As artificial intelligence reshapes how companies hire, structure teams, and measure productivity, organisations are entering a new era where judgment, systems thinking, and domain expertise matter more than routine execution.
In this exclusive conversation, Erik Stettler, Chief Economist at Toptal, explains why AI is driving a reallocation of talent demand rather than mass job elimination, why entry-level hiring is slowing, and how companies are redesigning roles around human-AI collaboration.
Stettler also unpacks the future of hybrid/remote work, the risks of underinvesting in junior talent, and what will define winning organisations in 2026 and beyond.
Read his full insights below:
How AI is reshaping workforce demand in real time
The data points to a clear reallocation of demand rather than broad job displacement. Companies are concentrating hiring on experienced professionals who can translate AI capabilities into real business outcomes with measurable ROI.
That includes developers who can operate across the full stack in terms of integrating with product, data, and security, as well as functional experts in areas such as finance, operations, and marketing who can apply AI directly within their domains. In simple terms, demand is strongest for people who can bridge AI and real-world use cases from either direction.
At the same time, conditions are weaker in other parts of the market, particularly for junior and more generalist roles.
AI is not eliminating jobs at scale today, but it is slowing hiring, especially at the entry level, as companies reassess how much human capacity they actually need. We are seeing tightening expectations, with companies becoming more selective and constantly raising the bar for what qualifies as true value add.
Taken together, these trends reflect a shift toward systems-level talent who can design, implement, and manage AI-enabled workflows across both technology and business functions. The defining capability is no longer simply execution, but the ability to translate tools into outcomes.
CEOs rethinking team structures and role design
Roles are evolving toward systems-level ownership rather than isolated task execution. Every role increasingly involves framing the right problems, using AI tools to explore solutions, and applying judgment to interpret results and drive decisions.
The middle layer of work - data gathering, analysis, and initial execution, is increasingly handled by AI. Human value is shifting toward the beginning and end of workflows in terms of defining the problem and then determining what to do with the answer.
One way to think about this is that AI expands the total potential of what each person can do, but thereby raises the standard for how that work is directed and evaluated.
This shift has clear structural implications. Teams can operate at a higher level of output with fewer people, which points toward smaller, more leveraged organisations.
At the same time, traditional functional boundaries become more fluid, as AI reduces the friction of working across domains. The result is not the disappearance of roles, but their evolution into positions that are more managerial, integrative, and judgment-driven.
Balancing talent investments: junior roles vs senior talent
Leaders need to think in terms of a portfolio strategy. In the near term, experienced specialists are essential for designing, implementing, and governing AI-enabled systems. That capability is scarce and directly tied to ROI, which is why demand for senior talent remains strong.
At the same time, investing in junior talent is critical for long-term competitiveness. The difference is that junior roles now need to be structured differently.
Organisations have an opportunity to train early-career professionals from the start in how to work effectively with AI systems, rather than retrofitting those skills later.
One important dynamic is that AI tools are becoming both more powerful and more accessible. While it takes experienced professionals to build and implement systems, their use can be learned relatively quickly.
Over time, this will help junior contributors to rapidly advance their execution capability. However, judgment, creativity, and accountability remain experience-driven and are not easily automated.
The organisations that perform best will be those that balance short-term execution with long-term capability building.
Underinvesting in junior talent may seem to improve efficiency today on paper, but it risks creating a structural gap in leadership and judgment in the future.
Rising layoffs and strong demand for high-skilled talent
These trends reflect segmentation rather than contradiction. Strong demand for high-skilled talent is driven by the complexity of AI implementation, which requires experienced professionals not only in engineering, but across business functions, combined with the judgment to deploy these systems effectively.
At the same time, layoffs are occurring across entire organisations, not just within these high-demand skill segments. Many are driven by macroeconomic uncertainty, pandemic-era over-hiring, and capital reallocation toward AI investments.
Layoffs are often framed by the companies in the context of AI efficiency gains, even when the underlying drivers are broader, as that framing shifts the narrative from cost-cutting or past errors to innovation.
The result is a labor market that is simultaneously tightening and restructuring. Companies are reducing overall headcount in some areas while increasing selectivity and investment in others. This is a reconfiguration of demand, not a simple expansion or contraction.
AI shifting how companies measure productivity
Productivity is increasingly being measured in terms of output and outcomes rather than the individual steps involved in producing them. One interesting dynamic is that any task that can be clearly quantified as a traditional productivity metric is also a strong candidate for automation. As a result, task-level measurement becomes less meaningful over time.
The most valuable human contributions of judgment, creativity, and relationship-building are not easily captured through isolated metrics. Instead, they are reflected in the quality and effectiveness of final outputs. This shifts the focus toward what is achieved, rather than how each step is performed.
In that sense, productivity becomes a function of leverage. It is no longer just about how much work a person can do, but how effectively they can use integrated systems with human and technological contributors to produce results. As AI expands execution capacity, the limiting factor becomes judgment and management.
Remote work, and the biggest strategic risk
The resilience of remote and hybrid work is closely tied to the nature of high-skilled roles. The same technologies that enable distributed work are the ones these professionals use every day. At the same time, as roles become more specialised and senior, companies increasingly need to access talent beyond their immediate geography.
The data shows that remote and hybrid work has reached a level of market maturity. It is no longer a temporary adjustment, but a structural component of the talent market, with demand trends increasingly aligned with broader hiring patterns.
The biggest strategic risk for companies is misallocating talent during this transition. Organisations that fail to secure the expertise required to implement AI effectively, both technically and operationally, risk falling behind in ways that compound over time.
This is not simply about adopting new tools, but about deploying them in ways that minimise risk and generate real returns.
In this environment, access to the right talent is not a marginal advantage; it is a structural one. Companies that source globally and act deliberately will build lasting capability. Those that do not will find themselves consistently behind.
What defines a winning organisation in 2026 and beyond
In the near term, winning organisations will be defined by their ability to move from experimentation to disciplined implementation. That means identifying high-impact workflows, integrating AI in ways that deliver measurable ROI, and building the governance structures required to ensure reliability and safe use.
The key differentiator will not be access to AI tools, but the ability to deploy them effectively at scale. Many organisations can experiment; far fewer can operationalise and scale.
Looking toward 2030, AI will be deeply embedded in daily workflows and will no longer be a differentiating capability on its own. Proficiency with these tools is already becoming table stakes in high-skilled roles. What will distinguish leading organisations and professionals is the ability to orchestrate human and AI systems effectively, combined with strong domain expertise and sound judgment.
The definition of a high-skilled workforce is therefore shifting. It is no longer centered on performing specialised tasks, but on designing, managing, and continuously improving systems that combine human and machine capabilities. As execution becomes more scalable, the constraint moves elsewhere.
The organisations that succeed will be those that recognise that capability can now scale faster than judgment, and build accordingly.