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Atlassian CEO Mike Cannon-Brookes predicts that AI will make software cheaper to build, prompting organizations to create more of it—not that AI has already proved software output or developer employment will rise. The claim is an argument about what may happen when lower costs make more projects worthwhile; the available reports do not establish the outcome.
What Cannon-Brookes predicts
In remarks attributed to him from a Q2 earnings call, Cannon-Brookes said: “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 quotation appears in a secondary roundup by Blossom Street Ventures; its report does not establish the exact call date or provide a primary transcript. Treat both the expected increase in software and the forecast of more developers as his predictions, not measured results.
The underlying idea is induced demand: if it takes less time or money to create software, businesses may pursue projects they previously could not justify. That could mean tools for more teams, processes, or specialized needs, as well as faster delivery of existing projects. A lower cost per project does not by itself prove that total software output will grow; organizations would need to choose to spend the saved time and money on additional work.
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Andrew Haschka makes a similar case in a Tech Lead Journal interview transcript updated May 10, 2026: “Now we’re making it easier to build software which means that more software gets built, not less software.” He argues that lower barriers can allow more people to participate and organizations to take on more projects. This is a plausible explanation for Cannon-Brookes’s forecast, but an interview argument is not evidence that AI has already increased software output across the industry.
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The roundup also attributes several figures to Cannon-Brookes, but they should be read as company-reported claims rather than independent benchmarks. It says he described Atlassian’s Teamwork Graph as containing 25 years of work data and more than 200 billion objects and connections; it also reports claims of up to 44% more accurate answers and 48% fewer tokens for agents grounded in that graph. The roundup says he reported more than 1 million MCP and CLI users, more than doubling during the quarter. The report does not establish the call year, and the reviewed sources do not independently validate these figures. They illustrate the company’s AI context, not proof of economy-wide productivity or job growth.
Why code generation is not the whole delivery process
Generating code is only one part of delivering dependable software. Haschka emphasizes testing, security validation, compliance, and checking whether a system meets its intended business outcome. If implementation becomes faster while those activities remain bottlenecks, work may shift downstream rather than disappear. Teams should therefore examine the entire workflow instead of treating lines of code or tool adoption as a measure of success.
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Haschka says that organizations his team assesses often spend less than 20% of their time coding. This is his assessment of organizations he encounters, not a general workforce benchmark. Its practical implication is still useful: before expecting AI coding tools to transform delivery, identify where a team actually spends time and where delays occur. Faster coding may have limited effect if review, test environments, approvals, security checks, or release processes constrain completion.
Will AI create more developer jobs?
That remains uncertain. Cannon-Brookes’s expectation of more developers in five years is a forecast, not a verified labor-market result. More software demand and more employment are related possibilities, but they are not the same claim: organizations could build more while changing team composition, reducing hiring in some roles, or using productivity gains to serve existing demand with fewer people. The sources reviewed do not establish AI’s net effect on software employment.
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The Tech Lead Journal discussion includes geographically specific expectations, not realized job counts. It cites 79% of surveyed Singapore DevSecOps practitioners as believing AI would create more engineering jobs. Haschka also discusses a Singapore regional C-suite survey in which 71% of executives believed the optimal human-AI split should be around 50:50. Neither figure should be generalized to other regions or treated as proof of future employment outcomes.
How to assess the claim inside a company
For an organization deciding whether AI is changing software delivery, the useful question is not simply whether developers are using AI. Measure whether the company can complete valuable work it previously deferred, and whether the whole delivery path improves.
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- Track demand: Are teams starting projects or addressing use cases that were previously out of reach, or are they mainly completing existing work at lower cost?
- Measure end-to-end outcomes: Look at delivery time, quality, reliability, security findings, and whether software achieves its intended business result—not just coding speed.
- Find the bottleneck: Check whether testing, review, compliance, data access, or release processes absorb the time saved during implementation.
- Separate output from staffing: Record project volume and role or headcount changes independently. A rise in one does not establish a rise in the other.
Haschka recommends measuring task and workflow outcomes rather than treating staff adoption of AI as success. That is an interviewee’s recommendation, not a finding from an independent comparative study, but it is a sound way to test whether a tool is improving the work that matters.
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The available material supports a clear account of Cannon-Brookes’s view and the reasoning behind it: lower software-creation costs could make more projects worthwhile, and he expects more developers in five years. It does not show how much AI has changed software volume, establish a net effect on jobs, or independently confirm the vendor-attributed performance figures. The most accurate reading is therefore a forecast worth testing, not a settled conclusion about AI’s effect on software or workers.
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