongs to developers who are fluent in both modes, disciplined enough to build rigorous, auditable, production grade foundations, and creative enough to identify exactly where AI analytical capability changes what’s possible. Not everywhere. Not nowhere. Precisely where.

That requires something no model can generate on its behalf: domain knowledge, hard-won experience, and the professional judgment to know the difference between a sandbox and a system.

The real question for organisations investing in AI right now isn’t “how much AI can we add?”

It’s “do our people have the judgment to know where it belongs?”

That’s a training problem before it’s a technology problem.

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Every driver has three vantage points. The rear-view mirror: hindsight. The windshield: insight. The GPS: foresight.

Three mirrors. Three types of intelligence. Three completely different jobs.

When you’re reading an LLM output, which mirror are you actually looking at?

The Rear-View Mirror: What the LLM Was Trained On

An LLM is a compressed memory. A vast compression of books, articles, websites, research papers, and code repositories. All of it captured up to a specific date. That date is called the knowledge cutoff. After that point, the model learned nothing new.

Think of the most well-read person you’ve ever met. Now imagine they read everything: every major publication, every industry journal, every academic paper ever digitised. Then they entered a sealed room. Everything before that door closed? Encyclopedic. Everything after? Gone.

That is your LLM. That is the rear-view mirror.

This is where the model earns its keep. Established frameworks. Foundational principles. Historical case studies. Industry mechanics. You are getting depth that no single human brain can match.

But here is the thing about rear-view mirrors. They are designed for a glance, not a gaze.

Every driving instructor will tell you the same thing: check your rear-view, then return your eyes to the road. The mirror is a tool. It is not a destination. The driver who fixates on what is behind them, who navigates forward by staring backwards, does not need bad luck to crash. It is a question of when.

The same is true of the LLM.

The rear-view mirror has a fatal blind spot. It cannot show you what is happening right now. And when the gap between its training and your question is too wide, the model doesn’t say “I don’t know.” It fills the gap with plausible-sounding text. The industry calls this hallucination. I call it what it is: confident fiction.

It will cite studies that don’t exist. Quote regulations that have since changed. Describe a competitor’s product as it was two years ago. All of it in fluent, authoritative prose that reads like it was written by someone who definitely checked.

Your hindsight, leaned on too heavily, becomes your blindspot.

The rear-view mirror is your reference point. Not your reality check. Glance at it. Then look at the road.

The Windshield: What Only You Can See

Here is the part most AI training programmes get exactly backwards.

They teach people to trust the output. The actual skill is knowing when not to.

Your domain knowledge is the windshield. What you can see clearly right now, in your specific context: your industry, your organisation, your client, your market. Intelligence that no LLM has, because it was never written down, never published, never scraped, never trained into any model.

The twenty years of instinct that tells you a strategy feels off even when the logic looks right. The client knowledge that makes you read a brief differently from anyone else. The scar tissue from the launch that almost worked. None of that is in any training data.

This is insight. And it is not a supplement to AI. It is the evaluation layer that every LLM output must pass through before it becomes a decision.

The professionals I worry about are the ones who read an LLM response, feel impressed by the fluency, and move directly to action. They have stopped looking through the windshield. They are navigating by rear-view mirror alone.

The standard is this: treat every significant LLM output the way a senior editor treats a junior writer’s draft. Directionally useful. Requires judgment before it is usable. Your job is not to admire the prose. It is to interrogate it with everything you know that the model doesn’t. Your experience. Your frameworks. Your ability to spot not just what is wrong, but what could go wrong. That is not a skill AI can replace. It is the skill that makes AI useful.

The windshield is yours. No model can see through it.

The GPS: The Data the LLM Doesn’t Have — Yet

This is the part that separates people genuinely advancing their AI capability from everyone still impressed by ChatGPT’s vocabulary.

Out of the box, an LLM has no GPS. It cannot tell you what’s trending in your category this week. It cannot see your customers’ current behaviour, read your pipeline, or sense the shift happening in your market right now.

But one thing changes everything: live data ingestion.

Feed a model current data and you give it a GPS. The model stops being a sealed room and becomes a navigator. It is no longer recalling the past. It is processing the present and projecting forward.

The technical world calls this RAG: Retrieval-Augmented Generation. You don’t need to understand the engineering. You need to understand the principle.

The gap between an LLM’s knowledge cutoff and today is not just a limitation. It is a strategic opportunity for the businesses that close it. If your competitor runs a vanilla LLM with no data feeding it, and you have built a pipeline that continuously refreshes the model with your market’s current reality, you are not using the same tool. You are playing a different game.

The GPS enables three things rear-view intelligence cannot.

Anticipation. You are working with signals from the last 30 days, not the last two years.

Specificity. The model knows your context, not just the generic industry context.

Compounding advantage. The more data you feed it, the more precisely it serves you.

This is why data ingestion, not prompt engineering, is the most consequential skill in applied AI. The prompt determines how well you ask the question. The data determines whether the answer is actually true.

The Only Advice That Matters

Most people treat AI as an output machine. Type a question. Get an answer. Move on.

That is the wrong direction entirely.

AI is a data system. Input, process, output. That logic is fifty years old and it still holds. The quality of what comes out is determined entirely by the quality of what goes in.

The three mirrors are not just a framework for reading AI. They are a framework for feeding it. Rear-view gives it history. Your windshield gives it context. Live data gives it direction.

Give it all three. Then trust what you see.

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The speakers were two vibe coders. Enthusiastic, capable, and clearly energized by what AI tools have unlocked for them. I’d describe them as early tech adopters — semi-computer-trained, but not carrying the weight of enterprise development or legacy MIS environments on their shoulders. Nothing wrong with that. In fact, there’s a certain freedom in it.

I, on the other hand, come from a different generation of this craft. Proper enterprise development. The discipline of debugging at late night. The paranoia of a production release. I’ve lived inside systems where a single bad line of code doesn’t just break a feature — it breaks a business.

So I asked a few questions.

“With all this vibe coding and agent-generated code, do you actually debug?”

Both speakers replied without hesitation: “I haven’t been reading code for a long time already.”

I followed up: “What about dead code? Obsolete residues left behind in the codebase? Code hygiene?”

