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Atlassian CEO Mike Cannon-Brookes predicts that AI will make software cheaper to build—and that organizations will respond by creating more of it, not less. That is a forecast about demand, not proof that AI is already increasing software output or developer employment. The distinction matters: a company can build more while changing its staffing, roles, or spending.
What does “more software, not less” mean?
Cannon-Brookes’s argument is an induced-demand forecast: when the cost and difficulty of building technology fall, organizations may pursue projects and use cases that previously were too expensive or impractical. The result, in his view, would be a larger volume of technology—and potentially more developers to build it.
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Blossom Street Ventures, a secondary roundup of remarks attributed to Cannon-Brookes on a Q2 earnings call, quoted him saying: “I think there’s going to be more developers in the world in five years’ time than there are today. The cost of building technology is going down. The amount of technology we’re going to build is going to go up.” The roundup does not establish the call’s exact date, and the reviewed sources do not include its primary transcript. Treat the statement as the CEO’s expectation, not a verified outcome. Blossom Street Ventures’ roundup
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Why could cheaper software lead to more projects?
If a team can create a useful application or automate a process at lower cost, it may become worthwhile to address needs that once stayed on a backlog or were handled manually. That can increase demand for software even if each individual project requires less coding effort. Andrew Haschka, a GitLab representative interviewed by Tech Lead Journal, made a similar point: “Now we’re making it easier to build software which means that more software gets built, not less software.” The interview presents this as an argument, not a measured estimate of AI’s effect on total software production. Tech Lead Journal interview transcript
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The mechanism is plausible, but it is not automatic. Lower costs could instead mean that organizations deliver the same work with fewer resources, or spend less on software development. Whether new demand outweighs those effects depends on what organizations choose to build and whether they can get the resulting work safely into use.
Does more software mean more developer jobs?
Not necessarily. More projects, more software output, and more jobs are related but separate outcomes. A company could build more while employing fewer people, changing the mix of roles, or reducing headcount elsewhere. The available sources do not establish AI’s net effect on employment overall.
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Cannon-Brookes’s prediction that there will be more developers in five years is a forecast, not a labor-market result. Likewise, the Tech Lead Journal interview discusses a survey in which 79% of surveyed Singapore DevSecOps practitioners believed AI would create more engineering jobs. That figure describes expectations among respondents in Singapore; it does not show that jobs were created, and it should not be generalized to other regions.
The interview also cites a Singapore regional C-suite survey in which 71% of executives believed the optimal human-AI split should be around 50:50. That is a reported view about a desired balance, not a measure of today’s workforce or a universal forecast. Tech Lead Journal interview transcript, updated May 10, 2026
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Why coding is only one part of software delivery
Generating code does not by itself produce a dependable product. Teams still need to test whether it works, validate security, meet compliance requirements, and confirm that the result addresses a real business need. If implementation speeds up but these other steps do not, the bottleneck may move downstream rather than disappear.
Haschka says organizations his team assesses often spend less than 20% of their time coding. That is his assessment of those organizations, not a general benchmark for software teams. His practical recommendation is to evaluate the whole delivery workflow and measure task or business outcomes rather than treating AI adoption by itself as proof of success. These are interviewee recommendations and company perspective, not findings from an independent comparative study. Tech Lead Journal interview transcript
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What the reported Teamwork Graph figures do—and do not—show
The secondary roundup attributes several figures to Cannon-Brookes about Atlassian’s Teamwork Graph and AI-related usage. They may describe company scale or vendor claims, but they do not independently demonstrate that AI has increased software output or employment.
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|---|---|
| 25 years of data about work in the Teamwork Graph | Attributed to Cannon-Brookes in Blossom Street Ventures’ secondary roundup; the call year is not established. |
| More than 200 billion objects and connections in the Teamwork Graph | Attributed to Cannon-Brookes in the same roundup; it is a company-reported scale figure, not an independent assessment. |
| Up to 44% more accurate answers and 48% fewer tokens for agents grounded in the Teamwork Graph | Vendor-attributed claims in the roundup, not independent benchmarks in the reviewed sources. “Up to” describes a maximum claim, not a guaranteed result. |
| More than 1 million MCP and CLI users, described as more than doubling in the quarter | Attributed to Cannon-Brookes; the roundup does not establish the call year or independently verify the figure. |
These statements offer context for how Atlassian describes its data and tools, but they should not be read as evidence that the wider software industry is producing more software or hiring more developers. Blossom Street Ventures’ roundup
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How to assess the forecast
For workers, businesses, and investors trying to judge whether the prediction is playing out, keep three questions separate:
- Is software demand growing? Look for evidence of new projects and use cases, not just faster completion of existing work.
- Are delivery bottlenecks changing? Consider testing, security, compliance, data access, and release processes alongside code generation.
- What is happening to labor? Distinguish project volume and role changes from hiring, layoffs, and employment totals. A survey of expectations or an executive prediction is not an observed job outcome.
The evidence cited here supports a clear account of what Cannon-Brookes expects and why: lower creation costs could prompt organizations to build more. It does not settle whether that demand response will outweigh automation-driven savings, how quickly it might happen, or what the net employment effect will be.
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