The Case for Tiny Teams: Why AI Changes Everything About Software Development
The Case for Tiny Teams: Why AI Changes Everything About Software Development
By
Ross
on •
Aug 22, 2025
AI powered software development is a polarizing topic these days.
On the one hand, you have the true believers. Hyping it to the max: “coding is now a useless skill and all developers will soon be wholly replaced with AI!” and “If you’re a computer science major, drop out now!” (This is bad advice).
On the other hand are the skeptics. Cynics and seasoned developers who’ve seen quite a few hype cycles and are mostly turned off by the breathless predictions flooding social media. They dismiss AI as an overhyped parlor trick that produces mostly unusable, and certainly unmaintainable, code.
The truth, as usual, sits somewhere in the middle—and it's more interesting than either extreme suggests.
After working with more than a few very large organizations navigating this transition, I've seen firsthand how AI fundamentally changes what's possible in software development. Not by replacing developers (not even close!), but by amplifying what the good developers can accomplish.
The result? A complete rethink of how we structure development teams and deliver business value.
Death of the Code Scribe
So what's actually changing? In many large organizations today, software development operates like an assembly line. You might have hundreds of developers whose primary job is essentially translating requirements from tickets into code. These developers don't think deeply about the domain or business context. They take assignments from product managers or engineering leads and focus on implementation, not innovation. It’s similar to scribes before the arrival of the printing press.
This model made sense when finding people with the ability to write code was the bottleneck. Companies scaled their output by adding more developers, often leveraging offshore labor for cost efficiency. It’s basically a quantity over quality approach, with layers of management coordinating the work.
AI significantly reduces this bottleneck. The ability to rapidly write code—the core skill of the “code scribe”—is exactly where AI excels. When you can instruct an AI agent to generate substantial amounts of working code in minutes rather than days, the economics of the prior model break down.
But here's what's not changing: the need for developers who understand your domain, your users, and your business goals. The developers who can guide AI to build secure, reliable, scalable software. The ones who know what's technically possible and can work hand-in-hand with business and design teams to create real impact.
These developers don't just write code—they architect solutions, make strategic technical decisions, and ensure the software delivers genuine business value. And with AI handling the implementation heavy lifting, you need far fewer of them.
Why Small Teams Win Big
This shift enables something new: tiny teams that can move with incredible speed and agility. Instead of managing teams of dozens or even hundreds of developers, you're looking at teams of under ten people—often just a handful of highly skilled devs who deeply understand their domain.
The transformation in operational dynamics is profound. With massive teams, just keeping everyone aligned is a full-time job. You need layers of management, coordination roles, and convoluted processes. The communication overhead is a constant drag on productivity; often taking up more time and energy than the work itself!
Even with perfect execution, reacting to new information and changing direction is cumbersome and expensive. You might be able to change priorities four times per year in this mode if you're lucky.
Tiny teams eliminate most of this overhead. Fewer brains need to stay synchronized. When new information comes in—whether from user feedback, market changes, or business metrics—these teams can react immediately. They can behave like the growth hacking squads that made Silicon Valley famous, continually iterating and optimizing based on real-world feedback.
This speed and agility is a big advantage. While your competitors are locked into quarterly roadmaps, unable to respond to market signals, your tiny teams are adapting weekly or even daily. They're not just building faster—they're building the right things.
The ROI of Right-Sizing Your Teams
The business and financial implications are staggering. Organizations spending tens of millions on staff augmentation and low-cost implementers can redirect those resources almost immediately. But the real value isn't just cost savings—it's the acceleration of business impact.
When you can experiment and build rapidly, you discover what works much faster. When you can respond to user needs in real-time, you capture opportunities that slower competitors miss. When your dev teams understand both the technology and the business deeply, they build solutions that actually move the needle.
I've seen companies shift from delivering major updates quarterly to shipping meaningful improvements weekly. The difference in business velocity is transformational.
The New Development Stack
Enabling this transformation requires embracing a new class of tools that didn't exist even a year ago in ‘24. The current state of the art centers around command-line AI agents—tools like Claude Code and OpenAI’s Codex that operate in full computing environments with access to all the tools a developer would use.
These agents can explore codebases, research API’s, install dependencies, and write software. The developer's role is then to provide explicit guidance about what to build and how to build it in a secure and scalable fashion. Managing the AI's work like they would an extremely fast junior developer.
This represents a fundamental shift from earlier AI coding tools. The first generation offered autocomplete functionality—helpful, but not transformational. The second generation provided chat-based programming, where you'd describe what you wanted and copy-paste the results. Useful, but limited by the AI's inability to run the code in its natural environment.
Today's command-line agents can actually execute code, get feedback, and iterate until they solve the problem correctly. They can run tests, debug issues, and work within the same feedback loops that make human developers effective.