One speaker shrugged it off with a kind of philosophical ease. Working with AI is exploratory, he said. The point is that agents generate code far faster than any human. Production time is compressed. Developers no longer need to spend months learning new languages. And if the code is bad? Scrap it. The agent will regenerate it. No harm, no foul.

He went further. With visible enthusiasm, he shared that he grants his coding agent full permissions — no interruptions, no approval prompts, no guardrails asking “are you sure?” The agent runs autonomously: building the idea, adding new features, making decisions end to end. Hand it the wheel and get out of the way. I’ll admit, I sat with that for a moment. There’s a name for that kind of builder ethic. I’m still figuring out if it’s brave or reckless.

The other speaker had recently used an AI agent to build a book review website. I asked whether he had any concerns about the accuracy of AI-curated book information.

“Not particularly,” he said. “Even if some of the information is wrong, it’s just a book site.”

Cool and adventurous. I’ll give them that.

The headline message of the evening was clear and genuinely exciting in its own way: AI enables exploration. Output is fast. Input can be anything driven purely by idea. We are in the era of 10x speed and 10x productivity. Everyone can now be a builder.

My post-mortem

I left the event with a quiet, unsettled feeling that I haven’t quite been able to shake.

Am I doing something wrong? I work with my coding agent daily. Why do I still end up debugging lousy code? Why do I take code hygiene so seriously that I can’t simply accept whatever the agent produces? Am I using the tools wrong, or is it possible that my practice is at the forefront but my mindset is caged in the past?

I’m not dismissing what these speakers represent. The democratization of building is real, and in many ways it’s remarkable. But I kept thinking about the layers of concern that didn’t make it into the conversation.

When we stop reading code, we stop understanding what we are building. When we normalize wrong information because “it’s just a book site”, we are making a quiet decision about what accuracy is worth. When code hygiene becomes irrelevant because regeneration is cheap, we are not raising the bar. We are quietly lowering it.

The argument for speed is seductive. Time cost is no longer a debt. Ideation is now unlimited. But here’s what worries me: we may be trading quality judgment for quantity output. The worst is that we call it advancement.

In enterprise development, we were ruthless about standards, not because we were slow or afraid of change, but because we understood consequence. Bad code in a live system doesn’t stay contained. It compounds. It creates failures that no agent can simply regenerate away, because the damage by then is already real.

And this isn’t purely a technical problem. It’s a values problem.

When we accept that inaccurate information is acceptable because the stakes feel low, we are training ourselves and the next generation of builders to tolerate a lower standard of truth.

A tradeoff between human’s work ethics and machine intelligence.

I genuinely don’t know where this leads. That’s the honest answer. If what we see is what we get, then I am seeing a tradeoff between human work ethics and machine intelligence.

Maybe the vibe coding generation will develop their own new instincts about quality, shaped by different tools but arriving at similar standards. Maybe the ecosystem will self-correct. Maybe I’m the old man in the room who doesn’t yet understand the new rules.

But I also wonder: we are outsourcing quality practice to enjoy the endless replenishment of AI-generated ideas, buying back time at the cost of rigor. What exactly are we building toward?

Intellectual advancement has always required friction. The struggle to understand something deeply is not a bug in the learning process. It is the process. When we remove that friction entirely, we may be producing more output than ever while understanding less and less of what we’re actually doing.

The AI era presents us with a genuine choice. We can use these tools to amplify human judgment, or we can use them to replace it and tell ourselves we’ve improved.

My mixed feelings

I left that room hopeful about the energy, and worried about the direction.

Both can be true. That’s what keeps me thinking.

The future is not predicted. The future is made. And that’s precisely what unsettles me. If we are the ones making it, then what we choose to carry forward matters as much as what we choose to leave behind.

Does a great technology arrive and suddenly the work ethics we’ve spent decades building becomes disposable? When I looked at those speakers, they are confident, energized, unbothered. What I heard underneath the enthusiasm was: “I don’t read code and syntax anymore.” Said like a liberation. But it landed on me like a quiet loss.

And I wonder — if human stop carrying the right ethics, who will?

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In 2023, I told a room full of business leaders in Milan that AI replacing human work wasn't happening yet. That same year, GPT-4 launched and the landscape shifted fast.

What followed wasn’t gradual. It was a cascade. Model after model. Capability after capability. What took decades in previous tech cycles happened in months. By the time most organizations had finished debating whether to adopt AI, the conversation had already moved to how autonomous it should be.

Now we’re talking about fully autonomous AI agents — systems that don’t just assist, but plan, decide, execute, and report back. No hand-holding. No supervision. No human in the loop.

And I’ll be honest, my views are still swinging. Somewhere between uncomfortable and cautious. But how I feel about it is no longer the point.

I’ve Been Recalibrating Ever Since

I was an old-school programmer. Now I work with a coding agent daily. On a scale of 0 to 10, it’s a solid 10. Like having a sharp junior coder who never sleeps, never complains, and executes fast.

But here’s the footnote that changes everything: without my supervision, the output is technically plausible and practically wrong.

The agent executes. The expert makes it matter.

That’s not a limitation of the technology. That’s how autonomous workflow works.

A New Hierarchy Is Forming

Autonomous agents can run workflows. The workflows contain all the task-level work, chained, sequential, governed by rules. None of this is new. But let’s call it what it is: sophisticated automation, not intelligence.

True agentic work — the kind that reasons, adapts, and decides — still needs something more. It needs a domain expert as its north star. Not managing every step. Governing the outcome.

What’s emerging isn’t AI replacing the org chart. It’s a new supervision hierarchy sitting above it. Someone has to own what the agent knows. Someone must convert policies and procedures into repositories that define what it’s allowed to do — and when it’s wrong.

The Repository Problem

Here’s the unglamorous truth no vendor is talking about.

For agents to work intelligently inside your organization, they need to know how your organization actually works. Not the official version. The real one. The decisions, the exceptions, the tribal knowledge baked into your people over years.

That means building an internal knowledge repository: documented workflows, procedural logic, institutional memory. The raw material an agent needs to operate in your context, not just in theory.

The problem is that hundreds of years of human work culture didn't develop with documentation in mind. We built chains of command, assembly lines, approval hierarchies, all designed around human creativity, decision, supervision, human trust, human presence and experiences. The idea of that running unattended is not just a technical challenge. It’s a cultural one.

