Standard stuff. Senior developer, market rate, fixed term.

Except it wasn’t standard.

I was running AI coding agents throughout — Claude, Gemini — and they were doing a substantial share of the mechanical work. Code generation, boilerplate, test scaffolding, repetitive transformations.

My honest estimate: I was producing roughly twice the output I would have without them.

The project is finished. The client was happy. I was paid the agreed rate.

Nobody renegotiated. Nobody got a bonus.

The productivity gain simply disappeared into the engagement.

I’ve been thinking about that ever since.

Where did the value go?

The client got a shorter project at market rates.

They captured the gain without asking for it. The contract was for a deliverable, not for hours of human effort. When the deliverable arrived faster, they paid less in total.

That’s it. That’s the whole story.

“Labour-saving devices don’t save labour. They transfer it — usually upward.”
— broadly attributed to economist Joan Robinson

This isn’t a complaint about one client. It’s a pattern.

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Every knowledge worker who adopts AI tooling is, right now, doing more and finding that the additional value flows directly to the buyer of their output. The market rate hasn’t caught up. Their leverage hasn’t increased.

The tools improved their output and, at the same time, made their premium rate harder to justify.

The rate pressure that’s coming

Here’s the part worth stating plainly.

I delivered double the output. A developer with five fewer years of experience and AI tooling could probably deliver 80% of what I delivered — for 20% less. A developer with ten fewer years and better prompt skills might deliver 60% for 40% less.

None of those transactions feels like a crisis.

Each one is just the market clearing.

But the cumulative effect is a rate floor that drops steadily — driven not by any explicit devaluation of skill, but by a quiet expansion of supply. More output per developer means the market needs fewer developers, or can pay them less, or both.

The maths is not complicated. The implications are.

This isn’t a prediction about the distant future. At current rates of AI capability improvement, it’s a description of the next two or three years — for developers first, then analysts, writers, paralegals, and financial modellers close behind.

The Last Safe Harbour

There’s a second thing I noticed on that project. It’s more uncomfortable.

AI coding tools are genuinely impressive at execution. Clean functions, edge cases handled, tests generated. Where they’re weaker — at least today — is in software architecture. The decisions that determine how a system is structured so that future changes are cheap and localised.

That’s actually the whole point of architecture. Not elegance for its own sake. Not intellectual satisfaction.

Good architecture exists for one reason: to make future changes fast and cheap.

A senior developer knows how to make one change in the right place. That skill takes years to develop.

But then I thought: does it matter?

If an AI can make 57 tricky changes to a poorly-structured codebase — flawlessly, instantly, at near-zero cost — then the value of elegant architecture starts to collapse.

The entire point of good structure is to make future change cheaper.

If future change is already cheap, you’ve removed the problem that good architecture was solving.

I’ve spent thirty years getting good at placing one change in the right place.

That skill may be obsolete within the next three years.

Not a decade. Not some abstract future.

Within the timeframe of this newsletter.

What this means for all of us

I want to be careful not to overclaim. One developer, one project, one data point.

But the pattern generalises.

The standard counter-argument is that productivity gains always create new jobs and new value categories over time. The loom, the assembly line, the internet — all displaced workers, all eventually created new types of work.

But here’s what’s different this time.

Nobody can name what those new jobs might be. Not even roughly. Not even as a category.

With every previous wave, you could point at the new thing. This time, the pointing hand is empty.

It is not a useful frame for the individual knowledge worker deciding what to do in the next eighteen months.

“It is not the strongest of the species that survives. It is the one most adaptable to change.” — commonly attributed to Charles Darwin

At the individual level, the question is simpler and harder:

The value you’re creating is increasing. The share of it you’re capturing is shrinking.

What are you going to do about that?

I don’t have a complete answer.

But I do see one viable path: escape the dynamic entirely. Build financial independence fast enough that your income is no longer hostage to a rate floor that only moves in one direction.

Don’t win the race to the bottom. Get off the track.

That’s what this newsletter is really about.

The gain was real.

The beneficiary wasn’t you.

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I want to be specific. Not therapeutic-specific — I’m not interested in processing feelings here. Financially specific. Because the numbers tell a story that is more useful to you than the narrative I was telling myself while it was happening.

