Web Summit’s first State of the Industry report captures a technology community optimistic about AI but increasingly busy, with respondents anticipating leaner teams and fewer entry-level roles.
The promise of AI at work is easy to understand: less time spent on routine tasks, faster access to information and more capacity to build. What happens to that additional capacity is a more consequential question.
Web Summit’s first State of the Industry survey offers an early indication. Nearly six in ten respondents said AI had increased their workload, while just one in five said it had reduced it. Yet roughly six in ten were optimistic or very optimistic about AI’s impact on the future of work.
The tension runs through the report. Respondents see opportunities to achieve more, but many also expect smaller teams and fewer entry-level positions. Taken together, the findings raise questions about how companies will organize work, develop employees and decide what greater productivity should make possible.
The State of the Industry 2026: AI Edition draws on responses from 1,455 people across Web Summit’s global community, including founders, investors, engineers and creators. Conducted during July and August 2026, it provides a snapshot of an industry whose enthusiasm for AI sits alongside concerns about its consequences.
When faster becomes the new baseline
The report’s detailed findings show that 59% of respondents experienced an increase in workload, compared with 20% who reported a reduction and another 20% who saw little change.
The increase was particularly common in businesses close to the technology itself. In AI and machine-learning infrastructure, 70% said their workload had risen, followed by 68% in SaaS and B2B software and 67% in hardware and the Internet of Things. Among engineering and product respondents, the figure was 64%.
Being close to AI appears, in this sample, to offer little insulation from having more work to do.
One explanation emerges in the written responses: faster production raises expectations. An engineering respondent in Europe described AI accelerating delivery while leaving insufficient time to review outputs that people may trust too readily.
The survey does not establish whether respondents are working longer hours or producing more within the same day. Workload can also grow during a period of experimentation, as teams introduce tools while continuing to operate existing systems. It would therefore be misleading to read the result as proof that AI reduces efficiency.
What it does challenge is the assumption that time saved automatically becomes time available. Once a company knows a task can be completed faster, the faster turnaround can become the expected standard.
That can support growth. A startup may be able to serve customers or test ideas it previously lacked the resources to pursue. But it also requires decisions about how much additional work a team can absorb and how much time should remain for checking, learning and improving what it produces.
The appeal of a smaller company
Respondents’ expectations about team size help explain why workload deserves attention.
Some 42% expected smaller teams with higher output. Another 31% anticipated teams staying roughly the same size but changing their mix of skills. Only 9% expected larger teams using AI.
The preference for leaner teams was especially pronounced among founders: half expected smaller teams to deliver more, compared with 46% of C-suite respondents. Half of respondents at companies with one to nine employees shared that expectation.
For an early-stage business, the appeal is clear. Reducing the resources needed to develop a product or reach customers can give founders more room to experiment. Tasks that previously required several hires or external support may become manageable within a smaller team.
However, producing an output is only part of operating a business. Someone still needs to understand the customer, choose between competing priorities, verify important information and take responsibility when something fails.
The report describes expectations about leaner organizations rather than evidence that these organizations will perform better. Its more interesting implication is that companies may begin redesigning roles around a different assumption about what one person can deliver.
That puts the composition of work under scrutiny. If employees spend less time creating initial outputs, their ability to assess, integrate and act on those outputs becomes more consequential.
Growth can coexist with a narrower route into work
On employment overall, respondents were far from united. Over the next two years, 39% expected headcount in their field to grow, 33% expected it to fall and 28% anticipated no significant change.
The outlook for entry-level positions was more concerning. Some 42% expected their company to have fewer such roles. Among C-suite respondents, that rose to 50%; among investors, it reached 58%.
These are forecasts, not recorded hiring decisions. Even so, the distinction between total employment and junior hiring matters. A sector can expand while becoming harder to enter, particularly if companies increasingly seek experienced people who can work independently with AI.
For an individual employer, hiring fewer juniors may appear commercially sensible. Across an industry, it raises a longer-term question about how experience is developed.
Early-career assignments provide opportunities to learn how to interpret a request, recognize mistakes, respond to feedback and understand why an apparently straightforward task becomes complicated in practice. If AI takes on more of those assignments, employers will need other ways to develop that judgment.