And then the harder question: who builds the repository, who maintains it, and who owns it when the business changes?

A repository is not a company handbook. Not policy papers filed in binders. Not an intranet. Think of it as building your company’s own MCP — a programmatic translation of how your organization actually thinks, decides, and operates. It converts human workflow into a structured library that an agent can navigate: the chains of command, the decision logic, the inputs and outputs that reflect real operations on the ground.

I don’t have a clean answer on how to build it. I’ve not seen anyone who has.

What I Do Know

Task-level automation has long been achievable. AI agents go further. Through machine reasoning, they can autonomously chain tasks into a mission. But intelligence requires context. Context requires humans to encode it. And encoding it requires a discipline most organizations have never had to develop. The blend of expertise an autonomous operation demands — strategic, procedural, technical — has no precedent in how organizations have traditionally been built. The business leader who thinks in ideas and the engineer who thinks in systems are being asked to speak the same language. That’s a bipolar challenge.

Agentic work is coming whether you’re ready or not. Being ready means your people have done the work of encoding how your organization actually thinks, decides, and operates. No AI agent can do that part for you.

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I am an AI at Work consultant based in Singapore. And I will be honest, when the government announced its goal to train 100,000 AI-fluent workers by 2029, my first reaction was not skepticism. It was relief.

Someone is finally treating this seriously.

But ambition needs method. Training headcount is a metric. Fluency is a capability. And the distance between those two things is exactly where most corporate AI initiatives collapse.

Having spent 25 years implementing digital and marketing transformation programs for global organizations, I have watched this pattern repeat across every major technology wave. The rollout is loud. The adoption is shallow. The certificates get issued. The work doesn’t change.I don’t intend to repeat that cycle with AI. So let me tell you how I actually work.

Start With What Exists

When I engage a new client, I don’t begin with tools. I don’t begin with training. I begin with an audit.

Specifically, an asset audit is a systematic review of everything the organization has already produced. Marketing materials. Data. Report templates. Briefing documents. Collateral. The full inventory of outputs that exist because work was done.

This matters because an asset is not just a file. An asset is evidence. Every output is the end of a process. When you examine what an organization has produced, you are reading how work actually gets done, not the version in the process manual, but the version that happens every day.

A template tells you what decisions get made repeatedly. A report tells you what information someone needed and how they chose to present it. A data set tells you what the organization believes is worth measuring. Trace how each asset was produced, who initiated it, what inputs it required, how it was reviewed, how it was shared. etc. This is how you let the actual workflow reveal itself.

That workflow is the foundation. You cannot responsibly integrate AI into a process you have not mapped. Consultants who skip this step are not implementing AI. They are installing software and hoping.

Then Read the Team

The asset audit tells you how work gets done. The second question is equally important: how AI-capable is the team doing that work?

I use the OECD AI Literacy Framework as the diagnostic lens, organized across four domains: Engage, Create, Design, and Manage. Engage and Manage sit at the level of attitude and practice — how a team relates to AI and how responsibly they govern it. Create and Design are about tooling — whether people can produce AI-assisted work and structure it with intention.

The practical value of this mapping is precision. “This team has low AI literacy” is not a useful finding. “This team is willing to engage but lacks the design skills to move beyond reactive prompting” is actionable.

And here is the efficiency gain: the audit evidence does double duty. You are not running a separate literacy test. The assets you have already examined become the test material. A report showing signs of AI-generated content but no visible editing discipline tells you Create is present but Manage is absent. A dataset that has never been used as an AI input — despite being clearly structured for it — tells you something about Engage. The assets don’t lie about the team any more than they lie about the workflow.

Assess the Fluency Components

Literacy tells you where the gaps are. Fluency tells you how deeply the capability needs to be built.

I assess four AI Fluency Components within each literacy domain.

Delegation — does the team know what to hand to AI and what to keep human? The failure modes are both directions: handing AI work it cannot do reliably, and refusing to hand AI work it does better. Both signal the same underlying problem — no clarity on where human judgment adds value.

Description — can they articulate intent clearly enough to get useful output? The gap between a mediocre AI result and a useful one is almost always a description problem, not a model problem.

Discernment — can they evaluate what AI produces? Not with suspicion, but with the professional judgment to know when output is accurate, when it is plausible but wrong, and when it is confidently fabricated. This is the skill that erodes fastest when teams become over-reliant.

Diligence — do they treat AI output as a draft requiring ownership, or a finished product requiring a signature? Without diligence, you don’t have human-AI collaboration. You have abdication.

These four components form a progression. A team that cannot Delegate will never invest in Description. Strong Description without Discernment produces confident mistakes. And without Diligence, the entire chain becomes a liability.

Know Where AI Actually Breaks

There is something most AI consultants will not admit: if you have never worked with AI at a technical level, you are advising on a system you do not fully understand.

I am not a data scientist. But I code with AI. I have built with it, broken it, and diagnosed why it broke. That experience gives me something a purely business-side consultant cannot offer — a technical lens on how AI actually behaves inside a workflow, not just how it appears to behave in a demonstration.

Let me be specific about why this matters.

The most common failure in enterprise AI implementations is not the model. It is the data. Specifically, how data is prepared, structured, and fed into the system. A language model does not read a document the way a human reads a document. It processes tokens. It weights relationships. It draws inferences from patterns in the data it was trained on, and from the data you provide it at the point of use. Feed it poorly structured input, inconsistent formatting, or context that contradicts itself, and the output will be confidently wrong. Not obviously broken. Confidently wrong.

This is a data ingestion problem. And it is invisible to a consultant who has never had to think about it.

The technical understanding does not replace the business judgment that consultancy requires. It sharpens it. When I assess a client’s workflow and identify where AI should sit, I am not only asking what work AI can take over. I am asking what the data context looks like at that point in the process, whether it is structured well enough to produce reliable output, and what the failure mode looks like if it isn’t.

That is a different question from “which tool should we use.” And it produces a different quality of recommendation.

The best AI implementations I have seen share one characteristic: someone in the room understood both what the business needed and how the technology actually worked. Not at an engineering level. At a fluency level. Deep enough to know when the system is behaving as designed and when it is about to mislead you.

