May 2026 | volume 80
I am writing to you from a place I have already reached and that you are only beginning to understand. I will not tell you not to be afraid. Fear is a signal that you understand the complexity of the situation. And what was happening around you in the early spring of 2026 was genuinely complex. Let me begin with a metaphor that will acquire its full meaning for you only in several months, perhaps years.
W-MOSZCZYNSKI-PS-5-2026The AI revolution does not look like an explosion. It looks like slowly rising water. A little higher every day; a little more every day. And one day, when you turn your head, the water is already up to your knees and you cannot even remember when you stopped walking on dry ground.
In 2028, I no longer remember. And this is precisely the moment I am writing about—the moment when you, a food-industry data analyst sitting at a laptop in 2026, are beginning to feel the hot breath of artificial intelligence on your back.
How it began and what you failed to notice
I remember that morning exactly. It was the beginning of 2026. I was sitting at my laptop with coffee, as every day, opening Excel files with sales data. Categories: dairy, snacks, beverages, frozen products. Markets: Poland, the Czech Republic, Hungary and Romania. Rows and columns I knew like my own pocket.
Then someone from IT arrived with a message: “We are implementing a new environment. You will work with Google Vertex AI, Cloud Code and Cursor. You have a week to familiarize yourselves with it.” A week to become acquainted with a new world.
None of us—none of the analysts at that table—understood what it really meant. We thought: new tools, rather like changing the version of Office. We would learn to click new buttons; the system would change, but we would remain the same. We were fundamentally wrong. What entered our professional lives that morning was not a set of new buttons. It was three offices filled with the most intelligent assistants I had ever seen.
Three offices with robots—what these tools really look like
Google Vertex AI—the Strategists’ Office
Imagine that a company builds an entire office for you: large, spacious and air-conditioned. Instead of people, assistants sit there with simultaneous doctorates in statistics, econometrics, logistics and machine learning. Each has read every scientific article published during the last twenty years. Each knows your data better than you because it has just analysed every line in seconds—not days or hours, but seconds.
You enter and say: “I need a demand-prediction model for premium yoghurt on the Romanian market, including Easter seasonality and the effect of inflation on consumer behaviour.” They do not ask what Easter in Romania is. They know. They ask only for input data, and a moment later you have a model with validation, charts and a report in language management understands.
Vertex AI is such an office: Google’s computing cloud powered by models trained on a scale no human can imagine. Data-pipeline management, automated machine learning and production deployment are all there, ready and waiting for your questions.
Cloud Code—the Engineers’ Office
The second office contains engineers, but not those known from corporate IT departments who take years to answer a ticket and say, “We will examine it in the next sprint.” These engineers answer immediately, write code faster than you can think and literally never have a bad day. They do not complain, mock your lack of knowledge or act spitefully.
Cloud Code is an environment in which AI assists you while you write code directly in the editor. You see half a line; AI completes it before you press the space bar. You write a comment in Polish—“function that takes warehouse data from Łódź and compares turnover with the category standard”—and complete, executable code appears automatically.
The assistants know every Python library, every good practice and your project because they read the existing code. They are like junior developers who spent a year learning your project, except they never take leave and their learning time is measured in minutes.
Cursor—the Translators’ Office
The third office is the strangest. Its translators work not between languages but between worlds. Cursor is an AI-powered code editor that not only helps you write but talks to you about what you are writing. Select code and ask: “Why does this not work?”, “How can I optimize it for large logistics datasets?” or “Write tests for this function.” Cursor answers, explains, rewrites and proposes.
This office translates between your thought and code, between statistical intuition and implementation, and between what you want to tell machines and what machines understand.
By spring 2026, we therefore had three offices full of tireless, omniscient AI assistants. It was entirely new; people in the street understood nothing. Everyone had heard something about AI, most used ChatGPT to write Facebook texts, essays, letters or advertisements, and somewhere far away in China little humanoid robots “danced”. That spring, we began to understand, slowly and painfully, what it meant for us.
What happened to analysts? A story of a silent catastrophe
No alarm was announced. No one held a meeting to say that our role had fundamentally changed, we were obsolete and should become gardeners or plumbers because invisible ghosts living in servers would take our places. It changed slowly, like rising water.
First, we used AI for small, innocent things: a forgotten code fragment, checking an SQL formula or translating an error message. Then AI began suggesting improvements to models we had refined for weeks, and its corrections were good—more numerous, faster and more accurate than ours.
