Software

Are Large Software Teams Still Relevant in the Age of AI?

Andres Max Andres Max
· 10 min read
Are Large Software Teams Still Relevant in the Age of AI?

A 5-person team in 2026 can ship what a 50-person team shipped in 2016. AI has compressed the development curve dramatically. The question every founder and engineering leader is asking: do large software teams still make sense?

After scaling teams from 3 to 80+ people over 18 years, and now watching AI transform what small teams can accomplish, I’ve developed a nuanced view. The answer isn’t “big teams are dead.” It’s “the reasons for big teams have changed.”

What AI Actually Changed

Let’s be specific about what AI tools changed in software development.

Changed: Individual Productivity

A skilled developer with AI tools is significantly more productive than the same developer without them.

Task Without AI With AI Productivity Gain
Writing boilerplate Hours Minutes 5-10x
Debugging errors 30-60 min 10-15 min 2-4x
Learning new frameworks Days Hours 3-5x
Writing documentation Hours 30 min 2-4x
Writing tests 1 hour 20 min 3x
Code review prep 30 min 10 min 3x

Overall impact: Individual developers are perhaps 40-60% more productive on pure coding tasks.

Changed: Minimum Team Viability

The minimum team required to build and ship a product has dropped.

2016 typical MVP team:

  • 2-3 backend engineers
  • 1-2 frontend engineers
  • 1 designer
  • 1 PM
  • Total: 5-7 people

2026 typical MVP team:

  • 1-2 full-stack engineers (with AI)
  • 1 designer who can code (with AI)
  • 0-1 PM (often founder)
  • Total: 2-3 people

This compression is real. I’ve seen it across dozens of startups I work with.

NOT Changed: Product Thinking

AI can write code. It cannot:

  • Decide what to build
  • Understand user needs
  • Make product trade-offs
  • Design for human behavior
  • Anticipate market changes

Product thinking remains entirely human work. If anything, it’s more important now that execution is faster.

NOT Changed: System Complexity

As systems grow, complexity grows non-linearly. AI helps write code faster, but it doesn’t help manage:

  • Distributed systems architecture
  • Data migration at scale
  • Cross-team coordination
  • Legacy system integration
  • Regulatory compliance

Large, complex systems still require large teams, just for different reasons.

NOT Changed: Organizational Needs

Big companies have big teams partly because of organizational requirements:

  • Multiple time zones
  • Support coverage
  • Knowledge redundancy
  • Career progression paths
  • Specialization requirements

AI doesn’t eliminate these needs.

When Small Teams Win

Small AI-augmented teams have decisive advantages in certain situations.

Situation 1: Early-Stage Products (Pre-PMF)

Before product-market fit, speed of iteration matters more than scale of execution. Small teams:

  • Make decisions faster
  • Pivot more easily
  • Stay closer to customers
  • Avoid premature process

My recommendation: Until you have clear product-market fit, keep your team as small as possible. 2-4 people maximum. AI makes this more viable than ever.

Situation 2: Focused Products

Products that do one thing well benefit from small, focused teams.

Examples:

  • Single-purpose SaaS tools
  • Developer utilities
  • Niche productivity apps
  • Personal finance tools

These products don’t need platform teams, infrastructure specialists, or complex coordination. A small team with AI can match the output of larger competitors.

Situation 3: Technical Products for Technical Users

When your users are developers, you can ship with less polish. Technical users:

  • Tolerate rougher UX
  • Prefer powerful features over smooth onboarding
  • Can figure things out without hand-holding

This means a small technical team can serve them without design and PM overhead.

Situation 4: Solo Founder + AI

For the first time, solo founders can build and ship real products that previously required teams.

What’s now possible solo:

  • Full-stack web apps
  • Simple mobile apps
  • SaaS products with payments
  • API-based services
  • Internal tools

What still needs help:

  • Complex distributed systems
  • Products requiring 24/7 support
  • Multi-platform native apps
  • Heavily designed consumer products

When Large Teams Still Win

Despite AI advances, large teams maintain advantages in specific domains.