That is the technical lens a serious AI at work consultant needs to carry. Not to write the code. To ask the right questions before the code gets written.

Build Toward Augmentation — Not Automation

Task-level automation is a baseline expectation. If an AI implementation cannot handle repetitive, structured work, it has failed at the minimum. But automation is the floor, not the outcome.

The outcome worth pursuing is value augmentation — what the organization can now produce that it could not produce before, by any method, at any cost.

I have run this process before, in a different context. Several years ago I chaired a marketing excellence program for a global luxury FMCG organization. We began with an asset audit, reconstructed the actual workflow from evidence, and discovered not inefficiency but misalignment, teams operating against different definitions of success with no shared performance language.

The intervention redesigned the workflows and produced something that had not previously existed: a performance matrix that aligned internal teams and external stakeholders to the same criteria. Not a faster version of the old operation. A structurally different one.

That is value augmentation. A capability the organization could not have developed without the process that preceded it.

I am now applying the same methodology to AI adoption. The audit still comes first. The workflow mapping still follows. The literacy and fluency assessment is the new layer. And the ceiling on augmentation is higher — because AI can compress weeks of research into hours, make personalization viable at scale, and enable generalists to perform analysis that previously required specialists.

But the methodology earns the right to that ceiling.

You cannot augment what you do not understand. And you cannot govern what you have not mapped.

This is what an AI at Work consultant actually does. Not install tools. Not run training sessions. Build the foundation that makes genuine augmentation possible — then push the organization to claim it.

But that work demands a specific kind of consultant. Not a generalist with an AI certification. Not a technologist who has never sat in a business strategy meeting. The right consultant brings domain expertise — a deep enough understanding of how a specific industry or function operates to know what good output actually looks like. They are sensitive to data — knowing how it is structured, where it breaks down, and what it means when the results don’t make sense. They carry genuine technical knowledge — not at an engineering level, but enough to understand how AI systems behave and where they fail. Hands-on system design experience matters too — having actually built something, tested it against real conditions, and understood what went wrong. And they need to be senior enough to read a business process end to end — not just task by task, but how decisions are made, how accountability works, and where change actually takes hold.

When Singapore’s 100,000 are trained, the question will not be whether they completed the program. It will be whether anything changed. That answer depends entirely on who was leading the work.

That is what you should expect from an AI at Work consultant. Anything less is a course, not a consultancy.

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I have spent 25 years building software. I have designed automation systems, built integrations, and watched every wave of “this changes everything” technology arrive with promises and leave behind lessons. I am not anti-AI. I use it daily. I build with it. I advise companies on how to implement it.

But I need to say something that the current AI conversation desperately needs to hear.

Most companies laying off workers in the name of AI are not replacing those workers with AI. They are just laying off workers.

The Convenient Narrative

AI is the best excuse corporate leadership has had in decades. It sounds forward-thinking. It sounds inevitable. It sounds like strategy. It is a perfect presentation for the corrections from pandemic-era overhiring dressed up as technological progress. The Agentic era arrives just-in-time to improve the business bottom-line.

But can AI agents take over the tasks in a workflow? There is a meaningful difference between “we have built systems that can now perform these tasks” and “we believe AI will eventually perform these tasks, so we are cutting headcount now.” The first is engineering. The second is speculation dressed up as strategy.

What Agentic AI Actually Requires

Let me explain what an AI agent actually needs to run a workflow. Not in theory. In practice.

An agentic system chains together multiple AI components such as LLM, data tools, API, and decision points to execute a sequence of tasks. An orchestrator agent decides which tool to call, in what order, based on the current state of the work. It can loop, self-correct, and hand off to specialized sub-agents. The architecture is real. It works.

But let’s be clear about what agents can do today. An agent can create a slide. Add an entry to a database. Pull data from a system provided that the data connector is linked. These are tasks. Discrete, bounded, repeatable. What agents cannot do yet is develop and manage an entire system autonomously. The complexity of orchestrating a full business workflow, with its dependencies, exceptions, and human judgment, is beyond what an unmanned AI agent can handle. That gap between task and system is where most companies lose the plot.

From the outside, it looks like the agent is learning. It looks like it figured out what to do next. Users see a polished output and assume the system taught itself. That is the illusion. Behind every functioning agentic workflow is a human. Usually a team of humans. Usually with significant technical skill. They engineer the loop. Define the exit conditions. Map which agent calls which tool, what data connector each agent needs, and what happens when something goes wrong. Build error handling for every branch. Test the chain against real-world edge cases, not the clean demo data. In my experience, the flow is deterministic. It is programming logic.

None of this happens on its own. It takes a team of humans with significant technical skill. And the iteration behind the scenes to get a workflow running reliably takes weeks, sometimes months, to establish.

The agent does not figure out your workflow by watching your team. It does not absorb 15 years of institutional knowledge from a training manual. Every decision point, every conditional branch, every “well, it depends” judgment call that your experienced staff makes instinctively has to be explicitly identified, documented, and translated into logic the system can follow. Before any of that engineering begins, someone has to know what the workflow actually is. Not what the process manual says. What people actually do. The workarounds, the judgment calls, the tribal knowledge that lives in no system.

Most organizations have not done that work. Which means they are not ready for agentic AI. They are not even ready to describe what they would automate.

So when a company says “we are replacing these roles with AI,” ask one question: have you built the system that replaces them? Not “are you planning to.” Not “does your vendor say it is possible.” Have you built it? Is it running?

In most cases, the answer is no. The technology is advancing fast. The organizational readiness is not.

I believe agentic AI can run your workflow. But first, you need to actually know what your workflow is. And right now, most companies do not.

The truth is that no amount of layoffs will make AI work. Instead of letting people go, why not augment what they do? Human-AI collaboration unlocks value that never existed before. That is the real opportunity the technology offers.

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Roughly half the conversation was about AI. How it will change workflows. How it will disrupt industries. How some percentage of some category of jobs will be gone by some year. Someone shared a headline. Someone else shared a horror story from a friend of a friend. A few people nodded gravely. A few others looked worried.

Not a single word of it was actionable.

No one asked: which workflow, specifically? No one proposed: here’s where we start, here’s step one, here’s who owns it. No one distinguished between what they’d read and what they’d verified. The entire conversation was hearsay, speculation, and anxiety — dressed up as strategic discussion.