Amplifying AI Coding with Traditional Best Practices
Interestingly, many traditional expert-level development practices become more important, not less, in this new environment.
Test-driven development, for instance, provides the perfect framework for AI agents. You can specify exactly what you want by writing tests that currently fail, then let the AI work until those tests pass.
This gives the AI clear exit conditions and prevents it from doing too much or going down rabbit holes of incorrectness. It also builds a comprehensive safety net that immediately alerts you if the AI breaks something else while working.
Pair programming remains highly relevant too. As these AI agents work at superhuman speed, having two human brains overseeing the work provides crucial oversight. It's also the fastest way to upskill your team on these new practices. People learn these techniques much better in the trenches than in theoretical training sessions.
Refactoring early and often plays a key role in maintaining a healthy codebase and preventing the big ball of spaghetti that pure “vibe coding” tends to produce. It also gives developers a method to understand implementation details as written by the AI since refactoring is one of the best ways to understand code written by someone (or something) else.
Summing It Up
AI Enhanced Development is here, it’s real and it’s already making a big impact in the industry. It’s enabling tiny teams of highly skilled developers to build more software, more quickly, which ultimately results in reduced total cost, improved ability to respond to change, and increased business outcomes.
There are, however, two things the hype might lead you to believe that are not happening yet:
Software engineers are not being replaced by everyday average joes wielding AI Assistants (or by AI on its own)
Junior developers are not being elevated to senior through their use of AI.
So far, this is a technology that takes your current skill and capability in software engineering and amplifies it exponentially.
Which means in the current state of play, Tiny Teams of high skill, high efficacy devs are the winning strategy.
AI powered software development is a polarizing topic these days.
On the one hand, you have the true believers. Hyping it to the max: “coding is now a useless skill and all developers will soon be wholly replaced with AI!” and “If you’re a computer science major, drop out now!” (This is bad advice).
On the other hand are the skeptics. Cynics and seasoned developers who’ve seen quite a few hype cycles and are mostly turned off by the breathless predictions flooding social media. They dismiss AI as an overhyped parlor trick that produces mostly unusable, and certainly unmaintainable, code.
The truth, as usual, sits somewhere in the middle—and it's more interesting than either extreme suggests.
After working with more than a few very large organizations navigating this transition, I've seen firsthand how AI fundamentally changes what's possible in software development. Not by replacing developers (not even close!), but by amplifying what the good developers can accomplish.
The result? A complete rethink of how we structure development teams and deliver business value.
Death of the Code Scribe
So what's actually changing? In many large organizations today, software development operates like an assembly line. You might have hundreds of developers whose primary job is essentially translating requirements from tickets into code. These developers don't think deeply about the domain or business context. They take assignments from product managers or engineering leads and focus on implementation, not innovation. It’s similar to scribes before the arrival of the printing press.
This model made sense when finding people with the ability to write code was the bottleneck. Companies scaled their output by adding more developers, often leveraging offshore labor for cost efficiency. It’s basically a quantity over quality approach, with layers of management coordinating the work.
AI significantly reduces this bottleneck. The ability to rapidly write code—the core skill of the “code scribe”—is exactly where AI excels. When you can instruct an AI agent to generate substantial amounts of working code in minutes rather than days, the economics of the prior model break down.
But here's what's not changing: the need for developers who understand your domain, your users, and your business goals. The developers who can guide AI to build secure, reliable, scalable software. The ones who know what's technically possible and can work hand-in-hand with business and design teams to create real impact.
These developers don't just write code—they architect solutions, make strategic technical decisions, and ensure the software delivers genuine business value. And with AI handling the implementation heavy lifting, you need far fewer of them.
Why Small Teams Win Big
This shift enables something new: tiny teams that can move with incredible speed and agility. Instead of managing teams of dozens or even hundreds of developers, you're looking at teams of under ten people—often just a handful of highly skilled devs who deeply understand their domain.
The transformation in operational dynamics is profound. With massive teams, just keeping everyone aligned is a full-time job. You need layers of management, coordination roles, and convoluted processes. The communication overhead is a constant drag on productivity; often taking up more time and energy than the work itself!
Even with perfect execution, reacting to new information and changing direction is cumbersome and expensive. You might be able to change priorities four times per year in this mode if you're lucky.
Tiny teams eliminate most of this overhead. Fewer brains need to stay synchronized. When new information comes in—whether from user feedback, market changes, or business metrics—these teams can react immediately. They can behave like the growth hacking squads that made Silicon Valley famous, continually iterating and optimizing based on real-world feedback.
