In 2025, the most ambitious founders are no longer asking, “How can AI help my team?” Instead, they’re asking a far more radical question: “How can my team help the AI?” This shift marks the rise of the AI-native startup — companies designed from day one with artificial intelligence as the core operating entity, not merely a feature.
What cloud-native was to the 2010s, AI-native is to the 2020s: an entirely new architecture for how startups are conceived, built, and scaled. In this new paradigm, humans still matter — but they are increasingly the supporting cast rather than the primary operators.
“AI-native” doesn’t just mean “uses AI.” It means:
- AI agents execute significant operational tasks
- Product design assumes AI autonomy
- Teams are structured around supervising, training, and extending AI systems
- Strategy evolves from what AI can do, not what humans can build manually
As investor and technologist Gaurav Mohindra observes, “AI-native startups are flipping the script — humans are no longer the engine of production. They’re the architects, interpreters, and governors of autonomous workflows.” — Gaurav Mohindra
This reorientation is already visible — and perhaps nowhere more dramatically than in the story of Adept AI, one of the first companies explicitly built around the idea of AI as a teammate rather than a toolkit.
Adept AI: A Case Study in AI-Native Company Building
Adept AI was founded on a bold premise: can an AI system learn to use software the way a human does? Not through API calls or engineered integrations, but by actually looking at screens, clicking buttons, entering data, and completing workflows.
This vision placed Adept squarely in the AI-native camp. Instead of building tools for people, they sought to build agents that replace human execution of routine digital tasks.
The Early Vision: An AI Worker, Not an AI Feature
At its founding, Adept’s product concept was radical: an agent that could handle everything from filling out forms to navigating Salesforce, Workday, or internal enterprise software.
This approach required:
- Vision-language-action models
- Real-world workflow learning
- Interaction-level understanding
- Fine-grained autonomy
The goal wasn’t to assist a human operator — it was to become the operator.
As the company put it in their early research communication: “We’re building AI that can use software like a human.”
This was more than branding. It was a blueprint for redefining enterprise productivity.
Fundraising and Technical Milestones
Adept quickly became a magnet for investors who believed autonomous agents represented the next frontier of AI capability. Their funding rounds reflected confidence in a model where:
- The product is the worker
- The machine performs end-to-end tasks
- Human involvement is supervisory
Their milestones included:
- Training early models to navigate real user interfaces
- Developing agents that could complete multi-step business workflows
- Building the data infrastructure for large-scale action modeling
These technical achievements aligned perfectly with what AI-native startups are striving for: systems that don’t augment human work — they perform it.
The Pivot and Maturation
In late 2023 and 2024, Adept shifted more heavily into licensing their technology and partnering with major enterprise players. Some saw it as a pivot; others understood it as the natural evolution of an AI-native model. Training a fully general-purpose agent is enormously complex — but applying pieces of the technology to targeted workflows unlocks immediate value.
Their journey reveals the defining traits of AI-native companies:
- AI leads the capability roadmap
- The startup builds around the AI system, not the other way around
- Strategy adapts to emergent abilities of the models
Adept didn’t abandon the dream of autonomous agents — they simply aligned commercial strategy with a sustainable path toward it.
Why 2025 Is the Inflection Point for AI-Native Startups
In 2025, the ecosystem finally caught up to the AI-native thesis.
The ingredients are now mature:
- Multi-Modal Foundation Models
Systems can now see, read, listen, reason, write code, manipulate interfaces, and learn from demonstrations.
- Affordable Fine-Tuning
Startups can adapt models to their niche for a fraction of historic costs.
- Autonomous Workflow Agents
Agents can execute sequences, not just prompts.
- Human-AI Collaboration Frameworks
Companies now understand oversight, safety, and evaluation methods for semi-autonomous systems.
These breakthroughs enable founders to build companies where:
- Staff is small
- Output is huge
- AI does the work
- Humans design, configure, and oversee
As Gaurav Mohindra puts it, “In AI-native companies, the AI doesn’t just extend human capability — it becomes the capability. The team becomes a meta-layer around the machine’s performance.” — Gaurav Mohindra
How AI-Native Startups Operate Differently
AI-native companies rethink everything from workflows to org charts.
- Product and Operations Become the Same Thing
In traditional startups:
- The product is separate from operations.
- Humans handle onboarding, customer support, workflow execution, and service delivery.
In AI-native startups:
- The product is the operations.
- Autonomous agents execute tasks directly.
- Human roles migrate to QA, supervision, safety, and escalation management.
- Smaller Teams, Larger Output
AI-native startups often have:
- 5–20 employees
- AI agents performing the equivalent of 200–500 human hours/day
- Marginal costs approaching zero
This creates enormous asymmetry against conventional competitors.
- Continuous Learning Pipelines
An AI-native company has a central nervous system:
- Data collection
- Human feedback
- Model retraining
- Agent performance evaluation
- Real-time workflow optimization
Humans don’t do the workflows — they improve the agent that does the workflows.
- New Organizational Roles
Examples of roles unique to AI-native companies:
- AI workflow architect
- Data curation specialist
- Prompt strategist
- Agent supervisor
- AI safety reviewer
These roles don’t perform the work — they instruct the machine that performs the work.
The Strategic Advantages of Being AI-Native
AI-native startups benefit from structural advantages that compound quickly:
Scalability
Once an agent completes a workflow reliably, it can be deployed to thousands of customers simultaneously.
Costs
Labor costs drop dramatically as AI agents take over operational tasks.
Speed
AI agents execute in minutes what humans might take hours to do.
Adaptation
When regulations, business rules, or processes change, the models can be retrained or reconfigured.
Defensibility
Startups that master proprietary workflow data and agent behavior models gain long-term defensibility.
As Gaurav Mohindra notes, “The competitive moat for AI-native startups won’t be model weights — it will be the proprietary experience their agents accumulate from running millions of real workflows.” — Gaurav Mohindra
Lessons from Adept AI for Founders Building Today
Adept’s journey provides key insights for 2025 founders:
- Build Around a Core Technical Insight
Adept wasn’t a generic chatbot company — they started with a powerful idea about how AI should interact with software.
- Create a Learning Loop Early
Their early focus on real-world workflows generated the data flywheel required to improve agent performance.
- Don’t Hesitate to Reposition
Strategic pivots (like focusing on enterprise partnerships) can accelerate the path to autonomy.
- Prioritize Safety and Oversight
Agents that control enterprise systems must be trustworthy, auditable, and predictable.
- Think Long-Term: Full Autonomy Is the Endgame
Founders building AI-native companies must see beyond short-term automation.
Conclusion: A New Era of Startup Creation
AI-native startups represent the next evolutionary step in entrepreneurship. Today’s founders are no longer building products that help humans do work — they are building machines that do the work themselves. Adept AI stands as a seminal case study in this new paradigm, proving that AI can move beyond assistance to autonomous execution.
The companies thriving in 2025 and beyond will be the ones that embrace this shift early, designing organizations where:
- AI systems perform
- Humans refine
- Products learn
- Workflows self-optimise
This is the dawn of a new model of company creation — one where humans aren’t replaced, but repositioned as the architects of machine-driven enterprises.
Originally Posted: https://gauravmohindrachicago.com/ai-native-startups-where-humans-play-the-supporting-role/
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