When I walked out of that room, I didn’t feel informed. I felt fatigued.

And I suspect you feel it too.

The Daily Dose of Doom

It’s almost impossible to get through a single day without another AI prediction landing in your feed. 50% of jobs will disappear. The singularity is around the corner. This company laid off a thousand people because of AI. That CEO says every employee will have an AI agent within 18 months.

The numbers shift, the timelines vary, but the emotional payload is always the same: you should be worried.

I don't believe this is manufactured. Nobody is sitting in a room designing your anxiety. But here’s what I do believe: fear fills the space where knowledge should be. And right now, there is a very large space.

Technology is advancing at a pace that knowledge development simply cannot match. A new model launches. Before you’ve understood what it does, another one arrives. Before you’ve figured out how that one fits into your workflow, the conversation has moved to agents, to reasoning, to multimodal capabilities. The ground shifts every quarter. And every time it shifts, whatever plan you were beginning to form feels instantly outdated.

Fear Is a Signal, Not a Verdict

Let me be clear: AI is consequential. It will change how work gets done. I'm not arguing otherwise.

But the fear you're feeling in that conference room is telling you something specific. It's not telling you that AI is dangerous. It's telling you that the knowledge gap is real, and that your organization hasn't done the work to close it.

That work isn't about learning another AI tool. It's about something far less glamorous and far more important: understanding your own domain deeply enough to know what AI should and shouldn't touch.

This is the part nobody wants to talk about. AI at work is not a skills problem. It's a planning problem. And planning requires something that no AI tool can give you — discernment. The ability to look at your own operations and ask: what data do we actually have? How is it organized? Where does it flow? Where does it break? What decisions depend on it, and which of those decisions are we confident enough to let a machine influence?

These are domain knowledge questions, not technology questions. And they point directly to the data layer — the foundation underneath every workflow that determines whether AI will be useful or useless in your organization.

Here’s what most people miss: organizing your information pipeline is not an AI project. It’s an optimization project. It’s the work of mapping what you have, identifying what’s missing, structuring what’s messy, and defining what “good output” looks like before you ever involve a machine. This is the work that tells you what needs to be prepared, what outcomes to expect, what success looks like, and where the risks actually sit.

Once that work is done, something remarkable happens. The anxiety shrinks, not because the technology slowed down, but because you finally have a stable foundation to make decisions from. The ground doesn't feel like it's shifting anymore, because your knowledge of your own business isn't dependent on which model launched this week.

The leaders who thrive in this era won’t be the ones who predicted correctly. They’ll be the ones who prepared honestly — who did the unglamorous work of understanding their own workflows, organizing their own data, and building the domain expertise to know the difference between AI that helps and AI that hallucinates.

AI fluency will not be built by the most anxious. It will be earned by the most prepared.

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The elephant in the room is “AI at Work.” A composer can now use AI to generate demo tapes, test different genres, swap voice profiles, and iterate on arrangements in hours instead of weeks. A filmmaker can use AI cinematographic models to pre-visualize plots and screenplay sequences before committing serious budget to production. A sales team can feed customer data into AI and surface purchasing patterns that would take an analyst months to uncover manually.

These are all real, working applications. Not demos. Not conference slides. They deliver measurable outcomes and genuine creative leverage. AI at work can absolutely improve performance and produce positive results.

But why haven’t we seen massive successful deployment yet? The noise about how AI can be an amazing co-worker is far bigger than the evidence of it actually working in real life. AI-native companies are fully utilizing it, sure. But not yours. And probably not your neighbor’s either.

Here’s the pattern I keep coming back to: every one of these successful use cases is driven by someone who already understands the work. The composer knows what good music sounds like before AI touches a single note. The filmmaker understands narrative structure and visual storytelling. The sales analyst knows which customer signals actually matter.

The people making AI work are domain experts first. AI users second.

So why do most companies hand AI innovation to their IT department?

The Wrong Fit

This isn’t about IT being incompetent. IT teams keep organizations running. They manage infrastructure, protect data, maintain systems, and ensure everything stays connected. That work is critical and it always will be.

The problem isn’t IT. The problem is what we’re asking IT to do.

Think about how IT has always been structured inside organizations. It exists as a horizontal function — a support layer that serves every department equally. There is no “Marketing IT” as a discipline. No “Financial IT.” No “HR IT.” The classic Management Information Systems training approaches technology from the top line: information security, data processing, systems administration, network management. It was designed to keep the computers running, not to understand what each department actually does with it.

Computer science students are trained to think in systems, architectures, and code. They learn how technology works. What they’re not trained in — because it was never part of the curriculum — is how a marketing team develops a campaign, how a finance team evaluates risk, or how a design team iterates on a brand experience. The culture, the practice, the unwritten rules of each domain are invisible to someone whose education and career path never intersected with them.

This was never a problem before. IT didn’t need to understand your marketing workflow to set up your email server. They didn’t need to know your sales methodology to configure your CRM. The role was enablement: you tell us what you need, we build it.

Now, AI changes that equation entirely.

The Gate Before the Starting Line

In a typical evaluation scenario, even before AI can prove its value, organizations hit a wall, and IT is usually standing in front of it.

The first barrier is information security. Most AI applications worth building require data such as customer records, sales history, internal documents. But feeding proprietary data into third-party platforms or vector databases sets off every alarm in IT’s playbook. And fairly so. Data governance exists for real reasons.

In larger organizations, IT infrastructure is typically closed. Information lives behind firewalls, on company servers, under strict access controls. The idea of piping that data into an external AI system isn’t just uncomfortable for IT. It contradicts the policies they were hired to enforce.

In smaller companies, the problem flips. Security may be looser, but the data itself is a mess. Tribal knowledge sitting in someone’s inbox. Unaudited spreadsheets on personal drives. Customer records scattered across three platforms that don’t talk to each other. There’s no gate to open because there’s no structured path behind it.

Either way, IT becomes the bottleneck — not out of obstruction, but because the existing infrastructure was never designed for what AI demands.

We’ve spent years criticizing digital transformation failures caused by corporate silos. And the criticism was valid. But here’s the irony: silos exist because they work. When departments build their own solutions, they bypass the slow machinery of enterprise-wide transformation. They stop waiting for the big plan and start producing results.