Domain 1: Platform Scale

Products serving millions of users with high availability requirements need large teams.

Why AI doesn’t solve this:

  • Distributed systems are genuinely complex
  • Edge cases multiply at scale
  • Operational overhead is substantial
  • On-call rotation requires headcount

Examples: AWS, Stripe, Shopify. These need 1000+ engineers not because they’re inefficient, but because the problems are genuinely hard.

Domain 2: Multi-Product Companies

Companies offering multiple products need teams for each product area.

Why AI doesn’t solve this:

  • Different products need different expertise
  • Cross-product integration is complex
  • Context-switching costs don’t disappear with AI
  • Each product needs dedicated ownership

Domain 3: Regulated Industries

Financial services, healthcare, and government software have compliance requirements that demand dedicated resources.

Why AI doesn’t solve this:

  • Compliance is procedural, not just technical
  • Audit trails require human oversight
  • Regulatory interpretation needs specialized knowledge
  • Security reviews can’t be automated

Domain 4: Enterprise Sales Products

Products sold to large enterprises need teams to support the sales process.

Why AI doesn’t solve this:

  • Enterprise customers want customization
  • Integration work is customer-specific
  • Support expectations are high
  • Security reviews require human response

Domain 5: Research and Innovation

Cutting-edge technical work requires deep expertise that AI augments but doesn’t replace.

Why AI doesn’t solve this:

  • Novel problems have no training data
  • Research requires creative thinking
  • State-of-the-art work pushes beyond AI capabilities
  • Deep specialization takes years to develop

The Hybrid Model

The most effective teams I see combine AI leverage with appropriate headcount.

Structure: Core Team + AI Multiplication

Core team (3-5 people):

  • Technical leader / Architect
  • Product lead / Founder
  • Senior full-stack engineers

Each person has AI multiplying their output. They make architectural decisions, own major systems, and set technical direction.

Extended team (scaled as needed):

  • Additional engineers for parallel workstreams
  • Specialists for specific domains (mobile, ML, security)
  • Support and operations roles

This isn’t “big team vs. small team.” It’s right-sized for the problem.

How to Think About Team Size

Ask these questions:

1. How complex is your system?

  • Simple product, limited integrations → Small team
  • Complex product, many integrations → Larger team needed

2. How fast do you need to move?

  • Speed critical, scope flexible → Small team
  • Multiple parallel workstreams required → Larger team needed

3. What’s your support burden?

  • Self-serve, low-touch → Small team can handle
  • High-touch, enterprise support → Need dedicated people

4. What are your operational requirements?

  • Downtime acceptable, best-effort → Small team
  • High availability, 24/7 operations → Need on-call rotation

5. What expertise do you need?

  • General web/mobile development → AI-augmented generalists
  • Specialized domains (ML, security, compliance) → Need specialists

Practical Team Sizing

Here’s how I think about team sizing by stage:

Pre-Seed / Validation

Team size: 1-2 people Structure: Founder(s) doing everything AI leverage: Maximum

You’re validating the idea, not scaling a company. One technical founder with AI can build an MVP.

Seed / Early PMF

Team size: 3-5 people Structure: Technical founder + 2-3 engineers, possibly designer AI leverage: High

You’ve found something that works. Now you need to iterate faster than one person can manage. Still small, but not solo.

Series A / Growth

Team size: 8-15 people Structure: Small teams or pods, each owning product areas AI leverage: Medium-High

You’re scaling what works. Multiple workstreams need to progress in parallel. Start thinking about team structure.

Series B+ / Scale

Team size: 20-50+ people Structure: Specialized teams (platform, product, ops, security) AI leverage: Medium

Operational complexity justifies specialized roles. AI helps individuals but doesn’t eliminate need for dedicated expertise.

The Counterintuitive Insight

Here’s what surprised me: AI might actually increase optimal team size at certain stages.

Why? Because AI makes each person more productive, the leverage of adding another productive person goes up.

