Charles Swann, a 44-year-old founder of Forage, an AI startup in Boulder, CO, has pioneered an innovative approach to workforce management by using AI tools as a middle management layer to amplify the capabilities of junior employees. Founded approximately 18 months ago, Forage operates in the marketing technology space, helping brands understand social media trends and influencer relationships.
Swann’s lean team consists of himself and one full-time employee—a 24-year-old growth and brand specialist with less than two years of experience. Rather than hiring expensive senior talent, Swann has leveraged Google’s Gemini 3 AI model to bridge the experience gap, transforming how his junior employee develops product strategy and executes complex tasks.
Initially, the young employee used ChatGPT to refine rough ideas and polish concepts. However, the introduction of Gemini 3 marked a significant shift from AI as a refinement tool to AI as a strategic co-creator. The most dramatic impact has been in creating product requirements documents (PRDs)—complex technical blueprints that typically require 8-10 hours from experienced product managers. With Gemini’s assistance, Swann’s junior employee now completes these documents in just 4-5 hours.
The productivity gains have been substantial. Swann estimates the work distribution has shifted from 70% human/30% AI to 40% human/60% AI, with Gemini excelling at expanding ideas and expediting execution. This AI-powered approach has also reduced Swann’s supervisory burden, allowing him to focus on big-picture strategy rather than detailed review of outputs.
To mitigate risks like AI hallucinations and feedback loops, Swann has created standardized prompt starters containing detailed background on the platform, features, and definitions. This ensures consistency and relevance in AI-generated outputs while maintaining quality control.
This experience has fundamentally changed Swann’s hiring philosophy. He now prioritizes raw intelligence, ambition, and cultural intuition over traditional credentials and years of experience. For a marketing technology company focused on authentic cultural understanding, hiring younger talent with deep social media fluency—augmented by AI capabilities—provides competitive advantages that experienced professionals in their 40s might not deliver.
Swann believes this AI-augmented hiring model represents the future of workforce development, particularly for startups operating with limited resources but requiring sophisticated output.
Key Quotes
Before using it, I had the idea that we should create 70% of the final product, and AI should generate 30%. Now, it’s probably 40% us and 60% Gemini, simply because it’s so good at expanding and expediting.
Charles Swann describes the dramatic shift in workflow distribution after adopting Gemini 3, illustrating how AI has evolved from a supporting tool to the primary producer in their product development process.
AI now serves as that middle layer, helping her level up the work she produces. I hesitate to say that AI alone has allowed me to have less supervision, but as she started getting more sophisticated in her use of the AI tool, I’ve spent less time reviewing in detail what she’s generating.
Swann explains how AI functions as an intermediary management layer, reducing his supervisory burden while acknowledging that the employee’s growing AI proficiency is equally important to the productivity gains.
I would rather have those mistakes come up, and we have to course correct, than not be able to move at the pace we are.
Addressing the risks of AI hallucinations and errors, Swann articulates a velocity-first philosophy that accepts occasional mistakes as preferable to slower, more cautious development—a mindset common in startup culture.
AI removes some of the traditional requirements around skills and expertise I need to see in candidates. It allows me to focus more on the raw intelligence, ambition, and drive.
Swann describes how AI has fundamentally changed his hiring criteria, shifting emphasis from experience and credentials to innate qualities and cultural understanding—a transformation that could reshape recruitment practices across industries.
Our Take
Swann’s experience provides compelling evidence that AI is already reshaping organizational structures in ways that extend beyond automation. The concept of AI as a “middle manager” is particularly intriguing—it suggests these tools can provide not just task execution but the strategic scaffolding that traditionally required senior expertise.
What’s notable is the specificity of the productivity gains: reducing PRD creation time from 8-10 hours to 4-5 hours represents tangible ROI that CFOs can understand. However, the real innovation isn’t just efficiency—it’s the talent arbitrage opportunity. By hiring for cultural intuition and intelligence rather than experience, Swann accesses insights that seasoned professionals might miss while maintaining output quality through AI augmentation.
The risk mitigation strategy using standardized prompt starters is smart and replicable, suggesting best practices are emerging for AI-augmented workflows. As Gemini, ChatGPT, and other models continue improving, this “experience compression” effect will likely accelerate, potentially disrupting traditional career ladders and credentialism across knowledge work sectors.
Why This Matters
This case study represents a paradigm shift in how startups and small businesses can compete with larger organizations despite resource constraints. By using AI as an experience multiplier, companies can unlock senior-level productivity from junior talent at a fraction of the cost.
The implications extend beyond cost savings. This approach democratizes access to expertise, allowing ambitious young professionals to contribute at levels previously requiring years of experience. It challenges traditional hiring practices that prioritize credentials over potential, potentially reshaping career trajectories and making the workforce more meritocratic.
For the broader AI industry, this demonstrates practical, high-ROI applications of generative AI beyond content creation. The shift from 30% to 60% AI contribution in workflows shows how rapidly these tools are evolving from assistants to collaborators. However, Swann’s emphasis on guardrails and human oversight highlights the continued need for experienced judgment to prevent AI errors.
As AI capabilities continue advancing, this model could accelerate startup velocity, reduce barriers to entrepreneurship, and fundamentally alter the economics of knowledge work, making small teams exponentially more productive.
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Source: https://www.businessinsider.com/founder-hired-one-employee-uses-ai-as-middle-manager-2026-01