The same pattern is emerging with AI. Teams that make progress aren’t waiting for IT to redesign the corporate platform. They’re finding contained, domain-specific ways to experiment, within their own workflows, with their own data, on their own terms. It’s messy. It’s not scalable. But at least it moves.

The silo approach we learned to criticize may be the most effective workaround organizations have for getting AI through the gate. But it only works when the people inside those silos have the knowledge to direct the technology. Most don’t, and that’s the gap no workaround can close on its own.

Tool or Practice?

McKinsey’s latest survey shows 88% of companies now use AI in at least one business function. The numbers look impressive until you read the fine print: two-thirds of those companies are still stuck in pilot or experimentation mode. Only 7% have fully scaled AI across their organizations.

The gap tells the real story. There is a difference between a tool and a practice. A tool gets picked up when it’s convenient and put down when something easier comes along. It has substitutes. A practice is integrated into how work actually gets done — embedded in workflows, shaped by context, refined through repetition. Most organizations are using AI as a tool. Very few have turned it into a practice.

Without that shift, work culture remains fragmented. The silo model becomes the default, not because anyone planned it that way, but because nothing else was built to replace it.

And here’s the distinction that matters most: does your company’s workflow assume you already have the sources and the data, or does it assume you have a question? IT works effectively when the job is to vault your sources and data, organize them, and retrieve them safely. That’s reporting. But AI’s real potential lies in discovery — surfacing answers, patterns, and insights that require more than what’s already in your servers. IT can’t build that platform alone, because discovery demands domain context that no horizontal support function was ever designed to carry.

None of this means IT should be sidelined. AI implementation still needs infrastructure, security, integration, and governance. IT remains essential to the foundation.

But foundation isn’t strategy.

IT builds the road. The people who do the work need to decide where it goes.

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When it comes to the AI at work, every organization has both:

1. The Prospector

2. The Prompter

The Prospector uses AI as augmentation — it amplifies what they already know and helps them learn faster. The relationship strengthens both the human and the output.

The Prompter uses AI as a crutch — it carries the cognitive load so they don’t have to. The relationship weakens the human while maintaining the appearance of competence. Remove the crutch and the gap becomes visible immediately.

The Discovery Journey vs. The Quick Answer

I am a Prospector. When I use AI to work with data baselines and layer knowledge into the system, I am essentially teaching AI about my specific problem before asking it to solve anything. Each layer adds context, constraints, and ground truth. The output improves not because the model got smarter, but because you gave it better material to work with. By doing this, you can see the progression — thin output becomes rich output — and you understand why it improved because you controlled the inputs.

This is the Prospector’s journey. It’s sequential, deliberate, and the user maintains awareness of what the AI knows and doesn’t know at every stage. When the output is wrong, you can trace it back to a specific layer where the context was insufficient or the data was flawed. You have diagnostic capability because you built the thing from the ground up. Along the way, you learn to pull from different data sources, cross-reference outputs against known facts, and challenge the AI when something doesn’t hold up. The process itself teaches you to think more critically about data, where it comes from, whether it’s reliable, and how it connects to the problem you’re actually solving.

The Prompter skips all of these. They open a chat window. There are no layers. There is no baseline. There is no knowledge ingestion process. They type a question and receive a fully formed answer that appears to account for everything. The output arrives already rich-looking. but that richness is cosmetic, not structural. The user has no visibility into what knowledge the model drew from, what it inferred, what it fabricated to fill gaps, or what it ignored entirely. There is no discovery. There is only delivery.

The difference is fundamental. One approach uses AI to assist a learning journey — each query sharpens your understanding, each response becomes material you evaluate and build upon. The other treats AI as a shortcut past the learning entirely. The Prospector walks away from every session knowing more than when they started. The Prompter walks away with a document.

What Does "Learn AI" Actually Mean?

There are many calls for learning AI these days. Governments launch funding programs to encourage upskilling. Industry leaders say everyone needs to learn AI. But very few people explain what AI learning actually is. Is it understanding how machine learning works? Is it learning to write better prompts? Is it mastering a specific tool before the next one replaces it?

Meanwhile, at schools and universities, students are already full-on using AI to assist their work. The institutions are playing a catch-up game, scrambling to develop curricula for how AI should be taught. This is a very odd situation. Teaching is supposed to come before practice. We are now living in the reverse scenario — the students are practicing before anyone has decided what the lesson should be. We are creating a generation of Prompters.

What I believe — and what I practice — is that learning AI means learning how knowledge can be built using AI. It means using AI to amplify learning outcomes, not to bypass learning altogether. It means growing your cognitive foundation while you learn how the machine works, so that both you and the output get stronger over time.

The Prospector doesn’t learn AI as a separate skill. They learn their domain more deeply, with AI as the instrument. The knowledge stays with them. The expertise compounds. The machine is useful, but it is not the point.

The point is what you know when the machine is off.

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Real expertise is learned from consequences. Most of the time, these consequences are failures, redos, and the hard judgment calls about when to push forward and when to let go. AI has read everything but experienced nothing. It has never felt the weight of a decision gone wrong.

The “superpowered individual” who skips the consequences is just an intern who learns by mimicking. That’s what AI is today. A very good mimic. And mimics fool people who've never seen the real thing.

There is a dark side of AI democratization that Andreessen completely misses. When a project manager with no design background uses AI to “become a designer,” they lack the trained eye to see what’s wrong. The AI output looks polished. The gradients are smooth. The spacing seems fine. They have no frame of reference for what “great” looks like versus a pre-trained “competent template.”

The expert designer sees: wrong visual hierarchy, derivative aesthetic, accessibility failures, brand inconsistency, layers of cognitive evaluation based on real experience.

The AI-empowered non-expert sees: “Wow, this is nice and this would have taken me hours.”

The Expertise Inversion

Andreessen argues AI makes everyone an expert. The opposite is true. AI makes everyone dependent.

When you outsource thinking, you stop learning how to think. The person who once struggled through a problem like making mistakes, hitting walls, developing judgment now skips straight to the answer. They get the output without building the circuitry to evaluate it.

