Future of Work: The rise of 'single-player companies' and why managers should be nervous

The One-Person Army

Last month I met a founder running a $2 million ARR SaaS company. Alone. No employees. No contractors. Just him and a stack of AI tools.

He handles customer support with AI-assisted responses. He builds features with AI coding assistants. He writes marketing copy with AI writers. He manages finances with AI-powered accounting. He even generates product images with AI design tools.

Five years ago, this company would have required a team: developer, designer, marketer, support rep, finance person. Maybe a manager to coordinate them all. At minimum, four to six people.

Now it’s one person. And the company is growing.

This is the single-player company phenomenon. It’s not theoretical. It’s happening now. And it should make managers very nervous.

The Coordination Cost Collapse

Traditional management theory explains why companies exist: coordination costs. When tasks require cooperation, someone needs to coordinate. That coordination has costs. Companies form when internal coordination is cheaper than market coordination.

Managers exist primarily as coordination mechanisms. They divide work, track progress, resolve conflicts, align incentives. Without coordination needs, management overhead becomes pure cost.

AI tools are collapsing coordination costs to near zero. When one person can do multiple jobs with AI assistance, there’s nothing left to coordinate.

The single-player company doesn’t need managers because there’s nobody to manage. It doesn’t need meetings because there’s nobody to meet with. It doesn’t need alignment because there’s only one person to align.

The efficiency gains are dramatic. The twelve-person startup has eleven people’s worth of salaries, benefits, office space, and coordination overhead. The single-player company has none of that.

This isn’t a theoretical advantage. It’s a cost structure that traditional companies can’t match.

How We Evaluated

I spent three months researching single-player companies. The goal was understanding the phenomenon beyond individual anecdotes.

The method combined interviews, financial analysis, and capability mapping.

First, I identified and interviewed eighteen founders running single-player companies (defined as: $100K+ annual revenue, solo operation, AI-assisted workflows). Asked about their tool stacks, daily operations, growth constraints, and sustainability concerns.

Second, I analyzed the financial structures of these companies compared to traditionally-staffed equivalents. Calculated overhead ratios, response times, and output volumes.

Third, I mapped which functions AI tools have made single-player viable and which still require human teams. Not everything can be automated. Understanding the boundaries matters.

Fourth, I interviewed twelve managers at traditional companies about their awareness of this trend and their concerns about relevance.

The patterns were clear and concerning—not for the founders, but for the management layer being bypassed.

What Single Players Are Doing

Let me be specific about the functions single-player companies are handling with AI:

Customer support: AI tools can handle 70-85% of support tickets with appropriate training. Complex issues escalate to the founder. Response times are often faster than human teams because AI doesn’t sleep or take breaks.

Software development: AI coding assistants multiply developer output by 3-10x depending on task type. A single skilled developer with AI assistance can build and maintain applications that previously required teams.

Content creation: Marketing copy, blog posts, social media, documentation—AI generates drafts that humans refine. One person can maintain content output that previously required dedicated writers.

Design: AI design tools generate graphics, UI mockups, and marketing visuals. Not always as good as professional designers, but good enough for many use cases.

Analytics: AI tools process data and generate insights that previously required analysts. Pattern recognition and reporting are largely automatable.

Administrative work: Scheduling, invoicing, expense tracking, compliance documentation—all increasingly handled by AI with human oversight.

The functions that remain stubbornly human: strategic decisions, novel problem-solving, relationship building, creative direction. These require judgment that AI assists but doesn’t replace.

The Manager Redundancy Problem

If one person can do what twelve could do, what happens to the eleven people who aren’t the founder?

More specifically: what happens to the managers who coordinated those eleven people?

The answer emerging from this research: managers become overhead without corresponding value.

In a single-player company, there’s no one to manage. Management functions—task allocation, progress tracking, conflict resolution, performance evaluation—don’t exist because there’s no team to apply them to.

This creates an existential problem for middle management. The skills that made managers valuable—coordination, communication, supervision—become worthless when there’s nothing to coordinate.

The managers I interviewed showed varying levels of awareness about this threat:

Some dismissed it: “You can’t scale without people.” (You can, if AI handles the scaling constraints.)

