Saturday, 5 September 2026
Artificial intelligence is widely portrayed as a threat to knowledge workers, but Upwork is preparing for a different outcome. Rather than assuming AI will simply replace the freelancers operating on its marketplace, the company is rebuilding its platform around a future in which people and autonomous software increasingly work together. The shift represents a fundamental change in what Upwork is trying to become. Historically, the platform connected businesses seeking particular skills with independent professionals capable of completing the work. In the emerging model, the customer may increasingly begin with an objective rather than a job description, while artificial intelligence determines what work needs to be completed, breaks it into individual components and identifies whether each component should be handled by a person, an AI system or a combination of the two.
Andrew Rabinovich, Upwork’s Chief Technology Officer and Head of AI and Machine Learning, described this transition during a discussion at AI4 2026. Having worked in artificial intelligence and machine learning for more than two decades, Rabinovich challenged the assumption that current systems are close to eliminating the need for human expertise across the economy. His argument is not that AI will leave employment unchanged. Quite the opposite. Many routine tasks are already becoming automated. Simple translation, basic summarisation, straightforward graphic production and other relatively predictable assignments can increasingly be performed by machines. What remains much more difficult is work involving ambiguity, judgement, taste, context and responsibility for determining whether the final result is actually good enough.
That distinction is becoming central to Upwork’s strategy. The company is moving beyond its historical role as a talent marketplace towards something resembling an operating system for knowledge work. Instead of asking a customer to decide in advance whether a project requires a designer, software engineer, marketing specialist or another professional, its AI systems increasingly attempt to understand the desired business outcome first. A company might know that it wants to increase online sales, for example, without knowing whether the solution requires changes to its website, advertising strategy, customer experience, pricing or product presentation. AI can potentially interpret that objective, divide it into smaller pieces of work and then organise the resources required to complete them.
At the centre of this strategy is Uma, Upwork’s AI work agent. The system is being developed not primarily as a replacement worker but as an orchestration layer connecting customers, independent professionals and AI capabilities. The distinction matters. Instead of asking one AI model to complete an entire complex project, the platform can potentially decompose the work into a sequence of smaller tasks. Some may be suitable for automation. Others may require a specialist. Some may involve an AI producing an initial result before a professional reviews, corrects or develops it further.
Upwork has an unusual advantage in attempting this because it possesses years of information about actual commercial work. Its marketplace has recorded project descriptions, skills, hiring decisions, completed assignments, payments and customer feedback across millions of engagements. That historical information can help the company understand how successful projects are structured and which combinations of skills tend to produce satisfactory outcomes. In an AI economy, this type of proprietary workflow information may become considerably more valuable than conventional marketplace data because it provides examples not simply of what people say they can do, but of work that customers were willing to pay for and subsequently accepted.
This is particularly important because evaluating professional work remains one of AI’s largest unresolved problems. Certain assignments can be checked relatively easily. Software either functions or it does not. A mathematical proof can be tested. A structured data transformation can be compared against a defined result. Most professional work is considerably less clear. There may be no objectively correct advertisement, architectural concept, marketing strategy, presentation or piece of writing. Whether the result is successful depends partly on the expectations and judgement of the person commissioning it.
Upwork estimates that much of the activity on its platform falls into these more qualitative categories. That creates a problem for fully autonomous AI because producing something is not the same as knowing whether the result meets the client’s actual requirements. The company’s Human+Agent Productivity Index is intended to investigate this gap using real commercial projects rather than traditional academic AI tests.
The initial study examined more than 300 previously completed Upwork assignments across fields including writing, marketing, consulting, data science, engineering, administrative support and software development. The company deliberately selected relatively straightforward, clearly defined projects where AI agents would have a realistic opportunity to succeed. Even with that advantage, agents operating independently struggled with many tasks. Upwork found that introducing experienced human professionals to review outputs and provide additional direction could increase completion rates by as much as 70% compared with agents working alone.
The result supports a very different interpretation of AI productivity from the idea that machines simply replace workers. A substantial part of the value may instead come from reducing the amount of human time required to produce an acceptable result while preserving human judgement at critical points. The reason becomes clearer when examining how knowledge work is specified. Clients frequently do not provide perfect instructions because they do not know precisely what they want until they see an initial result. A designer may need to show several versions before a customer can articulate what feels wrong. A marketing strategy can require multiple discussions before the true commercial objective becomes apparent.