How it started

March 13, 2020. New Zealand was four days from its first COVID lockdown. I was on a contract at a major Auckland tech firm, and I was frustrated. The same mistakes, everywhere. Bad naming. Tangled dependencies. Tests that tested nothing. Developers who were skilled but had never been shown a better way.

So I opened a Slack channel. I called it #olafs-daily-tips. Each day, one post: a clean code technique, a SOLID principle, a pattern for handling legacy code without breaking it. I thought I might get ten or twenty followers if I was lucky.

At its peak, the channel had just shy of 350.

When that contract ended in May 2020, I had a decision. I could file the tips away and go back to being a senior developer. Or I could find out if there was a business in what I’d been doing for free.

I set up a website. I kept writing. In the end: over 300 posts on software craft, covering everything from unit testing to clean architecture to component design principles. The kind of material I’d been wanting to exist for twenty years.

Word spread. By March 2021 — almost exactly a year after the Slack channel opened — I had my first paying client: a major NZ fintech. One workshop on TDD. A few thousand dollars. Proof of concept.

The good years

FY22 was the peak. Total revenue: $222,096.

The headline number flatters the underlying business a little. About $200,000 of it came from a single six-month embedded training contract with a large NZ tech company — essentially a coaching residency, working alongside their development teams every day. Strip that out and the standalone workshop business was around $22,000 that year.

But still. The business was real. The pipeline was building. I had:

• A major NZ fintech (repeat client, growing engagement)

• A Big Four bank

• A global payments network — a genuine showcase client, the kind that makes other prospects take you seriously

• An Australian software firm, trained in Bath, UK, via video call, up to midnight my time

That last one felt significant. Export dollars. International reach. I was no longer just a local training operation.

FY23 brought in $109,000. Down 51% from peak — but I didn’t read it that way at the time. The large embedded contract hadn’t repeated, which explained most of the gap. The global payments network was a new client. The Australian firm was back. The major NZ fintech was still booking.

Every business has down years. I’d had a down year. That was my read.

I pushed harder. LinkedIn posts, daily tips in an already crowded market, hoping to be spotted by a tech leader with budget. I built an online course on TDD using Teachable. I kept doing the midnight calls for international clients. I sent a monthly newsletter to a list of 43 people — every client manager I’d worked with, a handful of prospects, a few aspiring engineering managers. People with budget authority. People who had seen the work firsthand. I offered 30%, 33%, sometimes 50% off workshops and seminar bundles.

Six students enrolled in the Teachable course. Total.

Still, I thought: keep building, keep the quality high, the market will come back.

The six months

It didn’t come back.

October 2023 to March 2024. That’s the window. That’s when it became undeniable.

The newsletter kept going out. The discount offers kept going out. The 43 people on that list — the right people, the exact right people — read them and did nothing. Not a negotiation, not a “not right now”, not even a polite decline. Silence.

FY24 revenue: $47,000. Down 57% from FY23. Down 79% from peak.

By March 2024, I knew. Not that the business was struggling — I’d known that for a while. That the game was up.

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Free post — The Next 1000 Days

Something is happening to your job.

You might not feel it yet.

But the numbers don’t care what you feel.

The names you recognise are already moving

Last year, US employers announced over 1.17 million job cuts — the highest total since the Covid-19 pandemic in 2020 (CNBC). Of those, about 55,000 were explicitly linked to AI adoption, according to Challenger, Gray & Christmas (Equitypandit). That sounds manageable, right? A rounding error in a 160-million-person workforce.

Here is the problem with that framing: CFO survey data suggests AI-related job cuts could be nine times higher in 2026 than in 2025 (Fortune).

Nine times.

The dam isn’t broken. But the cracks are spreading fast, and almost no one in charge is watching.

This isn’t speculative. The announcements are public. Amazon announced the largest round of layoffs in its history, slashing 14,000 corporate roles and explicitly citing AI as the driver. Salesforce CEO Marc Benioff confirmed he cut customer support from 9,000 workers to 5,000 — because AI was already doing up to 50% of the work (CNBC). Meanwhile Microsoft cut 15,000 jobs, and Oracle cut thousands more — both framing the layoffs as their businesses “doing more with less” through AI (TechRepublic). Microsoft reported quarterly revenue of $70.1 billion, up 13% year-on-year (TechRepublic). They are not struggling. They are thriving. They just need fewer of us to do it.