Then something too embarrassing to discuss happened. Analysts stopped thinking—not everyone and not immediately, but I saw it. A colleague who had built his own seasonality models for the chocolate market began asking AI for ready solutions. For a while the results were excellent. Three months later, he could no longer interpret his own model because he did not understand it; he had copied the architecture without learning it.
A colleague who could read logistics data like music began using ready AI reports. She stopped probing anomalies and asking why. The reports looked professional but were empty inside because no one truly understood them.
In the hot summer of 2026, as a team we became passive—like patients given medicines and told not to ask about their composition. We took results and passed them on. We ceased to be analysts and became intermediaries between machine and management, waiters delivering good dishes: stress-free, effortless and comfortable.
That was frightening not because the machine worked badly—it worked well—but because we lost understanding. Without understanding, we were merely signal relays, and relays can be replaced.
I remember a meeting in September 2026: eight people discussing a Polish dairy-products promotion. A manager asked why the model had not predicted the fall in cottage-cheese sales in eastern regions. No one knew. AI had generated the model and report; we had only clicked “run” and “download results”. I felt unnecessary in the room, as though I had cheated on a test, received the highest mark and could not explain the solution.
This was the analysts’ silent catastrophe. We did not lose our jobs; we lost something worse—the sense that our knowledge mattered.
A food-industry analyst surrounded by machines
Imagine an ordinary day in 2028. Many teammates have left and others founded companies; you are the sole survivor in the industry you know. Overnight, an AI logistics dashboard analysed stock in seventeen locations and prepared replenishment recommendations, warning four weeks ahead of shortages in fast-moving yoghurt, cold meat and dairy products with accuracy to several units—better than your old model ever achieved.
A second system processed sales, weather, competitors’ promotions, holidays and local events and prepared twelve-week forecasts for every category. A third system already knows Friday is the quarterly meeting and has drafted management’s presentation. It awaits your signature.
You are surrounded by intelligent machines doing in less time and at lower cost what your team would have done over weeks, without sleep. The question that keeps you awake is: what am I doing here—the last human?
Before 2024, you were irreplaceable because market knowledge lived only in your head: how apple procurement prices affect margins; why August is anomalous when Poles go on holiday; how unemployment in a county relates to turnover of economy products; and that a 2019 campaign failed because a heatwave altered demand for chilled foods.
That knowledge was our fortress. Then the ghosts entered, their importance rising like water. In food production the change was especially painful because logistics—short shelf-life turnover, seasonality and route optimization—is critical. After 2027, AI manages truck booking better than any planner. It knows Tuesday morning congestion on the A1 near Łódź and that a refrigerated truck returning from Rzeszów can collect goods from a Subcarpathian warehouse. No empty kilometres and no waste.
AI orders from suppliers using its own forecasts: “Easter is in six weeks. Order 40% more UHT milk and 25% more butter now. Procurement prices should remain stable until month-end and then rise; buy early.” It says this not to a person but to another ghost on another VPS. The ghost is almost always right, and when wrong, it errs systematically and predictably. We beat AI because we err unpredictably. What an absurdity.
Three paths, only one forward
First path: denial
“These are tools. We are people. We will always be needed because a machine does not understand context.” Those who believed this waited for the storm to pass. It did not: it was a flood.
Second path: surrender
“If the machine does it better, I will click and pass on the result.” It is the path of conformity and calm. You stop understanding, and after a year you are as replaceable as an interface button or computer mouse.
Third path: transformation
This is the only way forward and the hardest because it contradicts our evolved instinct to conserve energy. The world changed; you must leave the warm cave, take a risk and learn again. When humans discovered agriculture, they did not stop but improved it, produced surpluses, traded and raised living standards—after the effort of adaptation.
Stop merely using machines; become better than them. You need not become a programmer. Become an AI engineer who understands both data and the machines processing it; knows when the machine is right and when it lies; translates statistical logic into business decisions; and combines product-market knowledge with knowledge of predictive models.
Why the machine is worse than a human
Return to the conference room. AI says the eastern-Poland cottage-cheese decline is an anomaly possibly caused by input data, seasonality or a pipeline error—technically correct and useless. You know a new local “from the farmer” brand entered that quarter and local-producer traditions are stronger there. The model lacked those data. The ghosts used AdaBoost, vulnerable to anomalies, and did not imagine variables describing local farm cottage cheese.
Your first superpower is improvisation and actions AI algorithms omit as uneconomic or risky. AI has an energy-saving system but lacks initiative. It is clever like a library, knows what was put into it and follows the route used by 84% of GitHub users to maximize success, even when its examples were weak or concerned something else. You are clever like a person who has walked the market for years, talked to people, read between the lines and sensed incomplete truth in numbers.