Humans handle this ambiguity through experience, intuition and conversation. Current AI systems remain much less reliable when requirements are incomplete, contradictory or dependent on unstated preferences. The problem becomes greater when assignments involve several forms of information simultaneously. A digital project might combine written content, visual design, software, customer experience, marketing and business strategy. AI systems can perform individual parts increasingly well but still struggle when responsibility extends across a long sequence of interdependent decisions.
This suggests that the future division between people and AI may not follow traditional job titles. Instead, individual occupations could be broken into components, with machines handling some portions while people become increasingly responsible for direction, judgement, verification and unusual cases. That would significantly change the structure of freelance work.
Lower-value production tasks are likely to experience substantial pressure. If an AI tool can create a basic logo, translate routine material or prepare an elementary summary within seconds, businesses will have progressively less reason to pay a professional to perform the entire process manually. But reducing the cost of those individual tasks does not necessarily reduce the overall amount of work companies undertake.
The history of technology contains repeated examples of falling production costs increasing demand. Cheaper computing did not eliminate software development. It dramatically expanded the number of applications businesses could afford to create. More capable AI could have a similar effect on digital work by making projects economically feasible that companies would previously have considered too expensive. Personalised software provides one example. Historically, organisations usually purchased standard products because developing custom applications for individual teams or users was uneconomic. AI-assisted development could substantially lower those costs, creating demand for far more specialised software than the traditional technology industry could realistically produce.
The same logic could apply across marketing, design, research, consulting and other professional services. If AI reduces the cost of delivering a project, businesses may choose to undertake substantially more projects rather than simply spending less on the work they already commission. This is where Upwork sees another potentially important change emerging. AI agents may become customers of human labour as well as competitors with it.
The conventional marketplace has humans on both sides: a person or company posts a requirement and another person accepts the assignment. In an agentic economy, the party deciding that human expertise is needed could increasingly be software. An AI assistant attempting to solve a complicated problem may reach a stage at which it cannot confidently evaluate its own output. Instead of simply failing or returning an uncertain answer, it could obtain specialist human assistance.
Upwork has already begun connecting its marketplace directly into major AI environments. Businesses can discover professionals through AI interfaces, while integrations are expanding the ability to reach human expertise without leaving the software environment in which the task began. The longer-term implication is more significant than simply making online recruitment more convenient. AI agents could eventually identify when they require external human judgement and locate an appropriate expert themselves.
A legal research agent could request specialist review. A software development agent could obtain a security assessment. A marketing agent could ask a human strategist to evaluate several campaign concepts. A design system could request aesthetic judgement before delivering its final recommendation. This creates the possibility of agentic hiring: machines purchasing small units of human expertise whenever their own capabilities become insufficient.
If this develops at scale, the traditional freelance project could begin to fragment. Instead of one professional spending days completing an entire assignment, experienced workers may increasingly provide shorter, higher-value interventions across many AI-managed workflows. Their economic role shifts from producing every element to resolving ambiguity, providing expert judgement and verifying that machine-generated outcomes are acceptable. A specialist might consequently participate in many projects simultaneously rather than working continuously on one assignment.
This could increase the premium attached to genuine expertise while reducing the value of routine production skills. Knowing how to produce a first draft becomes less valuable when machines can generate drafts instantly. Knowing why the draft is wrong, how it should change and whether the result solves the customer’s actual problem becomes more important.
For employers, this has potentially profound implications. Companies have traditionally organised work around jobs and departments. AI systems may increasingly organise it around outcomes and tasks. A project can be decomposed into components before resources are assigned dynamically according to the capabilities required. That model could alter corporate workforce planning. Organisations might retain smaller groups of permanent employees responsible for strategy, relationships, judgement and institutional knowledge while combining them with AI agents and externally sourced specialists brought into workflows as required.
The boundary between employee, freelancer, software supplier and AI agent could therefore become less distinct. This is not necessarily a simple reduction in labour. It represents a change in how labour is assembled.
For professional-services businesses, the implications are particularly important. Consulting, legal, accounting, marketing, engineering and other knowledge-intensive companies have historically linked revenue partly to the number of professional hours required to deliver work. AI threatens that model because the production time behind many deliverables is falling rapidly. Clients may increasingly become unwilling to pay traditional fees for tasks they know can be partially automated. Professional firms will therefore need to demonstrate value through expertise, accountability, judgement and outcomes rather than the amount of human effort involved.