IBM’s CEO confirmed that AI chatbots took over the jobs of several hundred HR workers (CNBC). Not the jobs of the future — the jobs that existed last Tuesday.

Who gets hit first

If you are early in your career, the data is brutal. Job listings for entry-level corporate roles have declined 15% over the past year, and over the past two years, there has been a 400% increase in employers using “AI” in job descriptions, according to career platform Handshake (CBS News). Translation: they want one person who can direct AI, not five people to do the work.

The brunt of job losses is falling on entry-level roles — data entry, customer service, admin, help desk. Businesses are not just laying people off; they are eliminating the roles entirely (TechRepublic).

And it’s not only the obvious targets. In early 2026 alone, there were 32,000 job losses in technology firms. Nearly 55,000 job cuts were directly attributed to AI across 2025 — and over 75% of those happened after 2023 (AIMultiple).

The curve is accelerating, not flattening.

Here is what they’re not telling you

Every AI company will tell you they want to augment workers, not replace them. They will use words like “partnership” and “co-pilot.” That’s the $20-a-month story — the one where you buy a subscription and become superhuman.

The real story is different. The actual product these companies are building is your replacement, sold to your employer for a few thousand dollars a month. Not a tool that helps you — a service that removes the need for you. The incentives are not aligned with your job security. They never were.

There is no government committee managing this transition. No international body setting a humane pace. No adult in the room deciding that 3% annual job displacement is the safe speed, not 30%. The decision theorist Eliezer Yudkowsky put it bluntly: in a sane world, we would coordinate to ensure the pace of automation doesn’t outrun our ability to adapt. We don’t live in that world. We live in this one, where capability races ahead and the workforce scrambles to catch up.

Some economists will tell you new jobs will emerge. Historically, they’re right — the steam engine and the internet both created more jobs than they destroyed. But that transition took decades, and it happened when the technology had hard limits. Today’s models have no obvious ceiling. Anthropic’s new Claude Mythos model has already identified thousands of software vulnerabilities across every major operating system and browser — a capability previously thought to require elite, state-sponsored hacking teams (The Atlantic/DNYUZ). Anthropic considered it too powerful for public release (AOL/Anthropic). That’s the version they’re keeping back. Think about what it says about the versions they’re shipping.

On software engineering benchmarks, Mythos jumped from 80.8% to 93.9% — and on high-difficulty mathematical reasoning, from 42.3% to 97.6% (36kr). That is not incremental progress. That is a step change, in a single model generation.

Speaking personally: I use Claude Code daily in my development work. It is extraordinary. Some days I feel less like a software developer and more like a code reviewer — checking what the AI built, nudging it, redirecting it. My output is up. My team size requirement is down. I am not complaining. But I am paying attention.

What about UBI?

Universal Basic Income gets floated as the answer every time this conversation comes up. Maybe it happens, maybe it doesn’t — the political obstacles are enormous, and the timeline is anyone’s guess. But here’s the thing even the optimists miss: UBI isn’t a solution, it’s a consolation prize. The problem with depending on UBI isn’t the money. It’s the leverage.

Leverage comes from contributing something the world needs. When your income is a government transfer rather than a wage, you have no seat at the table — not in your company, not in your industry, not in the economy. You become a beneficiary, not a participant. That is not a position of strength. That is the definition of dependency.

The only durable answer is to build assets that earn whether or not you are employed. Investments that compound. Income streams that don’t require your employer’s approval to exist. Financial independence is not a retirement goal for the comfortably-off. It is quickly becoming the only genuine hedge most knowledge workers have.

What to watch

I made a call in the last post on unemployment figures by year-end. Here’s what to track over the coming months:

The official unemployment rate is a lagging indicator — it will look fine right until it doesn’t. What to watch instead: entry-level job postings (are they continuing to fall?), the proportion of layoff announcements that cite AI (it was 4.5% of all cuts in 2025 — by January 2026 it had already risen to 7%, per HIGH5 Test), and whether we start to see college graduates unable to find starting roles at scale. That last one will be the canary.