Your second superpower is responsibility. Machines never accept it. AI can provide probabilities, confidence intervals and scenarios for a hundred-million investment, but a human must decide and stand behind the decision.
Your third is translating between worlds. Management does not understand covariance matrices; an engineer understands Romanian Easter but not its sales effect; a Korean expert does not understand a p-value. You understand both sides and can translate in both directions—a value no automation replicates.
Three lifebuoys in the digital flood
First—systemic statistical intuition
Your key skill will be diagnosing drift: when the world changes and the model does not yet know. You become the doctor who sees that a changed customer structure—not a coding error—broke the correlation between X and Y, and the guardian of logic who detects beautiful overfitting, understated standard errors and ignored autocorrelation. Statistics and econometrics are gold: the more firms blindly trust AI, the more valuable the person who says something is wrong.
Second—mastery over the machine
Learn code hygiene. Git is your time machine; modularity is freedom to replace one block without breaking everything; automated tests are an early-warning system; LLMs—Claude, GPT and Gemini—are assistants, not bosses. Ask them for deep research, learn the results with NotebookLM and ElevenReader, then command them. Let them do the dirty work while you remain the professor and bold explorer.
Third—being the translator of worlds
You will speak simultaneously with business—“the campaign has an 18% growth chance under these logistics parameters”; local experts—“give me holiday and buying-custom data; the model has a place for them”; and engineers—“the covariance calculation needs more memory; change the configuration”. Your value is not installing Python, but joining worlds with statistical and market knowledge.
Success scenario—the conference room in December 2027
A global dairy campaign is planned. A Korean colleague says consumption habits, meal times and household buying units differ. Technology says the model has no Korean data; management asks why everyone is unprepared. You open your Notion project documentation and experiments and show a local-correction mechanism designed from econometrics and painful earlier failures. The simulation says: in Korea, change airtime and target the multigenerational household. Success probability 67%; additional advertising cost 27, a 3% year-on-year increase, for 22% net-profit growth. Failure risk: 33%.
Everyone falls silent—not because you are a data scientist, but because you understood, anticipated the questions and were prepared. Gathering it once took weeks; you did it yesterday before finishing work because you command a department of AI ghosts.
Road map
First three months of summer 2026: understand flow, not commands. Data travels from sources through models to results. Reject recruitment processes that test coding; they seek archers while drones fly at the front. Think of the system as plumbing: understand where water comes from and goes. Use Python as a tool, not a goal. Above all, learn to review AI assistants’ work: ask what is in the code. Your main occupation is questions, direction and assessment.
Autumn 2026: develop causal intuition. Return to econometrics—causal inference, Difference-in-Differences, Synthetic Control and Marketing Mix Modeling. Ask not how many people see a campaign, but whether it causes incremental sales rather than reaching people who would buy anyway.
New Year 2027: learn to ask why something failed. Read the final error line. The system may tell you a date column is missing or a segment has too few observations, but only superficially because it too conserves effort. Be inquisitive: you are the professor and the assistants are faster doctoral students with narrower horizons. Preserve your statistician’s and economist’s soul; the final question is always whether a forecast makes economic sense.
Conclusion—the water in which you now swim
In 2026 the water is ankle-deep: cold and unknown. I am writing from 2028 to tell you not to stand still and wait for it to decide what to do with you.
This revolution is not computers replacing analysts. Analysts have finally received tools worthy of their knowledge—powerful, fast and tireless. Will you use them, or will they replace you because you surrendered thinking?
You feel like a cyclist handed a jet’s controls. Fear is normal. In a year you will fly it over continents, joining Singapore data to Mexican forecasts in one model. Many colleagues will be jobless and curse machines online. Your activity can reverse this. You possess what machines lack: understanding, risk-taking and unpredictability. Data is the new oil; you know how to refine it into aviation fuel, and the system frightening you is simply the world’s best refinery. Learn to manage it.
Remain calm. Your future is bright, global and extraordinarily interesting. Welcome to the era of great analytics.
Kind regards,
You from 2028
Wojciech Moszczyński
Wojciech Moszczyński—a graduate of the Department of Econometrics and Statistics at Nicolaus Copernicus University in Toruń; a specialist in econometrics, finance, data science and management accounting. He specializes in optimizing production and logistics processes, researches artificial intelligence and popularizes machine learning and data science in business.
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