Futurist

A futurist is not someone who simply fantasizes about the future. In a professional sense, futurism is connected to strategic foresight: searching for weak signals, building scenarios, evaluating potential consequences, and preparing decisions under conditions of high uncertainty.

Modern professional communities and organizations view foresight as a distinct discipline with its own methods: horizon scanning, scenario planning, systems thinking, backcasting, weak signals, trend analysis, and other approaches.

However, the rapid development of AI presents the futurist profession with a new challenge: historical experience and documented analogies are struggling to keep pace with the speed of technological change.

The Classical Futurist

A classical professional futurist should not simply invent beautiful scenarios. Their work is typically built on a combination of:

• history and analogies;
• statistics and research;
• analysis of technological and social trends;
• economics;
• organizational behavior;
• scenario modeling;
• evaluation of secondary consequences.

A strong futurist strives to answer not only the question "what might happen?", but also:

• what happened previously in similar situations;
• which companies and states undertook similar actions;
• what consequences those actions yielded;
• what forces can accelerate or halt a scenario;
• what risks an organization is underestimating;
• which decisions remain reversible.

This is precisely why professional foresight is historically closer to systems analysis than to science fiction.

Why AI Is Changing the Futurist Profession Itself

In the AI era, a fundamental limitation arises: technology develops so rapidly that stable accumulated experience lags behind the frontier.

By the time an organization obtains a sufficient body of cases, research, and evidence, the technological landscape itself may have already changed.

A conflict arises:

evidence is needed for a confident decision → evidence appears after practice → practice requires decisions before evidence is accumulated.

This is especially noticeable in 2025–2026, when massive investments in AI were made on the basis of prospects and promises, only for practical limitations to manifest increasingly: operational costs, data quality, scaling, latency, security, maintenance, integrations, and the absence of expected ROI.

Theory and Practice

One of the author's hypotheses underlying this direction is that over recent decades, theoretical models in many fields have acquired enormous institutional weight. Some major achievements did indeed first emerge in theory and were only later confirmed by practice.

The problem begins when new theories are built on top of other theories that have not yet undergone sufficient practical testing. A long chain of assumptions emerges, where mathematical or logical consistency may be preserved, while the real system has long since diverged from the model.

In such a situation, a practical specialist possesses a different type of knowledge. They may not command all the formulas of a specific theory, but based on years of experience, they quickly notice systemic constraints:

• "the economics won't work out here";
• "the data volume will start growing exponentially and the system will choke";
• "this cannot be properly maintained by a team of this size";
• "you are underestimating the migration cost";
• "users will not behave the way the model assumes";
• "in production, this will require a different architecture."

This does not invalidate theory. Practice serves as a way to more quickly discover those hidden variables that the model has not yet accounted for.

The Engineering Futurist

From this arises the hypothesis of a new type of technological futurist.

A modern futurist in a rapidly changing technological environment must be not only an analyst, but also a strong practitioner, capable of testing a piece of the anticipated future with their own hands.

Their work cycle looks roughly like this:

see a weak signal → formulate a hypothesis → build a small experiment → check practical constraints → revise the scenario.

Deep competence in at least one complex technical domain is especially important. At the same time, engineering alone is not enough: a futurist needs systemic literacy in economics, psychology, logistics, business organization, law, infrastructure, and other fields, because technological changes almost never remain within a single technical layer.

Why Companies Rarely Can Give a Futurist Suitable Conditions

Here an organizational paradox arises.

A genuine research experiment in a domain where stable experience does not yet exist cannot be guaranteed in advance. If a result can already be proven with sufficient accuracy, it is often no longer research, but routine implementation.

A futurist working at the very frontier needs:

• significant freedom in choosing a direction;
• the right to test hypotheses without a guarantee of results;
• space for rapid and sometimes pointless experiments;
• the ability to discard unsuccessful solutions;
• access to real technical systems;
• time for research that may not yield commercial results.

Yet to a company, all of this looks like poorly managed risk.

A rational company wants a business case, a predictable budget, KPIs, timelines, and a probability of success. Consequently, most organizations actively begin implementing a new technology only after someone else has sufficiently reduced uncertainty.

Hence the rarity of genuine corporate research laboratories and the even greater rarity of a position that could be called a full-fledged technological futurist.

The Futurist and Big Tech

This problem is directly connected to the concept «Big Tech makes a specialist stronger in the past».

A large successful organization typically rewards the ability to reliably operate already proven technologies. The more successful the product, the higher the cost of a radical experiment.

Therefore, a production engineer naturally becomes stronger within the existing technological system, while a futurist needs to constantly step beyond its boundaries—into areas where evidence is still insufficient.

Practical Experience as the Foundation of Foresight

For Nikolay Fi1osof Lanets, this topic is connected with more than 19 years of active practical programming, work with MODX, large production systems, legacy, architecture, AI agents, and his own experimental projects.

The approach is built not on contrasting theory and practice, but on the following principle:

The higher the uncertainty of the future, the more important the ability to quickly turn a hypothesis into a practical experiment and see real constraints before they make it into reports and studies.

This is precisely the direction planned for development in the futurist.expert project.

Related Topics

• Big Tech makes a specialist stronger in the past
• Nikolay Fi1osof Lanets
• Architectural problems of modern LLM agents

Sources and Directions for Further Study

• Association of Professional Futurists — a professional community of futurists;
• OECD — materials on strategic foresight and competencies of foresight practitioners;
• World Economic Forum — strategic foresight, AI, and future-ready organizations;
• modern studies on reproducibility/theory crisis and practical limitations of AI implementations.