The transition won’t announce itself. It will arrive as a series of ordinary-looking events — a hiring freeze here, a restructure there — until one day the aggregate is undeniable.

Don’t wait for undeniable.

The Next 1000 Days tracks one person’s attempt to build financial resilience before the labour market changes in ways we can’t fully predict. If this resonated, consider sharing it with someone who needs to read it.

Nothing here is financial advice. Any capital you lose acting on my reasoning is your problem. Any gains are obviously due to my outstanding insights.

There’s a version of this post where I tell you I’ve figured something out.

I haven’t. What I have done is looked at where things are heading — for developers, for knowledge workers, for anyone whose income depends on skills that are becoming cheaper by the month — and decided that working through it in public, with real numbers and outcomes, is more useful than pretending I have a map.

I don’t have a map or a crystal ball. I have a set of hypotheses and a limited amount of time to test them.

So, what do you actually get out of reading this? A real-time record of what one person is trying, what it costs, and what happens. Not a framework. Not a success story reverse-engineered from the ending. Something closer to a flight recorder — running whether things go well or badly, with nothing tidied up after the fact. If you’re navigating the same terrain, that’s more useful than most of what’s out there.

That’s what this newsletter is. Not a self-help column. Not a success story told in retrospect. A lab notebook, written in real time.

The situation, plainly stated

I’m a software developer and architect with thirty years of experience. I’ve worked across development, architecture, analysis, CTO roles, consulting, mentoring and coaching. I know this domain well.

I’ve also watched AI quietly pull the rug out from under a business I built. Not because I executed badly, but because the structural conditions that made it viable disappeared. The training market for developer skills. Gone. First slowly, then all at once. All over in a couple of years.

And this isn’t just my problem. Watch what’s happening in any development team right now. Developers who once wrote code are increasingly just reviewing what AI agents produce. That might sound like an upgrade, and in some ways it is, but let’s be clear about what it means economically: you’ve just become a quality control layer. The next generation of agents, the ones that make fewer errors, need fewer human reviewers. The arithmetic is simple. Shouting, for those willing to look at it directly.

I’m in my mid-fifties. This hits differently when you’re not twenty-eight with thirty years of runway ahead of you. But I’d argue it hits harder for the twenty-eight-year-olds — they just don’t know it yet. At the current pace of AI development, the knowledge work landscape in three years will look nothing like it does today. For anyone whose income depends on cognitive tasks, the question isn’t whether this affects you. It’s when, and how much, and what you’re going to do about it.

The experimental capital I have available to work with is real but bounded. Enough to be meaningful. Not enough to be reckless. Every decision I make here is made under the same constraint most of you are working under: genuine consequences for getting it wrong. A thousand days is a very short time horizon. Most of us will need to do some fairly fancy footwork before the clock runs out.

That’s not a disclaimer. It’s what makes this worth reading.

The countdown

Day 1000 is April 8, 2026 — the day this experiment begins. Day 0 is January 3, 2029.

The clock is running.

By Day 0, the employment landscape for knowledge workers will look materially different from today. Compressed rates. Fewer opportunities. More competition from AI-assisted generalists, and from AI itself. Contractors will feel it sooner — there’s no organisational inertia to buffer them, no HR process, no redundancy package, no three-month consultation period. Just fewer renewals. But employees aren’t safe either. The permanent role just has a slightly longer fuse.

Nobody really knows how governments and central banks will respond to job displacement at this scale. It’s new territory. The policy toolkit wasn’t designed for this. I find that either fascinating or terrifying, depending on the day.

If I’m still primarily dependent on contract income when the clock hits zero, the experiment will have failed.

That’s the hard constraint the rest of this is built around.

The four streams: what I’m actually testing

Most income diversification advice is too abstract to be useful. Here’s what I’ll actually be testing, why, and what I honestly think about each one. I’ll go deep on each in future posts.

1. Contracting. The floor, not the future

Still doing it. Right now, it’s my primary income source. It supports my family and funds the experiments. Without it, there are no experiments.

