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Atlassian CEO Mike Cannon-Brookes predicts that AI will make building technology cheaper and lead organizations to build more of it—not that software developers will disappear. That is a forecast about what companies may choose to do when development becomes less costly, not proof that AI is already increasing software output or employment.
What does “more software, not less” mean?
The argument is an induced-demand forecast: when a product or service becomes cheaper to create, people may use it to meet needs they previously left unaddressed. Applied to software, lower barriers could make it worthwhile for a company to build tools for more teams, workflows, and business problems, as well as to take on more projects.
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Cannon-Brookes’s remark was attributed to a Q2 earnings call in a secondary roundup by Blossom Street Ventures. The roundup quotes him expecting more developers in five years than today, because the cost of building technology is falling while the amount built is likely to rise. The report does not establish the exact call date, and a primary call transcript was not available in the material reviewed. Treat this as his prediction, not a verified outcome. Blossom Street Ventures’ roundup
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A lower build cost can change which projects are worth pursuing. A company that once could not justify a custom internal tool might decide it is now practical; a team may automate more processes or develop additional services. In this view, AI does not simply substitute for existing coding work. It can also expand the pool of people able to contribute and the number of projects organizations consider feasible.
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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.” That is an interviewee’s argument about what lower barriers could enable, not a measured finding that software volume has already grown. Tech Lead Journal interview transcript
Will software developers disappear?
The available evidence does not establish that developers will disappear—or that AI will increase employment overall. Cannon-Brookes’s expectation of more developers is an executive forecast. It should be kept separate from observed hiring, layoffs, or net job changes.
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More software demand and fewer employees at some companies can coexist. Organizations may expand the number of projects they pursue while changing team size, role mix, or how work is divided. A forecast about total software output does not, by itself, settle what happens to a particular worker or the overall labor market.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11The interview transcript discusses a survey in which 79% of surveyed Singapore DevSecOps practitioners believed AI would create more engineering jobs. That figure records expectations among a geographically specific group, not jobs created. The same discussion reports that 71% of Singapore executives believed the optimal human–AI split should be around 50:50; it is a regional survey view, not a global measure of actual work allocation. Tech Lead Journal interview transcript
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Why writing code is only part of software delivery
Generating code does not complete a software project. The interview with Haschka emphasizes the work of testing, security validation, compliance, and determining whether a result meets the intended business outcome. If implementation gets faster but those steps do not, they may become the constraint on how quickly a team can safely release useful software.
Haschka says organizations his team assesses often spend less than 20% of their time coding. That is his assessment of the organizations they evaluate—not a general benchmark for software teams. It illustrates why measuring only code-generation speed can miss where time and risk remain in a delivery process.
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What would show whether the forecast is coming true?
The key question is not only whether AI helps produce code faster. It is whether lower costs lead organizations to deliver more useful software, and what happens to the work and workforce as they do so. These outcomes need to be measured separately:
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- Software demand: Are companies starting projects or serving use cases they previously considered too costly, or are they mainly completing existing work more cheaply?
- Delivery bottlenecks: Are testing, security, compliance, data handling, and release processes keeping pace with faster implementation?
- Workforce outcomes: Are organizations expanding project portfolios, changing job responsibilities, reducing headcount, or combining these responses?
- Evidence quality: Are claims based on measured results, or on company forecasts, vendor figures, and survey expectations?
Haschka recommends assessing workflow and task outcomes rather than treating staff adoption of AI tools as success on its own. That is his operational advice in the interview, not an independently tested comparison of organizations.
How strong is the evidence behind the claim?
The support for “more software, not less” in these accounts is chiefly explanatory reasoning and executive opinion: lower costs could make more projects worthwhile. The available sources do not quantify how much AI has changed software output or establish its net effect on software employment. Those are separate empirical questions.
The secondary roundup also attributes several product and platform figures to Cannon-Brookes: 25 years of work data in Atlassian’s Teamwork Graph; more than 200 billion objects and connections; “up to” 44% more accurate answers and 48% fewer tokens for agents grounded in that graph; and more than 1 million MCP and CLI users, described as more than doubling during the quarter. The roundup does not establish the earnings-call year or provide independent validation of these company-attributed metrics. They are not independent evidence that AI has increased software output or developer employment. Blossom Street Ventures’ roundup
The balanced conclusion is that cheaper software creation could induce more demand, while the effect on headcount remains unsettled. Whether the forecast proves right depends on what organizations build, whether they can validate and operate it responsibly, and how they adjust their teams—not on code-generation speed alone.
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