Big Tech makes a specialist stronger in the past

The paradox of Big Tech is that it can make an engineer stronger and stronger within an already established technological world, while simultaneously increasing the risk of professional lag relative to the external frontier.

This is not an argument that working in a large company makes a specialist weak. On the contrary: it can make them exceptionally strong—in architecture, operations, scaling, reliability, processes, teamwork, and a specific technology stack.

The problem lies elsewhere: the specialist becomes stronger, but the coordinate system within which they grow may age.

A successful product strives for stability

Virtually any large tech organization wants to create a product that:

• works stably;
• generates revenue;
• scales;
• has predictable processes;
• is maintained by a large team;
• doesn't break with every new change.

The ideal commercial result is to create a "cash cow": a system that produces value for a long time in a predictable manner.

For business, this is rational.

But it is precisely success that begins to cement the technology stack.

The more users, integrations, data, processes, and money are tied to a product, the higher the cost of a technological experiment. A new technology must not just be interesting—it must justify migration, team training, new risks, infrastructure changes, and years of maintenance costs.

Why an experiment becomes an organizational problem

In a small project, a developer can try a new technology and pay the price for their mistake themselves.

In a large product, an experiment almost never remains personal:

• the rest of the team must understand the new approach;
• reviewers must know how to check it;
• DevOps and SRE must support it;
• the company must hire people with the new skill set;
• documentation and processes must change;
• the new technology becomes a commitment for years.

A completely reasonable question arises:

Where is the proof that the new technology will provide a gain sufficient to cover all these costs?

Yet for a truly new technology, such an evidence base often does not exist yet.

This results in a trap:

to introduce the new, you need to prove its usefulness → to prove its usefulness, you need practical experience → to get experience, you need to allow implementation or experimentation.

Therefore, a large organization naturally prefers proven solutions.

The specialist continues to grow — but where to?

By working for years with a single technological system, a person becomes better at it.

They know:

• rare edge cases;
• internal limitations;
• historical reasons for architectural decisions;
• operational quirks;
• ways to quickly solve problems that would cause a newcomer to lose weeks.

Thus, their professional strength truly grows.

ஆனால் if the technology stack itself falls out of touch with the current market, the growth of this competence is increasingly directed into the past.

COBOL as an extreme example

COBOL clearly demonstrates this paradox.

One can be an outstanding COBOL specialist, understand massive enterprise systems, and solve problems inaccessible to most modern developers.

This is a real professional strength.

However, it is hard to argue that deep refinement exclusively in COBOL simultaneously means moving alongside the modern technological frontier.

A person becomes stronger—but in a technology whose role relative to the new market is gradually shrinking.

MODX as a personal example

MODX was once a modern and interesting platform. An active community existed around it, with new approaches, packages, architectural solutions, and complex commercial projects emerging.

Nikolay Fi1osof Lanets was one of the strongest and most well-known specialists in this ecosystem, creating his own packages and tools, working deeply with the internal architecture of MODX, and participating in community development.

Over time, however, it became obvious that the platform itself was developing slower and slower.

One could continue to become even stronger in MODX, building the same types of websites faster and more efficiently, knowing the accumulated legacy better, and operating the existing stack more precisely.

This would be an increase in productivity.

However, productivity is not the same as professional development.

If the external technological world moves toward API-first architectures, modern frontend frameworks, distributed systems, AI, and agentic systems, while a specialist continues for years to refine themselves solely within an old stack, the gap inevitably grows.

Today, the MODX market is significantly smaller than during its heyday. At the same time, the remaining legacy projects create a separate commercial market for support and modernization—but this is a different role for the technology: not a frontier, but a heritage.

Big Tech amplifies this effect

The problem exists outside of Big Tech as well, but it is there that it is particularly pronounced.

A large successful product:

• has a high cost of failure;
• generates a massive amount of legacy code;
• involves a large number of people;
• requires stability;
• has a long lifecycle;
• handles abrupt technological pivots poorly.

Therefore, a strong specialist is rationally utilized where they are already especially effective.

Paradoxically, the more valuable an engineer is to the existing system, the more the organization is interested in having them continue doing precisely what they are already strong at.

The internal censor

Over time, organizational constraints can turn into internal ones.

The engineer dismisses ideas in advance:

• "this won't pass architectural review";
• "the team won't want to learn this";
• "we won't be given time for migration";
• "there is no proven ROI";
• "there is too much production risk."

Even if no one directly forbids experimenting, the specialist gradually stops seriously considering directions that cannot be realized within their organizational environment.

It is here that technological inertia begins to affect not just the stack, but the very way of thinking.

AI makes the problem particularly noticeable

In 2025–2026, the technological frontier began moving at an unusually high speed due to AI.

Even the largest companies cannot necessarily provide a specialist with good conditions for studying the newest AI technologies. The reason is not just budget.

Often, there is still no expertise inside the organization capable of soberly assessing the risks and potential value of a new technology, because stable practical experience simply hasn't had time to accumulate.

The company demands proof, and proof does not yet exist precisely because the field is new.

As a result, the most interesting experiments turn out to be the hardest to defend organizationally.

This brings the conversation to the role of the futurist: a specialist who must work precisely where past experience is insufficient and part of the future must be tested through experimentation.

Research laboratory vs. production logic

A characteristic conflict arises when a strong engineer wants to explore new directions, while the business is already optimized for a stable production process.

Rational business says:

We make money on the existing product. We need to make the existing product better.

Rational researcher replies:

While we are improving the existing product, the technological world may change so much that our current efficiency ceases to hold its previous value.

Both sides may be right within their own coordinate systems.

The problem arises when a specialist needs the next stage of professional development, while the organization finds it more profitable to leverage the competence they have already accumulated.

Two phases of professional growth

At an early stage, a strong organization can dramatically accelerate a specialist's development.

Large tasks, experienced colleagues, high responsibility, production loads, and real constraints provide experience that is difficult to gain in small projects.

However, after a certain level, the situation can flip.

First, a person needs a strong system to grow.

Later, they may need to step outside the stable system to regain the right to:

• choose new technologies;
• set their own tasks;
• conduct risky experiments;
• bury unsuccessful ideas;
• build projects without the requirement to prove the result in advance.

Main thesis

Big Tech makes a specialist stronger in the past, when a person's professional growth increasingly happens inside a proven and stable technological system, while the external frontier develops faster than this system is able to change.

This does not mean that every specialist in Big Tech inevitably falls behind.

Just as the statement "war kills people" does not mean that every person dies in a war, the thesis describes a systemic causal effect rather than an absolute outcome for every individual case.

Risk can be mitigated—through research labs, side projects, R&D teams, sabbaticals, internal sandbox environments, and constant practice outside the main production stack.

However, the very conflict between the stability of a successful product and the unpredictability of the technological frontier remains.

Related Concepts

• Futurist — on the role of the specialist who must explore a space where proven experience is not yet sufficient;
• Nikolay Fi1osof Lanets — the author's professional context and history;
• MODX → prisma-cms → freecode → haih-agent — an example of breaking out of one technological system and sequentially searching for new architectures;
• Architectural problems of modern LLM agents — an example of a current research direction on the rapidly changing AI frontier;
• futurist.expert — a project for the further development of these studies.