Freelancers face a similar transition. Competing against AI on tasks that machines can perform cheaply is unlikely to be sustainable. The stronger position may be using AI to increase personal capacity while developing expertise in areas where customers still require human responsibility and judgement.
That does not remove concerns about the labour market. Greater productivity can increase expectations. If professionals become capable of producing several times as much work, businesses may simply demand more output rather than allowing people to work less. AI agents also operate continuously, potentially creating a workplace where projects remain active around the clock and employees feel pressure to supervise automated systems long after traditional working hours. The growth of gig and project-based employment creates additional concerns around income stability, employment protections and the increasingly fragmented nature of professional careers.
Technology-driven labour transitions rarely develop smoothly. Companies overreact, reverse decisions and experiment with organisational structures before more stable models emerge. Customer service provides a visible example. Some companies initially predicted that generative AI would dramatically reduce the need for human support teams, only to discover that automated systems could not reliably handle every customer situation. The resulting model increasingly combines automation for straightforward interactions with people handling complex, emotional or unusual cases.
Knowledge work may follow a similar trajectory. AI is likely to eliminate particular tasks much faster than it eliminates entire professions. The important question then becomes what happens to the remaining human work and whether workers can move towards the higher-value components quickly enough. Upwork is effectively betting that they can.
The strategy also suggests why marketplaces with proprietary data may occupy an unexpectedly important position in the AI economy. Large language models have access to enormous amounts of general information, but they have much less knowledge about the commercial process through which real work is commissioned, evaluated and accepted. Platforms that possess this information can potentially teach AI not simply how to generate an output but how professional work is structured and what customers consider successful.
Upwork says Uma is increasingly embedded throughout the process of defining projects, assessing potential talent, starting contracts and monitoring work. The marketplace is consequently becoming more deeply involved in the workflow itself rather than simply introducing buyers and sellers. That distinction is strategically important. Digital marketplaces traditionally make money by facilitating transactions. An AI-driven work platform could participate much earlier, helping determine what needs to be done, how the project should be structured, which resources should perform it and whether the result is acceptable.
If successful, the platform moves from matchmaking towards orchestration. For businesses, that could reduce one of the largest inefficiencies associated with external talent: managers frequently know the outcome they need but lack sufficient technical knowledge to define the exact combination of specialists required to achieve it. An intelligent orchestration layer could potentially bridge that gap.
The result could also broaden access to professional expertise. Small businesses that cannot employ permanent teams of designers, developers, analysts and marketing specialists could combine AI systems with independent professionals on demand. The cost of accessing sophisticated capabilities could therefore decline substantially. This has implications far beyond the freelance economy. The same model could eventually influence how companies organise project teams, outsource specialist work and manage temporary expertise across almost every knowledge-intensive industry.
It also challenges one of the most common assumptions surrounding AI and employment. The debate is frequently framed as a contest between human workers and machines. In practice, the emerging labour market may be considerably more complicated. AI systems can replace certain tasks, enhance the productivity of workers, create new categories of work and simultaneously become customers purchasing human expertise.
That final development may prove especially important. An internet populated by autonomous agents will still encounter situations that require judgement, verification and responsibility. If those agents can locate and purchase human expertise dynamically, people could become an on-demand layer within machine-driven workflows rather than simply being displaced by them. The economic value of human work would then move progressively away from producing routine outputs and towards deciding what should be done, resolving ambiguity and determining whether the result is acceptable.
The transition is unlikely to be painless. Some categories of work will shrink, skills will become obsolete and companies will experiment aggressively with replacing labour. Productivity gains could also place new pressure on workers as expectations increase. Yet the evidence emerging from real commercial assignments suggests that current AI agents still perform very differently when left alone compared with when knowledgeable professionals guide them.
For companies, the strategic lesson may therefore be less about choosing between employees and AI and more about redesigning work so that each performs the functions where it creates the greatest value. The organisations that succeed may not be those that automate the largest number of jobs. They may be those that become best at dividing work between machines and people.
Upwork’s transformation provides an early example of what that could look like. The company that once built its business connecting businesses with freelancers is increasingly preparing for a world where the customer, the worker and the manager of the project may each be some combination of human and machine. If that model spreads, AI will not simply change how people work. It will change the architecture through which work is commissioned, organised, delivered and valued.
Source: CIJ.World Research & Analysis Team