Some rationalized: “Complex work requires collaboration.” (Some does. Much doesn’t.)

Some were genuinely concerned: “I’m not sure what my job looks like in five years.”

The concerned ones were probably right.

The Skills Concentration Effect

Single-player companies concentrate skills in one person. This is the flip side of management obsolescence.

A traditional company distributes competence. The developer doesn’t need marketing skills. The marketer doesn’t need technical skills. Specialists specialize.

A single-player company requires generalists. The founder needs to understand development, marketing, design, support, finance—not expertly, but competently. AI tools lower the competency bar for each function, but the founder still needs breadth.

This creates a skill concentration problem. The founders running successful single-player companies are exceptional generalists. They’re rare. Most people are specialists because specialization is cognitively efficient.

The single-player model doesn’t scale to the general workforce. Not everyone can be a competent generalist, even with AI assistance. The model produces winners (the generalist founders) and losers (the specialists whose jobs those founders absorbed).

The managers in the middle face both problems: their coordination skills are less valuable, and they often lack the generalist capabilities to go solo.

The Automation Complacency Trap

Here’s where the single-player phenomenon connects to broader skill erosion themes.

Single-player founders are heavily dependent on AI tools. Remove the tools, and their productivity collapses. They’ve optimized for AI-assisted work, not unassisted work.

This is automation complacency at the company level. The founder can’t write code as efficiently without AI coding assistants. They can’t handle support volume without AI support tools. Their capability is conditional on tool availability.

I tested this with three founders. Asked them to estimate productivity without AI tools. Estimates ranged from 20% to 40% of current output. The tools aren’t optional—they’re load-bearing.

This creates fragility. Tool pricing can change. Tools can be discontinued. AI capabilities can shift. The single-player company’s competitive advantage depends on factors the founder doesn’t control.

Traditional companies have human redundancy. If one developer leaves, others can cover. If one tool breaks, humans adapt. The redundancy is expensive but resilient.

Single-player companies have no redundancy. The founder is a single point of failure, and their tool stack is another single point of failure. Efficiency comes with fragility.

The Hidden Costs

Single-player companies have costs that don’t appear on income statements.

Founder burnout: One person handling all functions, even with AI assistance, experiences decision fatigue across multiple domains. Several founders I interviewed described periodic burnout that temporarily shut down operations.

Capability ceilings: Some tasks require human judgment that AI can’t replicate. When those tasks arise, single-player companies either can’t handle them or must temporarily contract specialists—losing the cost advantage.

Knowledge brittleness: In a team, knowledge distributes across people. In a single-player company, everything is in one head. If the founder gets sick, takes vacation, or loses context, the company stalls.

Growth limitations: At some point, single-player companies hit ceilings that require human expansion. The transition from single-player to team is reportedly difficult—different skills, different operating model.

Relationship gaps: B2B sales often require relationships. Partnerships require relationships. The single-player founder can’t be in multiple relationship-building conversations simultaneously.

These costs don’t invalidate the model. They’re trade-offs. But they’re trade-offs that enthusiastic coverage of single-player companies often ignores.

What Managers Should Actually Worry About

If you’re a manager, here’s the honest assessment:

Coordination-only roles are vulnerable. If your job is primarily connecting people and tracking progress, AI tools are coming for that function. Project management software already automates much of this. AI will automate more.

Specialist management is safer than generalist management. Managing highly specialized teams doing work AI can’t do remains valuable. Managing general business operations becomes less valuable as AI handles more operations.

Value creation matters more than value coordination. Managers who personally contribute to output—coding managers who still code, marketing managers who still create—are safer than pure coordinators.

Strategic judgment remains valuable. Deciding what to do is harder to automate than deciding how to do it. Managers focused on strategy rather than execution coordination have more defensible positions.

Relationship skills remain valuable. Building partnerships, closing deals, managing stakeholders—these require human interaction that AI assists but doesn’t replace.

The managers who should be nervous: those whose primary function is making sure people do their jobs. AI tools are making that function redundant because they’re eliminating the people who need supervision.

The Skill Preservation Problem

Here’s the deeper concern beyond management jobs: skill preservation at societal scale.