Old math:

  • 1 engineer = 1 unit of output
  • 5 engineers = ~4 units of output (coordination overhead)
  • Adding engineer 6 = diminishing returns

New math:

  • 1 engineer + AI = 1.5 units of output
  • 5 engineers + AI = ~7 units of output (each person more productive)
  • Adding engineer 6 + AI = still positive returns

The productivity gains offset coordination costs, making slightly larger teams more viable than before.

But this has limits. Beyond a certain size, coordination costs still dominate, and AI doesn’t help with coordination.

What This Means for Founders

If you’re starting a company:

1. Start smaller than you think

AI means you can do more with less. Don’t hire too early. Get to product-market fit with the minimum viable team.

2. Hire for judgment, not just execution

AI handles execution increasingly well. What you need are people with excellent judgment about what to build and how to build it well.

3. Invest in AI fluency

Your team’s ability to leverage AI is a multiplier on their output. Hire people who use AI tools effectively. Train those who don’t.

4. Resize dynamically

Team size should match current needs, not anticipated future needs. It’s easier to grow than to shrink.

5. Watch the complexity threshold

There’s a point where your product complexity exceeds what a small team can manage. Know the signs:

  • Bugs increasing faster than fixes
  • Knowledge concentrated in one person
  • Deployment fear
  • Customer requests backlogging
  • Team burning out

When you hit this threshold, it’s time to grow, even with AI.

What This Means for Existing Teams

If you’re leading a larger team:

1. Audit for AI leverage

Are your team members using AI tools effectively? The productivity gap between AI-fluent and AI-avoidant developers is widening.

2. Reconsider specialist roles

Some specialized roles exist because generalists couldn’t do the work. AI is changing this. A full-stack developer with AI can handle tasks that used to require specialists.

3. Focus on what AI can’t do

The highest-value activities are increasingly:

  • Product thinking and prioritization
  • Architecture and system design
  • Customer understanding
  • Cross-team coordination
  • Organizational design

These remain human work. Make sure your team is doing them.

4. Experiment with smaller teams

Try giving a 3-person team what used to require 10. See what happens. You might be surprised.

FAQ: Team Size in the AI Age

Will AI replace software engineers?

Not wholesale. AI will replace some coding tasks. Engineers who only wrote boilerplate are at risk. Engineers who make good decisions, architect systems, and understand users are more valuable than ever.

How do I know if my team is too big?

Signs of an overstaffed team: lots of meetings, work falling through cracks despite headcount, people unsure what to work on, coordination overhead consuming significant time. If AI tools could consolidate roles, you might have room to shrink.

Should I wait to hire until AI improves more?

For early-stage companies, yes, staying lean is wise. For growing companies, don’t delay critical hires waiting for AI. The people who know how to leverage AI are worth hiring now.

How do I balance AI productivity with knowledge building?

This is a real tension. If AI solves problems for your team, they may not learn as deeply. Balance by: having team members explain AI-generated solutions, reviewing AI code thoroughly, and occasionally working without AI to build foundational understanding.

Key Takeaways

  • AI compresses the minimum viable team. What took 5-7 people can now be done by 2-3.
  • Small teams win at early stages and focused products. AI amplifies the small team advantage for startups.
  • Large teams still win at platform scale, multi-product, regulated, and research contexts. Some complexity can’t be compressed.
  • The best model is hybrid: Core team with AI multiplication, extended as needed for specific requirements.
  • AI might actually increase optimal size at growth stages. Higher individual productivity increases returns on adding people, up to a point.
  • Hire for judgment, not just execution. AI handles execution. What you need are people who know what to build.

What’s Next

If you’re building a team right now, start by asking: what’s the smallest team that could validate this idea? Build that team. Use AI aggressively. Only add people when you hit real constraints.

The companies that win won’t necessarily have the biggest teams or the most sophisticated AI. They’ll have the right-sized teams using AI effectively to build things people want.

Team size is a tool, not a goal. Match it to your problem.

If you’re wrestling with team sizing decisions or want a thinking partner on your product strategy, book a call.

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