There is a stage in a startup where the CTO is expected to know everything.
They know which engineer is working on which Jira ticket, which production bug is still open, which customer is waiting for an integration, which pull request needs review and why the last release was delayed. They may still be writing code themselves while also handling architecture, hiring, security and product decisions.
In the first few years, that can be exactly what a startup needs. When the engineering team is small, having the CTO close to the code keeps decisions fast.
The problem starts when the company grows but the engineering model stays the same.
The CTO is still doing the work they did when the company had five engineers, except now there are 15, 25 or 40 people depending on them for decisions and updates.
That is when engineering capacity starts becoming an organizational problem rather than simply a hiring problem.
The numbers behind the problem
The economics alone explain why founders need to think carefully about engineering capacity. The U.S. Bureau of Labor Statistics reported a median annual wage of $133,080 for software developers in May 2024, with the profession expected to grow 17% from 2023 to 2033. That is salary alone, before benefits, recruiting, equipment and other employment costs.
But adding another engineer does not necessarily mean adding the same amount of useful shipping capacity.
DORA’s 2025 research provides an interesting example. Its research found that a 25% increase in AI adoption was associated with an estimated 1.5% decrease in delivery throughput and 7.2% decrease in delivery stability. DORA describes these as associations rather than proof of causation, and points to larger batches of changes as one possible explanation: AI can make code production faster, while larger changes can become harder to review and more likely to create instability.
What the DORA data says
| Metric | Estimated change associated with 25% more AI adoption |
| Delivery throughput | -1.5% |
| Delivery stability | -7.2% |
This is not an argument against AI.
It is an argument for paying attention to the entire delivery system.
If developers can produce code faster but reviews, testing, deployment and incident response cannot keep up, the bottleneck simply moves somewhere else.
Your developers can write more code and still ship less
This is one of the most interesting engineering problems in 2026.
Stack Overflow’s 2025 Developer Survey collected responses from 49,019 developers across 177 countries. It found that 84% of developers use or plan to use AI tools in their development process. At the same time, 46% said they distrust the accuracy of AI output, while 66% reported frustration with AI-generated solutions that are almost right but still require additional work.
That creates a very practical problem for startups.
A developer may now produce a feature faster, but someone still has to determine whether the implementation is correct, whether the tests cover the important cases, whether the change works with the existing architecture and whether it is safe to put into production.
The bottleneck has moved from writing code to reliably getting code into production.
That is why simply saying “we need more developers” can be the wrong diagnosis.
Look at your engineering flow, not just headcount
A CTO should be able to look at the path from an idea to production and identify where work is accumulating.
A typical flow looks like this:
Product requirement → Development → Code review → CI/tests → QA → Deployment → Monitoring → Production
If development is fast but pull requests sit open for three days, development is not the bottleneck.
If code reviews are fast but QA takes a week, adding another developer may not solve the problem.
If QA is fast but deployments are risky because the changes are too large, the problem may be release engineering.
And if every one of those decisions still requires the CTO, the bottleneck may be organizational.
This is where engineering metrics become useful.
DORA measures software delivery using metrics including deployment frequency, lead time for changes, change failure rate and time to restore service. These metrics are much more useful than simply asking how many tickets the team closed this month because they measure the actual path from development to reliable production software.
A real example: Stripe deliberately did not simply hire faster
Stripe is a useful case study because it has publicly documented how its engineering organization changed as the company scaled.
Stripe explains that as its products grew to serve millions of businesses and process billions of dollars, it faced the obvious temptation to grow its engineering organization as quickly as possible.
Instead, Stripe says it deliberately added engineers at a slower rate than its user base was growing.
The reason was not that Stripe did not need engineering capacity. It was concerned that rapid engineering growth could create silos, reduce user impact per engineer and make it harder to change the way teams worked. Stripe chose to keep teams relatively small and continuously adapt its organization as the company grew.
That is an important lesson for a startup.
Engineering scale is not the same thing as engineering headcount.
You can have more engineers and create more coordination overhead at the same time.
So what happens to the CTO?
The CTO does not suddenly stop being technical.