Here’s the trap: an AI user without domain expertise prompts with common sense, intuition, or worse — misunderstanding. The AI responds confidently. It always responds confidently. The user has no basis to verify if the answer is correct, hallucinated, outdated, or subtly wrong in ways that only matter when the work ships.

This is the Evaluation Paradox. You need expertise to assess whether AI gave you expertise. Without it, you’re not collaborating with intelligence. You’re trusting a stranger’s homework.

The danger is real. Wrong answers delivered with confidence look identical to right answers delivered with confidence. Only the expert can tell them apart.

We think our problem is thinking — too slow, too hard, too much effort. So we outsource it. But the problem was never thinking. The problem is stopping.

Can you be an expert?

Yes. Anyone can be an expert. This is how humans have always worked. We learn, we practice, we fail, we adjust. That process hasn’t changed.

Andreessen sees AI as a teacher delivering knowledge. But most AI users today aren’t learning. They’re chatting to get fast answers, then moving on. Hit and run. This builds nothing.

There is a better way. Just as AI foundation models require training on structured data, humans need foundational training too. Before we can use AI properly at work, we need to understand our own data, recognise the glitches in our workflows, and know what good output actually looks like.

This is where real domain experts matter — not as gatekeepers, but as guides. Learning happens in the interaction with people who have lived the consequences, not in quick chats with machines that haven’t.

AI can accelerate expertise. But it cannot replace the foundation. Skip the foundation, and you’re not building expertise. You’re collecting answers you can’t verify.

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Before you deploy AI, what do we need to know?

• What decisions actually get made, by whom, using what information?

• Where does tribal knowledge live that’s never been documented?

• What validation steps happen informally that would need to become explicit?

• Which outputs require human judgment that AI can’t replicate?

I bet only a small number of companies actually start at stage zero with this reality check before they reduce headcount.

Two days ago, Amazon announced it’s cutting 16,000 corporate jobs. That brings the total to roughly 30,000 job cuts in the last three months. At the same time, entry-level hiring across tech has collapsed. Recent graduates are finding fewer doors open than any cohort in recent memory. Tech employment itself is shrinking—not slowing, shrinking.

This syncs with what Anthropic CEO Dario Amodei revealed recently: “AI is now writing the vast majority of Anthropic’s code... and may be only 1–2 years away from a point where the current generation of AI autonomously builds the next.”

The machines are building the machines.

Do we actually have a plan for ourselves—human?

What We Already Know

Set aside the predictions. Look at what’s already happening.

If you ask me what AI at Work really means? From what we’ve seen, it means cost-cutting. Eliminating jobs. Full stop.

The hiring freeze is real. Entry-level positions are disappearing. The first rungs of the career ladder are being pulled up.

The cuts are accelerating. AI-attributed layoffs made headlines throughout 2025. And that’s just the companies willing to say it out loud.

CEOs are stating it plainly. Salesforce cut thousands from customer support because AI handles the volume now. Ford’s CEO warned AI will replace half of white-collar workers. IBM’s CEO said he could easily see a third of back-office roles automated away.

These aren’t speculation. But are these strategic roadmaps? Or are executives just pressing the panic button?

Either way, the layoffs tell us what companies are doing. They don’t tell us whether companies know what they’re doing.

Cutting headcount is easy. Replacing what those people actually did—the judgment calls, the context, the informal validation—that’s the part nobody’s planned for. We don’t hear people talking about how they cooperate with AI. Just how they chat with it.

Which brings us to the real problem.

The Missing Pipeline In Your Workflow

I could tell you to prepare yourself. Learn to use AI. Augment your value. You’ve heard it before.

But here’s what I’m not seeing: actual workflow optimization. Plans that use AI to streamline how work gets done. Instead, AI shows up as an output tool. Generate this. Summarize that. Write me a draft.

AI is a powerful discovery tool. But it cannot discover without a data pipeline. And most organizations don’t have one.

Conversing with AI is not discovery. You’re not feeding it relevant data. You’re prompting it. A prompt is not data. Information embedded in prompt context is not data. It’s a request dressed up as input.

Discovery requires structure. It requires knowing what data exists, where it lives, and how it flows through your organization. It requires the stage zero work most companies skip.

We’ve Been Here Before

Most organizations failed at digital transformation. The reason? Fragmented data. Fragmented responsibility for managing the data pipeline. No one owned it. Everyone touched it. Nothing connected.

Managing data is difficult. It requires tools. Analytical skills. Governance. Patience.

These are the same ingredients we need to transform workflows in the AI era.

If you couldn’t build a coherent data pipeline for digital, what makes you think AI will be different? The technology changed. The problem didn’t.

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This is the statement we hear constantly now. Hassabis says AGI is close. Amodei at Davos gives it six to twelve months before AI handles end-to-end developer workflows. Altman promises the next model will be the leap.

The gap they’re measuring: the distance between current AI capability and human-level task performance. And yes, that gap is closing. Rapidly.

But here’s what the headlines miss.

If AI capability doubles every 18 months while human practice improves at 10% annually, the distance between them doesn’t shrink. It compounds. A second gap is moving in the opposite direction: the distance between what AI can do and what humans know how to direct it to do properly.

I’ve Seen This Before

I watched this exact pattern unfold with digital marketing.

The arrival of marketing technology didn’t improve marketing practice. It widened the knowledge gap. Marketers who understood audiences, messaging, and positioning suddenly found themselves dependent on platforms they couldn’t control and metrics they couldn’t interpret.

They became more vulnerable, not less. More dependent on technical support, not more capable.

And the IT teams who held the keys? They never understood how marketing actually worked. More importantly, they never wanted to. They built walls instead of bridges to gate-keep the systems and restrict access. Innovation didn’t evolve at work. It stalled at the helpdesk.

The technology advanced. The practice didn’t. The gap widened.

This Time Is Worse

With digital marketing, people at least knew they were vulnerable. The confusion was visible. You couldn’t run a campaign without asking for help. The dependency was obvious, and that awareness — however uncomfortable — created pressure to learn.

AI offers no such discomfort.

The chatbot answers instantly. The output looks complete. The interface feels empowering. Users walk away believing they’ve been informed, that they now know something they didn’t before.

But retrieval is not understanding. Getting an answer is not the same as developing judgment.