But let me be direct about what it is and isn’t. Contracting income at the senior end is heavily taxed in a progressive regime. You trade time for money, the government takes a large cut, and it scales linearly. More hours, more income, no compounding. If I try to squeeze more out of it after hours, diminishing returns kick in fast: I’m working into my rest periods, my day-job productivity suffers, and the taxman cheerfully harvests much of the gains anyway. I’d just be earning regular income, the hard way.

The strategic role of contracting in this experiment is to preserve runway while the other things develop. I’ll keep my eyes open for extraordinary opportunities here. But I’m not holding my breath.

Honest assessment: Useful short-term. Structurally limited. Probably shrinking as an opportunity before Day 0.

2. Investing. Real but slow

Investing is on the list, but I want to be straight about where I’m starting from: the capital I have available for this is limited, and a fair amount of it is still in the process of being saved. If you’re in a similar position — income mostly spoken for, not much left over at the end of the month — that’s exactly the situation I’m designing these experiments for. You don’t need a war chest to start, though it obviously helps. You do need to be deliberate about what you do with whatever you have.

Here’s the honest version of what investing can do: doubling your money in three years is a 26% annualised return — exceptional by any measure. It’s just not going to solve a cash flow problem in the near term.

I’ll also confess something. In the past, I’ve been sloppy about this. I spotted two exceptional opportunities in the last two years, knew it at the time, and did nothing with either of them. One would have doubled my money in six months. Another went up 300% over two and a half years. I watched both play out from the sidelines. That kind of thing is clarifying. Good investing turns out to be as much about psychology — what you do when you’re losing, whether you act when you should — as it is about finding the right opportunities. Through general laziness I’ve managed about 10% per annum. Not bad. Also not where we need to be.

I’m being more rigorous now. I’ll document positions and reasoning here as a record you can learn from and adapt — not a system to follow blindly, but a real account of decisions made under real constraints, scored against outcomes.

What investing does particularly well: it runs in parallel. It doesn’t require much of my time to compound. And the analytical skills — reading balance sheets, assessing competitive moats, thinking probabilistically about outcomes — transfer directly to evaluating business opportunities. Which brings us neatly to stream three.

Honest assessment: A genuine parallel track. Best case, I double the capital over three years. Worth running seriously regardless.

3. Building or buying a business. Where the real opportunity is

This is where I think the actual upside lives. It’s also the most complex category, and the one I know least about, beyond what transfers from investing. I’ll be learning this in real time and sharing the process as I go. Feature or bug, depending on your perspective.

The barrier to building software products has collapsed. Any competent developer can now build and deploy in days what would have taken a team months not long ago. That’s simultaneously the threat to contracting income and the opportunity on the other side: if you can identify the right problem, the cost to build a solution is lower than it has ever been in the history of software.

But this stream isn’t just for developers. Bricks-and-mortar businesses are interesting for a different reason, and they’re accessible to anyone willing to do the analytical work. Physical reality still has friction. A good local business — genuine repeat customers, solid margins, owner ready to exit — doesn’t become obsolete because the latest model dropped. And the analytical toolkit from investing transfers directly: you’re still reading numbers, assessing moats, thinking about what makes an economic position durable. The skills compound across categories. Which is convenient, because I need all the compounding I can get.

I’m actively looking at opportunities in both categories. When I find one worth pursuing, you’ll read about it here — including the reasoning, the numbers I can share, and the parts I got wrong.

Honest assessment: Highest potential. Highest complexity. The category where this experiment either works or doesn’t.

4. This newsletter. The meta-experiment

I’ll be straight with you: I have no idea if this will work.

The thesis is a timing bet as much as anything else. Most developers aren’t ready to hear what’s coming. Not because they’re unintelligent, but because the disruption hasn’t hit hard enough yet to feel personal. The AI agents are still making silly mistakes. That will change. The question is whether establishing a clear-eyed voice now, before the urgency peaks, puts this newsletter in a position to matter when it does.

If the timing is right, the readers who show up early themselves gain the most value and, in turn, become the most valuable subscribers of the newsletter. If I’m too early, I’m writing into a relative void for a year or two while the market catches up to the thesis.