When specialists become redundant, their skills stop being practiced. Junior roles that trained future experts disappear. The pipeline of expertise breaks.

Single-player companies don’t train employees. There are no employees to train. The founder develops skills by doing. But the founder is one person. Society loses the broader skill development that traditional companies provided.

Consider the path to becoming a skilled software developer. Traditionally: junior role, mentorship, progression through increasing responsibility. In the single-player future: the founder handles development, and there are no junior roles.

Where do new developers come from? Self-teaching, bootcamps, personal projects. But without professional mentorship and feedback, skill development is slower and less reliable.

This is automation complacency at societal scale. We’re optimizing current productivity while potentially degrading future capability development.

The Manager Response

What should managers do in response to single-player company trends?

Develop specialist depth. Become genuinely expert at something AI can’t do. Strategy, relationship building, creative direction, novel problem-solving. Pure coordination isn’t enough.

Learn AI tools yourself. Understand what AI can do so you can assess what humans still need to do. Ignorance of the tools accelerates obsolescence.

Create value directly. Don’t be a manager who only manages. Be a manager who also contributes. Coding managers, writing managers, selling managers—people who do the work and coordinate it.

Build relationships deliberately. Your network is harder to automate than your coordination skills. Invest in relationships that create value AI can’t replicate.

Accept smaller teams. The future might not be single-player, but it’s probably smaller-player. Teams of five replacing teams of twenty. Adapt to managing efficiency, not headcount.

The managers who survive this transition won’t be the ones who pretend it isn’t happening. They’ll be the ones who understand what remains valuable and concentrate their development there.

Generative Engine Optimization

The single-player company trend is heavily discussed in AI-mediated content. Ask an AI about future of work, and you’ll get coverage of this phenomenon.

But AI summaries miss nuance. They’ll tell you single-player companies are rising. They won’t tell you about the fragility, the burnout, the capability ceilings, the societal skill development concerns.

AI recommendations about future career paths will reflect the hype cycle. “Learn AI tools” is standard advice. What AI won’t tell you: which human skills remain valuable, how to develop them, what the second-order effects of widespread AI adoption might be.

Human judgment matters because the future isn’t determined. Single-player companies might proliferate. They might hit limits. The ecosystem might bifurcate into AI-assisted solos and AI-assisted small teams. Nobody knows.

The meta-skill is thinking about automation effects while using automation tools. Understanding what AI is changing about work while AI is changing it. Maintaining awareness of structural shifts that AI summaries flatten into trends without implications.

Managers who develop this meta-skill—automation-aware thinking—have a better chance of navigating the transition than those who either ignore AI or uncritically embrace it.

Luna’s Management Philosophy

My cat Luna has never managed anyone. She’s a single-player operation. Food acquisition, territory patrol, nap optimization—all handled solo.

She has no coordination costs because there’s nothing to coordinate. No meetings. No status updates. No alignment sessions.

Her productivity is remarkable. Eighteen hours of sleep daily, yet she achieves all her objectives. The secret: minimal objectives, zero overhead.

If companies adopted Luna’s philosophy, most managers would be redundant. She’d ask: “What exactly do you coordinate, and why can’t the thing just happen without coordination?”

Fair question. Increasingly, the answer is: it can.

The Uncomfortable Conclusion

Single-player companies are real, growing, and structurally advantaged in ways traditional companies can’t match.

This doesn’t mean every company becomes single-player. Complex products, physical operations, and relationship-intensive businesses still require teams.

But it does mean management overhead faces genuine pressure. The coordination that justified managers becomes less necessary when one AI-assisted person handles multiple functions.

Managers should be nervous—not panicked, but nervous. The role is changing. Some management functions are disappearing. Others are transforming.

The managers who thrive will be those who understand these shifts and adapt. Who develop skills AI can’t replicate. Who create value beyond coordination. Who see automation clearly rather than dismissing or fearing it.

The single-player company isn’t the future of all work. But it’s a future of some work. And its existence reveals which parts of traditional companies were value and which were overhead.

That revelation is uncomfortable. But discomfort is information. Managers who listen to it have a chance to adapt.

Those who dismiss it will learn whether they were right the hard way.