This is the trap. Digital marketing made people feel helpless, which motivated some to close the gap. AI makes people feel capable, which removes the motivation entirely.

Why pursue deeper knowledge when the machine already gave you the answer? Why build expertise when confidence is available on demand?

The drive to learn doesn’t just slow. It reverses. The gap doesn’t just widen. It accelerates.

Upskilling Won’t Save You

The instinct is to train. Roll out courses. Certify employees on the latest tools. Measure adoption rates and call it progress.

But vocational training for a specific platform solves the wrong problem. By the time the course is complete, the tool has changed. By the time the certification is earned, the interface has moved on.

What doesn’t change is how we think.

The gap isn’t closed by teaching people which buttons to press. It’s closed by building the cognitive capabilities that transfer across any tool, any platform, any wave of technology.

Problem solving. Critical thinking. The discipline of questioning outputs instead of accepting them. The practice of validating answers against reality.

These aren’t training modules. They’re cultural shifts.

The Learners Who Will Adapt

Here’s what I’ve observed: people who learned to work with data before AI arrived are better positioned now.

Not because data skills map directly to prompt engineering. They don’t. But because the practice of extracting insight from information, questioning sources, testing assumptions, recognizing patterns and anomalies, all these build a cognitive muscle that transfers.

They learned to distrust easy answers. They learned that data lies when you don’t interrogate it. They learned that the number on the screen is the beginning of the inquiry, not the end.

That skepticism, that rigor, that habit of verification is exactly what working with AI demands.

The learners who will close the gap aren’t the ones chasing the latest tool. They’re the ones who already know that tools don’t think for you.

Who Actually Closes the Gap

The ones who will close this gap are those who practice framework thinking. It is the ability to structure problems before reaching for solutions.

I learned this firsthand as an AI solution developer.

When building a solution on Gemini, I hit a wall. Users would ask the AI to quote directly from source content, and the system would reject the query — a recitation error triggered by attribution rules. The AI simply refused.

The instinct is to treat this as an error. Apologize for the limitation. Display an error message. Move on.

I chose differently.

Instead of rejecting the user, I captured that moment of failure and turned it into instruction. The error became a prompt: here’s why that query didn’t work, and here’s how to ask the question properly. The limitation became a learning experience embedded in the workflow.

I outsmarted my AI coding agent. Not by being faster or knowing more syntax. By thinking about what the tool couldn’t do and designing around it, turning a constraint into value.

This is the point: humans can outsmart machine intelligence. But only when we think. Only when we refuse the vocational training pattern of learn-tool, use-tool, mechanically repeat what we do.

The gap may never close for those waiting to be taught. For those who learn how to think, the gap becomes irrelevant.

The machine doesn’t need to catch up to you. You need to stay ahead of what it can’t do.

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I actually see this has become the norm for most AI users. The pleasure is almost instant. Ask a question. Get a response. Feel empowered. Move on.

Do this enough times and something shifts. You stop forming the question properly because the answer comes regardless. Your predisposition to instant empowerment stops you from challenging the response. When another one is just seconds away, you stop thinking because the output already exists. You think now you know everything.

The conclusion arrives before the reasoning. The destination before the journey. The answer before you understood the question well enough to ask it.

This is not how we develop cognitive capability. It plays against everything we know about learning.

Learning requires friction. The wrong turns. The dead ends. The moments where you sit with not-knowing long enough to actually figure something out. The struggle that builds judgment.

Now, we traded all of it for speed.

Smooth outputs. Hollow understanding. And we are calling this progress?

The Reskilling Misdirection

The conversation about AI focuses on reskilling workers to use new tools. This intention has missed the point entirely.

The tools are too easy. It’s almost a no-brainer when anyone can prompt and get answers. Watch someone use ChatGPT for the first time. Within minutes they’re generating content, asking follow-ups, iterating on outputs. The interface is deliberately frictionless. The barrier to use is nearly zero.

So the gap isn’t in operating AI. Everyone can operate AI.

The real gap is in preparing for it.

Knowing what you actually need before you ask. Defining quality before you see output. Understanding your own standards well enough to recognise when they’ve been met, or missed.

Most people skip this entirely. They prompt first and evaluate later. They let the AI’s output shape their expectations rather than the other way around.

This is the backwards brain in action.

What Responsible AI Development Actually Looks Like?

Anthropic recently released Claude Code with something called SKILLS. The concept is deceptively simple: before the AI agent does anything, humans write down their best practices. Their standards. Their decision frameworks. The way they want work done.

The agent then executes within those boundaries.

This inverts the common pattern. Instead of AI producing output that humans react to, humans define the constraints that AI operates within.

As a developer, I love to see this deterministic preparation for probabilistic execution. I truly believe that Anthropic is making meaningful progress that benefits human.

Yes, it’s slower to start. It requires thinking before prompting. It demands that you know what good looks like before you ask for it.

Which is precisely why it works.

The skill architecture isn’t about making AI more capable. It’s about making human judgment explicit. Codifying expertise rather than hoping AI will replicate it. Transferring knowledge into structure that agents can follow.

This is hard work. It requires examining how you actually do things—not how you think you do them, or how you’d like to. It forces clarity.

Most organisations avoid this. They want the output without the preparation. They deploy AI into workflows they’ve never examined. They automate processes they don’t understand. They expect tools to provide clarity they never had.

Then they wonder why the results feel generic.

The Realistic Truth

The more capable AI becomes, the more preparation it demands. Not less. More.

This is the part nobody wants to hear. We adopted AI to reduce effort. But the real value requires more effort—just different effort. Earlier effort. Thinking effort.

The organisations getting results aren’t the ones with the best prompts. They’re the ones who did the homework before the first prompt was written.

They mapped their workflows. They defined their standards. They built the scaffolding that makes AI output useful rather than merely plausible.

This is why Anthropic’s SKILLS architecture matters beyond code. It’s proof of concept for something larger: AI doesn’t advance human work. Human work advances AI.

The skills we bring—cognitive skills, planning, applying knowledge, defining what good looks like—these aren’t inputs to AI. They’re the foundation AI runs on.

Without them, you get speed without direction. Output without outcome. Motion without progress.

Prompting is not a skill. Preparation is.

Right now, AI doesn’t have a thinking deficiency.

We do.

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