This speed and agility is a big advantage. While your competitors are locked into quarterly roadmaps, unable to respond to market signals, your tiny teams are adapting weekly or even daily. They're not just building faster—they're building the right things.
The ROI of Right-Sizing Your Teams
The business and financial implications are staggering. Organizations spending tens of millions on staff augmentation and low-cost implementers can redirect those resources almost immediately. But the real value isn't just cost savings—it's the acceleration of business impact.
When you can experiment and build rapidly, you discover what works much faster. When you can respond to user needs in real-time, you capture opportunities that slower competitors miss. When your dev teams understand both the technology and the business deeply, they build solutions that actually move the needle.
I've seen companies shift from delivering major updates quarterly to shipping meaningful improvements weekly. The difference in business velocity is transformational.
The New Development Stack
Enabling this transformation requires embracing a new class of tools that didn't exist even a year ago in ‘24. The current state of the art centers around command-line AI agents—tools like Claude Code and OpenAI’s Codex that operate in full computing environments with access to all the tools a developer would use.
These agents can explore codebases, research API’s, install dependencies, and write software. The developer's role is then to provide explicit guidance about what to build and how to build it in a secure and scalable fashion. Managing the AI's work like they would an extremely fast junior developer.
This represents a fundamental shift from earlier AI coding tools. The first generation offered autocomplete functionality—helpful, but not transformational. The second generation provided chat-based programming, where you'd describe what you wanted and copy-paste the results. Useful, but limited by the AI's inability to run the code in its natural environment.
Today's command-line agents can actually execute code, get feedback, and iterate until they solve the problem correctly. They can run tests, debug issues, and work within the same feedback loops that make human developers effective.
Amplifying AI Coding with Traditional Best Practices
Interestingly, many traditional expert-level development practices become more important, not less, in this new environment.
Test-driven development, for instance, provides the perfect framework for AI agents. You can specify exactly what you want by writing tests that currently fail, then let the AI work until those tests pass.
This gives the AI clear exit conditions and prevents it from doing too much or going down rabbit holes of incorrectness. It also builds a comprehensive safety net that immediately alerts you if the AI breaks something else while working.
Pair programming remains highly relevant too. As these AI agents work at superhuman speed, having two human brains overseeing the work provides crucial oversight. It's also the fastest way to upskill your team on these new practices. People learn these techniques much better in the trenches than in theoretical training sessions.
Refactoring early and often plays a key role in maintaining a healthy codebase and preventing the big ball of spaghetti that pure “vibe coding” tends to produce. It also gives developers a method to understand implementation details as written by the AI since refactoring is one of the best ways to understand code written by someone (or something) else.
Summing It Up
AI Enhanced Development is here, it’s real and it’s already making a big impact in the industry. It’s enabling tiny teams of highly skilled developers to build more software, more quickly, which ultimately results in reduced total cost, improved ability to respond to change, and increased business outcomes.
There are, however, two things the hype might lead you to believe that are not happening yet:
Software engineers are not being replaced by everyday average joes wielding AI Assistants (or by AI on its own)
Junior developers are not being elevated to senior through their use of AI.
So far, this is a technology that takes your current skill and capability in software engineering and amplifies it exponentially.
Which means in the current state of play, Tiny Teams of high skill, high efficacy devs are the winning strategy.
AI powered software development is a polarizing topic these days.
On the one hand, you have the true believers. Hyping it to the max: “coding is now a useless skill and all developers will soon be wholly replaced with AI!” and “If you’re a computer science major, drop out now!” (This is bad advice).
On the other hand are the skeptics. Cynics and seasoned developers who’ve seen quite a few hype cycles and are mostly turned off by the breathless predictions flooding social media. They dismiss AI as an overhyped parlor trick that produces mostly unusable, and certainly unmaintainable, code.
The truth, as usual, sits somewhere in the middle—and it's more interesting than either extreme suggests.
After working with more than a few very large organizations navigating this transition, I've seen firsthand how AI fundamentally changes what's possible in software development. Not by replacing developers (not even close!), but by amplifying what the good developers can accomplish.
The result? A complete rethink of how we structure development teams and deliver business value.
Death of the Code Scribe
So what's actually changing? In many large organizations today, software development operates like an assembly line. You might have hundreds of developers whose primary job is essentially translating requirements from tickets into code. These developers don't think deeply about the domain or business context. They take assignments from product managers or engineering leads and focus on implementation, not innovation. It’s similar to scribes before the arrival of the printing press.
This model made sense when finding people with the ability to write code was the bottleneck. Companies scaled their output by adding more developers, often leveraging offshore labor for cost efficiency. It’s basically a quantity over quality approach, with layers of management coordinating the work.
