The freelance IT market has always been a “laboratory” of change in digital work: global, highly price-sensitive, prone to task modularization, and relentlessly competitive. Artificial intelligence will not so much “kill” this market as accelerate several processes that were already underway: the commodification of code production, the growing importance of reputation and trust, and a shift in value from “writing” to “delivering outcomes” in real business environments.
It is worth starting with a simple distinction that helps bring order to the chaos of forecasts: AI lowers the cost of producing certain artifacts (such as code snippets, tests, or documentation), but it does not eliminate the cost of accountability for the result. In practice, the difference between “generating” and “deploying, maintaining, and staying out of trouble” will become the main axis of differentiation.
Why freelance reacts faster than full-time employment
Remote and contract work platforms are by nature more “task-based” than full-time roles. A large share of projects is short-term, narrowly defined, and price-compared. This is exactly the type of work where generative tools can easily enter as a substitute (the client tries to do it themselves) or as “cheap competition” (more people are able to deliver something that is good enough).
Research based on data from leading platforms shows that after the emergence of ChatGPT, demand for projects in automation-prone categories declined rapidly. In one large analyzed dataset, there was an approximately 21% drop in postings for automation-prone tasks related to writing and coding within eight months of ChatGPT’s launch (compared to more “manual-intensive” work), while at the same time task complexity and maximum budgets in those categories slightly increased.
This is important because it suggests two simultaneous phenomena: “cheap, simple” jobs disappear or become cheaper, while what remains shifts toward more difficult, less obvious problems.
Displacement versus demand “reinstatement”
Discussions about AI often conflate two mechanisms: displacement (AI takes over part of the work) and reinstatement (technology creates new tasks, roles, and needs). In freelance market data, both effects are visible at once, but unevenly distributed across categories.
For example, transaction-level analysis from the Upwork marketplace indicated that after generative AI became widespread, the total number of job postings on the platform increased overall (estimated at around +2.4%), and average earnings per new contract rose (around +1.3%). At the same time, earnings in writing and translation declined—especially for low-value contracts—while high-value work in those categories grew.
From this picture, two compatible theses emerge for freelance IT:
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part of the demand for simple programming work may genuinely disappear (because the client does it themselves or buys it cheaper),
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but the market itself does not disappear—it reorients toward different types of tasks, where AI is a component rather than a substitute.
Additional studies (focused on narrower skill clusters) show that after ChatGPT’s release, demand grew in “complementary” or non-substitutable clusters, while it fell sharply in clusters considered substitutable (such as writing/translation—by as much as 20–50% relative to counterfactual trends in some estimates). The same datasets show growing demand for machine-learning programming and chatbot development, alongside declining demand for junior roles.
This leads to a key intuition: AI does not average the market out. AI polarizes it.
“Near-zero software cost”: truth, half-truth, and a trap
The idea of software costs approaching zero is tempting, because the cost of generating code, architectural drafts, or unit tests really does fall. The problem is that “software” as a market product is not a bundle of text files. It is a system operating in a specific context: with data, users, incidents, integrations, legal risk, SLAs, and reputation.
From the perspective of companies implementing AI in software development, expectations are in fact being revised downward. A Bain & Company report notes that code-generation tools alone typically deliver moderate productivity gains (around 10–15%), and that meaningful economic impact appears only when AI covers the entire lifecycle (requirements, planning, testing, maintenance) and when organizations redesign their processes.
Moreover, Bain emphasizes that coding and testing account for only part of the total “idea-to-deployment” time (roughly 25–35% in their framework), so even large improvements in coding speed do not necessarily shorten time-to-market if the rest of the process remains a bottleneck.
There is also a counterpoint often missed in “zero-cost” narratives: AI inference itself is not free. Financial and industry analyses increasingly point out that many AI products have significant variable costs (compute and energy), making this sector closer to infrastructure than to classic software with near-zero marginal cost.
The practical conclusion is this: the cost of producing a “first version” falls. The cost of delivering, maintaining, and taking responsibility does not fall proportionally.
AI increases the supply of programming labor—even if the number of programmers does not change
AI functions as a productivity lever: the same freelancer can process more tasks in the same amount of time. A controlled experiment involving the GitHub Copilot coding assistant reported a significant acceleration in task completion—around 55.8% faster for the group with access to the tool.
From a market perspective, this means an increase in effective supply. And when supply grows faster than demand for a given type of task, rates in the “commodity” segment come under pressure. A second factor reinforces this effect: a lower barrier to entry for less experienced workers (or even non-IT entrants) who can deliver “good-enough” results in simple projects.
This is precisely why the effect is felt fastest in freelance markets: AI does not need to be perfect. It only needs to lower the minimum quality threshold at a given cost.
That is why “intense competition” is not a side effect here—it is the core. And it will keep growing.
What happens to prices and job structure
Three typical shifts are increasingly visible in both data and market practice:
First: a decline in the number and value of simple tasks. Platform-based studies point to clear drops in postings for automation-prone work, including coding tasks.
Second: a rising share of more difficult, higher-responsibility work. Even where overall demand falls in volume, complexity and maximum budgets rise—suggesting that clients filter out easy tasks and retain those that cannot be safely done with a single prompt.
Third: migration of demand toward “AI-adjacent” categories. Upwork has reported growth in AI-related services, including AI integration and prompt engineering (in its data: AI-related GSV +25% year over year in Q1 2025; prompt engineering +52% year over year).