Nidhi Naithani, Web Summit’s senior vice president of technology operations, addresses this concern in the report’s opening remarks, emphasizing the importance of helping junior developers learn their craft as AI becomes capable of building more from scratch.
Students offered a different outlook: 55% expected entry-level roles to grow. They represented just over 5% of respondents, so the finding concerns a small subgroup. Nevertheless, the contrast is worth examining. People preparing to enter the industry and those already making investment or leadership decisions may be working with different expectations about its future.
For businesses building talent pipelines in Qatar and across MENA, the issue is practical. If entry-level work changes, recruitment, supervised assignments and professional development will need to change with it.
Critical thinking remains a shared priority
The report also explores whether AI is changing how people think.
Overall, 55% said regular use strengthened their critical-thinking and problem-solving skills, while 26% said it weakened them and 19% perceived little difference. Engineers were more skeptical, with 37% reporting a weakening of those skills.
These are assessments of personal experience, rather than tests of cognitive performance. The findings show how respondents perceive AI’s influence; they cannot settle whether it improves reasoning.
The distinction matters because feeling more capable and developing a capability are different things. A useful answer may help someone explore a problem more deeply. It may also make it easier to accept a conclusion without examining how it was reached.
The survey’s findings on future skills provide a useful counterpoint. Women placed greater emphasis on emotional intelligence and people skills than men, at 41% versus 27%. Men were more likely to select technical and engineering depth, at 37% versus 24%.
Yet the strongest shared priority was critical thinking, selected by 67% of women and 58% of men, ahead of the other skills listed for both groups.
That common ground is more instructive than treating technical and interpersonal skills as opposing choices. As tools become more capable, respondents still expect value in the ability to question information and make sound decisions.
MENA’s place in a market of multiple tools
The report suggests that using AI increasingly involves more than one provider. Respondents used an average of 2.3 providers for work, and 43% used three or more.
Around a third of respondents in MENA and Asia used alternative open-weight models, compared with 15% in North America. Chinese models, including DeepSeek, Qwen and Kimi, were also more common in MENA and sub-Saharan Africa, according to the report.
These findings point to a broader range of tools in use, although the survey does not establish why respondents choose them. Price, language performance and preferences about deployment would require separate investigation.
For companies, this variety creates opportunities to match tools to particular tasks. It also makes it important to understand where company information goes, how outputs are checked and which uses have been approved.
Regional differences appeared in investment sentiment too. Around 31% of MENA respondents considered AI investment too high and expected a slowdown or sharp fall, compared with roughly half in Europe and North America.
Globally, 34.6% believed AI investment was still in its early stages and would continue growing, while 32% expected a gradual slowdown and 10.2% anticipated a sharp fall. Concern about investment levels did not amount to a consensus that a crash was imminent.
The regional comparisons should be read as signals within Web Summit’s community. The report does not publish regional subgroup sizes, and its sample cannot establish how all businesses in Qatar or MENA view AI.
What greater capacity should make possible
The report leaves companies with a more demanding question than whether employees should use AI. It asks, indirectly, what organizations intend to do with the capacity it creates.
More output may be valuable. So may faster service, better decisions or the ability to attempt work that was previously beyond reach. Each requires attention to how work is assigned, reviewed and rewarded.
The same applies to people development. If companies expect employees to exercise greater judgment while offering fewer opportunities to acquire it, they risk weakening the capabilities on which their AI ambitions depend.
There is room for a more deliberate approach: preserve time for review, give junior employees supervised responsibility and assess improvements through the quality of completed work as well as its speed.
The survey suggests considerable willingness to embrace AI. Turning that willingness into durable progress will depend on whether companies invest as much thought in the people doing the work as in the tools accelerating it.
About the survey: Web Summit surveyed 1,455 members of its global technology community during July and August 2026. The sample leaned toward founders and product builders, and the findings reflect respondents’ experiences and expectations.
Source: Web Summit, State of the Industry 2026: AI Edition. Read Web Summit’s announcement.
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Last Updated on November 5, 2026 by Safiya K