Either way, this is a business experiment in its own right, not a side project. The metrics I care about: paid subscribers, churn, revenue per subscriber, and whether the writing holds up when I re-read it in two years.

Honest assessment: Complete unknown. The experiment is live whether I like it or not.

The prediction ledger: one call to open the account

This section is what separates lab notes from commentary. I make specific, falsifiable predictions. I assign confidence levels. I score them when they resolve.

The scoring system is the Brier score — a proper scoring rule that rewards calibration, not just correct picks. The short version: you’re penalised for being confidently wrong, and rewarded for being accurately uncertain. If you want the full technical explanation, ask, and I’ll write it up. It keeps me honest in a way that vague prognostication doesn’t.

Some issues will have a prediction. Some won't. Here's the first one.

Prediction #1: The OECD unemployment rate, currently sitting at 5.0% (January 2026), will rise by at least 2 percentage points to above 7.0% by end of 2026 — approximately 1 percentage point attributable to macroeconomic headwinds (trade disruption, geopolitical friction, slowing growth), and at least 1 percentage point attributable to AI-driven displacement of knowledge work.

Confidence: 75%. Scores on Day 650 (approximately March/April 2027, allowing a quarter for the December 2026 figures to be released). Baseline and scoring data: OECD.org monthly unemployment releases.

The macroeconomic 1% feels relatively uncontroversial given where we are with wars, tariffs, trade fragmentation, and general global uncertainty. The AI 1% is the more interesting call. Organisational inertia will buffer wholesale displacement for a while — companies don’t restructure overnight, and the transition has retraining and liability dimensions that slow things down. But the trickle has started. By end of 2026, I expect it to be visible in the numbers.

One caveat worth making explicit: if AI investment crashes between now and then, that doesn’t invalidate the thesis. The dotcom bust in 2000 didn’t mean the internet wasn’t going to matter. It just meant the market got ahead of itself. The underlying capability keeps developing regardless of what the Nasdaq does.

What comes next

Paid posts go out weekly. I’m committing to that cadence partly because there’s enough happening to write about, and partly because without it I’ll let things slide. You’ll get positions taken, decisions made, things read and found useful, outcomes scored. Easy calls and harder ones. Occasionally, I turn out to be wrong about something in public.

Next week goes deeper on investing — specifically, the approach that led me to those two opportunities I mentioned, and why you don’t need to spend all day buried in annual reports to identify a candidate worth looking at. I also built a Python script that does much of the intelligence-gathering for me. More on that next time.

If you have questions about the setup, the predictions, or anything I’ve glossed over here — comment or reply. I read everything.

The clock is running. Tomorrow is Day 999.

— Olaf

Before you go: one practical tip

Each issue will end with something immediately useful. Not a framework. Not a mindset. Something you can act on.

This one is about debt, and it matters more right now than most people realise.

If your income outlook is uncertain, reduce your debt.

The logic is simple. Debt is a fixed obligation in a world where your income is becoming variable. It doesn’t care whether you’re between contracts, whether your hours got cut, or whether the renewal didn’t come through. The repayment is due regardless.

Most people think about debt in terms of interest rates. The more important variable right now is fragility. A mortgage extension, a car loan, a credit card balance — each one narrows the gap between an income disruption and a genuine crisis. The bigger the debt load, the less runway you have when things get bumpy. And things are going to get bumpy.

This isn’t about becoming debt-free overnight. It’s about not adding to the pile while your income is still reliable. The time to shore up the foundations is before you need them, not after.

If you’re currently thinking about taking on new debt — extending the mortgage, upgrading the car, whatever it is — ask yourself one question first: how does this look if my income drops 30% in twelve months? If the answer is uncomfortable, wait.

That’s the tip. Simple. Not exciting. Worth more than most advice you’ll read this week.

The Next 1000 Days documents one person’s attempt to build financial resilience in the face of AI-driven disruption to knowledge work — in real time, with real numbers, scored against outcomes. If someone you know is thinking seriously about this, forward it to them.

Nothing here is financial advice. Any capital you lose acting on my reasoning is your problem. Any gains are obviously due to my outstanding insights.