Alexey Spas is the founder and CEO of Instinctools, a software engineering company focused on AI-powered digital solutions.
For most of the software industry’s history, when a software backlog started growing faster than the team could handle it, a CTO or product leader knew what came next. It was either opening new roles or sending some work to an outside partner.
Whichever the option, the rule of thumb used to be the same: More software to build meant more people needed to build it, until AI put that long-held assumption into question.
A senior engineer working with coding agents can now produce far more raw output than before. That is why software delivery is beginning to reorganize around a different unit: a small senior team surrounded by AI-enabled workflows.
AI Pod: The Delivery Unit That Has Humans And Agents
The label is still loose, but the operating idea is easy to picture: An AI pod is a compact, senior-led engineering team accountable for a defined software outcome. Specialized AI agents support the delivery flow from requirements drafting to code generation and testing, while humans keep their hands on the wheel when it comes to business context, quality, security and the final result.
And just like that, AI has completely reshuffled the deck for software engineering. An age-old dilemma is now taking on new contours: If companies need a different kind of delivery unit, where should it live? Some organizations will have good reasons to build it in-house. Others may prefer to bring in an external pod. For many, the most practical answer may sit somewhere in between.
An in-house model builds institutional knowledge into the pod.
A big part of the appeal of an in-house AI pod is its lean structure, and it’s not hard to see why: Hiring one or two senior engineers supported by AI agents is a very different proposition from building a cross-functional team of five-plus people.
The strongest case for keeping the pod internal, though, may have less to do with headcount than with proximity of in-house engineers to the product, business context and the decisions. Moreover, the lessons learned while building AI-enabled delivery workflows remain inside the organization.
That advantage takes time to build. Giving experienced developers access to coding agents does not immediately produce an effective AI delivery system. Teams still have to determine what can safely be delegated, where human review belongs, which quality gates can be automated and how AI-generated work fits into the rest of the software lifecycle.
There is also the expertise question. A small pod can still draw on a broad range of skills. A modernization effort can suddenly require security input, an AI-heavy feature may call for data engineering or a customer-facing workflow may need experienced UX support. Building all of those competencies internally makes sense when the demand is sustained. It is harder to justify when the need appears for only a few weeks.
Outsourcing shortens the learning curve and brings cross-project experience.
There is no secret AI stack tool available only to software vendors. Anyone can access the same models, coding assistants and agentic frameworks. The advantage of an external pod usually comes from reducing the distance between access to AI and a delivery system that works.
If spinning up a few agents could solve the software delivery problem overnight, things would be simple. In practice, faster generation creates its own downstream load: More code has to be reviewed, tested, integrated and checked for security. The challenge is turning that AI-driven speed into production-ready software.
An outsourced team that has already cut its teeth on different projects can bring patterns refined across more than one production environment: agent configurations, evaluation methods, review workflows, reusable components and quality controls. I know—there’s a lot of skepticism when software development vendors talk about their “unique expertise.” But in this case, uniqueness does not mean exclusive knowledge that nobody else can acquire. It just implies a shorter route through mistakes someone else has already made and experience that has been tested often enough to become adaptable.
There is also a flip side to that breadth of experience. An external pod does not begin with the same institutional knowledge as an internal team, nor is it naturally as close to every product decision. The model makes the most sense when the value of faster ramp-up, cross-project experience or temporary capacity outweighs the benefits of having a team deeply embedded in the product.
The hybrid model keeps more options open.
When a company needs AI-enabled delivery capability now but is not yet certain how much of it should become a fixed part of the organization, a hybrid model can be considered. Demand may surge around a product launch, modernization program or backlog push, and then ease again. An external pod can take the edge off this sudden spike without turning peak capacity into permanent headcount.
The same arrangement can also serve a longer-term purpose. A company that ultimately wants the capability in-house can use an outsourced pod as a working prototype for the team it may later build itself.
A “borrow, prove, then build” approach gives leaders a chance to observe which roles they repeatedly need, which workflows survive production, where human judgment remains essential and which metrics genuinely improve. The parts that prove strategic can then move inside the organization with much less guesswork.
Why The Operating Model Does Not Have To Be Set In Stone
AI pods are making the old build-versus-buy decision more nuanced. For leaders, the choice comes down to a few practical questions: How enduring is the demand? How important is institutional knowledge? How quickly should the capability become operational?
The answers may change as the organization learns. That is probably the main point. AI is reshaping software delivery quickly enough that the operating model that fits today does not have to be the one a company commits to for good.
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