In fact, the opposite should happen.
As the product becomes more complicated, the CTO’s technical judgment becomes more valuable. Architecture decisions become more expensive to reverse. Security becomes more important. Infrastructure decisions affect more customers. Technical debt starts influencing roadmap speed. Hiring senior engineers becomes more important.
The question is whether the CTO needs to personally own every operational step around those areas.
If the CTO is spending a large part of the week assigning routine tickets, asking developers for status updates, reviewing ordinary bugs and coordinating work that another capable engineering lead could own, that is a signal worth investigating.
It does not automatically mean the company needs an external team.
It might need an engineering manager.
It might need a tech lead.
It might need better project ownership.
It might need to reduce the roadmap.
Or it might need additional engineering capacity.
The important thing is to identify the actual bottleneck first.
Where a dedicated external team can help
Suppose your internal team owns the core product, architecture and technical direction, but the roadmap also contains ten enterprise integrations, a major QA automation project, a new backend service and several frontend initiatives.
Hiring every person permanently may be the right decision for some companies.
But not every piece of work needs to become a permanent internal headcount decision.
A dedicated engineering team can take ownership of a clearly defined area of work while the internal CTO remains responsible for the architecture and technical direction.
For example:
Internal CTO and engineering leadership
Architecture
Security
Product and technical strategy
Core systems
Senior engineering hiring
Dedicated engineering team
Integrations
QA automation
Defined frontend or backend modules
Infrastructure projects
Technical debt
Specific AI implementation work
The important part is not whether the team is internal or external.
The important part is whether someone other than the CTO can own the work from requirements through delivery.
If an external team still needs the CTO to assign every ticket, answer every technical question and chase every deadline, you have not created additional engineering capacity. You have created additional management work.
The five-minute CTO test
Open the CTO’s calendar for the last month and look at where the time actually went.
Then classify the work.
High-leverage CTO work
Architecture, security, technical strategy, product decisions, scaling, senior hiring and major technical risks.
Delegatable engineering work
Routine ticket assignment, status chasing, ordinary bug investigation, release coordination, QA follow-up and clearly defined development projects.
There is no magic percentage that tells you when the balance is wrong.
But if your CTO is spending a significant amount of their week on work that another capable team could own, it is worth asking whether the company has outgrown its original engineering structure.
That is usually a better question than asking whether you simply need to hire five more developers.
The real engineering bottleneck
The biggest mistake is assuming that the solution to every engineering problem is more headcount.
Sometimes it is.
Sometimes the team needs better testing.
Sometimes it needs smaller releases.
Sometimes it needs another technical leader.
Sometimes the CTO needs to stop being the only person who can make a decision.
And sometimes the company genuinely needs another engineering team that can take a meaningful part of the roadmap off the internal team’s plate.
The goal is not to take engineering away from the CTO.
The goal is to give the CTO enough engineering capacity and ownership around them that their time goes toward the problems where their judgment matters most.
The question I would ask any growing founder is simple:
If your CTO disappeared from Jira, bug triage and daily engineering coordination tomorrow, would the engineering organization keep moving?
If the answer is no, the next engineering investment may not be another developer.
It may be the ownership structure around the developers you already have.
Sources:
U.S. Bureau of Labor Statistics, Software Developers, Quality Assurance Analysts, and Testers – https://www.bls.gov/ooh/computer-and-information-technology/software-developers.htm?utm_source=chatgpt.com
DORA, 2025 State of AI-assisted Software Development – https://www.bls.gov/ooh/computer-and-information-technology/software-developers.htm?utm_source=chatgpt.com
DORA, Impact of Generative AI in Software Development – https://dora.dev/ai/gen-ai-report/report/?utm_source=chatgpt.com
Stack Overflow, 2025 Developer Survey – https://survey.stackoverflow.co/2025/?utm_source=chatgpt.com
Stack Overflow, 2025 AI Survey – https://survey.stackoverflow.co/2025/ai?utm_source=chatgpt.com
Stripe, Scaling Engineering Organizations – https://stripe.com/guides/atlas/scaling-eng?utm_source=chatgpt.com