In other words, it is not “less IT,” but “different IT.”
Where value creation will move
If the cost of producing code falls and competition intensifies, value does not disappear. It changes its carrier. In freelance IT, the strongest advantages will increasingly be those that cannot be easily copied by “more tokens.”
1. Problem definition and responsibility for decisions
The most underestimated competence in IT is not “writing,” but “deciding what to build and why.” In a world of cheap code, the value of diagnosis, prioritization, risk assessment, and matching architecture to real constraints (budget, time, team, compliance) increases. AI helps, but it does not assume responsibility. Clients will still pay for someone who carries the burden of decisions—and can justify them.
2. Integration and “organizational friction”
New features rarely live in isolation: SSO, payments, ERP, CRM, logs, monitoring, permissions, data migrations, incidents. These are areas where code is only the tip of the iceberg. The rest is negotiation with reality.
3. Quality, security, and reliability
As production costs fall, the risk of a flood of “almost working” solutions increases. Paradoxically, this raises the premium on testing, code review, threat modeling, secure SDLC, observability, and incident response. This is also because AI can generate code that is syntactically correct but flawed in its assumptions—or subtly vulnerable.
4. Data as a moat
Much real value will shift into data: its quality, accessibility, methods of collection, labeling, retention policies, and processing rights. “Cheap code” does not mean “cheap data.”
5. Reputation and trust
Freelancing has always had a “market” component (ratings, portfolios, references), but with AI the role of trust grows, because clients know that code can be generated anywhere. The real difference is whether the delivered system will still work in a month, whether someone will answer the phone during an outage, and whether the project will end without legal or reputational problems.
It is worth noting that developer sentiment studies show both rising adoption and rising caution. In 2024, Stack Overflow reported strong favorability toward AI development tools (72% “favorable/very favorable”), but lower than the year before. Such ambivalence usually means the tools are useful but require strong verification skills—which, in turn, favors senior engineers and those “responsible for the whole.”
6. Skills will change faster than job titles
At the macro level, the World Economic Forum forecasts major shifts in employment structures and skill demand by 2030, including a significant change in core skills (in their estimates, 39% of key skills will change by 2030). For freelancing, this is almost the definition of the game: those who win are the ones who can rebuild their “value package” fastest—before the market forces it upon them.
How this will affect different segments of IT freelancers
Juniors and “commodity developers”
The strongest price pressure and the steepest decline in job volume will affect tasks that are easy to specify, short, and repetitive: simple websites, scripts, “tutorial-based” integrations, minor fixes. In this type of work, global competition intensifies, and AI lowers the barrier to entry for new providers. Platform data also shows signals that demand for novice workers may decline even in complementary areas, because clients prefer “one person who can handle everything,” especially when part of the work is already done by AI.
Seniors, specialists, and “delivery owners”
For those selling responsibility, architecture, security, migrations, maintenance, and compliance, AI can actually amplify margins—allowing faster delivery without sacrificing quality. From Upwork’s perspective, AI supported growth in contract value and high-value work across many categories, even where low-value segments suffered.
AI integration and automation specialists
This is the fastest-growing segment, because companies do not want “AI as a curiosity,” but AI embedded in processes: customer support, analytics, internal tools, knowledge search, automation. Growth in AI-related service volumes on platforms is a strong market signal here.
What this means for pricing strategy and collaboration models
“Hourly rates for writing code” will lose appeal in the mass market—not because they will disappear, but because they are increasingly hard to defend when clients see that “code can be generated.” In response, three models are gaining traction:
1. Outcome-based / value-based pricing
You pay for results: a system that works, meets SLAs, passes audits, converts users, or shortens process time. Code is a means, not the product.
2. Productized services
Instead of “I’ll build anything,” offers like: “monitoring + alerting + runbook in one week,” “CI/CD pipeline with regression tests,” or “security audit and configuration hardening.” Clients buy predictability.
3. Retainers, maintenance, and ongoing support
In a world of cheap production, maintenance and responsibility become more valuable. This is a natural moat for freelancers who can build long-term relationships.
The role of regulation and occupational exposure
It is also important to remember that AI’s impact is uneven across industries. The International Labour Organization is developing methods to measure occupational exposure to generative AI at the task and job classification level. For freelancers, this signals that “automation risk” will increasingly be understood in task-based rather than profession-based terms: within a single project, some work becomes automated, while other parts become more valuable because they are harder to capture in a prompt.
The most realistic picture for 2026–2030
Stripped of mythology, the outlook looks like this:
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The freelance IT market is not disappearing. It is fragmenting.
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Simple, repetitive jobs will suffer the most. Platform data suggests demand declines precisely there.
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Demand is growing for AI integration and for roles that connect the entire build-and-operate lifecycle.
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Value will be created less by code itself and more by: problem definition, integration into the client’s environment, risk control, quality, data, trust, and distribution.
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“Near-zero cost” applies to a fragment of the work; the rest—responsibility, operations, security, and organizational context—remains, and will be priced ever higher.
In closing: anyone who today sells “the ability to write code” as a standalone value will need a new narrative. Those who sell “the ability to make a system work and deliver results” may actually see their ceiling rise—because competition becomes cheaper, while trust and responsibility do not depreciate nearly as easily.