AI significantly reduces this bottleneck. The ability to rapidly write code—the core skill of the “code scribe”—is exactly where AI excels. When you can instruct an AI agent to generate substantial amounts of working code in minutes rather than days, the economics of the prior model break down.
But here's what's not changing: the need for developers who understand your domain, your users, and your business goals. The developers who can guide AI to build secure, reliable, scalable software. The ones who know what's technically possible and can work hand-in-hand with business and design teams to create real impact.
These developers don't just write code—they architect solutions, make strategic technical decisions, and ensure the software delivers genuine business value. And with AI handling the implementation heavy lifting, you need far fewer of them.
Why Small Teams Win Big
This shift enables something new: tiny teams that can move with incredible speed and agility. Instead of managing teams of dozens or even hundreds of developers, you're looking at teams of under ten people—often just a handful of highly skilled devs who deeply understand their domain.
The transformation in operational dynamics is profound. With massive teams, just keeping everyone aligned is a full-time job. You need layers of management, coordination roles, and convoluted processes. The communication overhead is a constant drag on productivity; often taking up more time and energy than the work itself!
Even with perfect execution, reacting to new information and changing direction is cumbersome and expensive. You might be able to change priorities four times per year in this mode if you're lucky.
Tiny teams eliminate most of this overhead. Fewer brains need to stay synchronized. When new information comes in—whether from user feedback, market changes, or business metrics—these teams can react immediately. They can behave like the growth hacking squads that made Silicon Valley famous, continually iterating and optimizing based on real-world feedback.
This speed and agility is a big advantage. While your competitors are locked into quarterly roadmaps, unable to respond to market signals, your tiny teams are adapting weekly or even daily. They're not just building faster—they're building the right things.
The ROI of Right-Sizing Your Teams
The business and financial implications are staggering. Organizations spending tens of millions on staff augmentation and low-cost implementers can redirect those resources almost immediately. But the real value isn't just cost savings—it's the acceleration of business impact.
When you can experiment and build rapidly, you discover what works much faster. When you can respond to user needs in real-time, you capture opportunities that slower competitors miss. When your dev teams understand both the technology and the business deeply, they build solutions that actually move the needle.
I've seen companies shift from delivering major updates quarterly to shipping meaningful improvements weekly. The difference in business velocity is transformational.
The New Development Stack
Enabling this transformation requires embracing a new class of tools that didn't exist even a year ago in ‘24. The current state of the art centers around command-line AI agents—tools like Claude Code and OpenAI’s Codex that operate in full computing environments with access to all the tools a developer would use.
These agents can explore codebases, research API’s, install dependencies, and write software. The developer's role is then to provide explicit guidance about what to build and how to build it in a secure and scalable fashion. Managing the AI's work like they would an extremely fast junior developer.
This represents a fundamental shift from earlier AI coding tools. The first generation offered autocomplete functionality—helpful, but not transformational. The second generation provided chat-based programming, where you'd describe what you wanted and copy-paste the results. Useful, but limited by the AI's inability to run the code in its natural environment.
Today's command-line agents can actually execute code, get feedback, and iterate until they solve the problem correctly. They can run tests, debug issues, and work within the same feedback loops that make human developers effective.
Amplifying AI Coding with Traditional Best Practices
Interestingly, many traditional expert-level development practices become more important, not less, in this new environment.
Test-driven development, for instance, provides the perfect framework for AI agents. You can specify exactly what you want by writing tests that currently fail, then let the AI work until those tests pass.
This gives the AI clear exit conditions and prevents it from doing too much or going down rabbit holes of incorrectness. It also builds a comprehensive safety net that immediately alerts you if the AI breaks something else while working.
Pair programming remains highly relevant too. As these AI agents work at superhuman speed, having two human brains overseeing the work provides crucial oversight. It's also the fastest way to upskill your team on these new practices. People learn these techniques much better in the trenches than in theoretical training sessions.
Refactoring early and often plays a key role in maintaining a healthy codebase and preventing the big ball of spaghetti that pure “vibe coding” tends to produce. It also gives developers a method to understand implementation details as written by the AI since refactoring is one of the best ways to understand code written by someone (or something) else.
Summing It Up
AI Enhanced Development is here, it’s real and it’s already making a big impact in the industry. It’s enabling tiny teams of highly skilled developers to build more software, more quickly, which ultimately results in reduced total cost, improved ability to respond to change, and increased business outcomes.
There are, however, two things the hype might lead you to believe that are not happening yet:
Software engineers are not being replaced by everyday average joes wielding AI Assistants (or by AI on its own)
Junior developers are not being elevated to senior through their use of AI.
So far, this is a technology that takes your current skill and capability in software engineering and amplifies it exponentially.
Which means in the current state of play, Tiny Teams of high skill, high efficacy devs are the winning strategy.