A major cost in card grading is two-way shipping and manual inspection. A disruptive model would allow users to submit high-resolution scans via a proprietary app. AI could perform the initial grade, and the company would only ship the final, high-quality display slab, cutting costs and turnaround time.
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AI enables a fundamental shift in business models away from selling access (per seat) or usage (per token) towards selling results. For example, customer support AI will be priced per resolved ticket. This outcome-based model will become the standard as AI's capabilities for completing specific, measurable tasks improve.
The forward-deployed engineer (FDE) model, using engineers in a sales role, is now a standard enterprise playbook. Its prevalence creates a contrarian opportunity: build AI that automates the FDE's integration work, cutting a weeks-long process to minutes and creating a massive sales advantage.
In the current market, AI companies see explosive growth through two primary vectors: attaching to the massive AI compute spend or directly replacing human labor. Companies merely using AI to improve an existing product without hitting one of these drivers risk being discounted as they lack a clear, exponential growth narrative.
There is a repeatable business model in the success of vinyl record valuation apps. Target a niche collectible market (e.g., comic books, vintage toys), and build a simple app that lets users scan an item to learn its identity, condition, and market value.
VCs have traditionally ignored the massive $16T services sector due to its low margins. AI automation can fundamentally change this by eliminating repetitive tasks, allowing these companies to achieve margin profiles similar to software businesses, thus making the sector newly viable for venture investment.
The dominant card grader, PSA, is slow, expensive, and opaque. A competitor could win by building a brand around transparency and entertainment. A live-streamed grading process, like an "Antiques Roadshow for cards," creates engaging content, builds trust, and establishes a David-vs-Goliath narrative.
Professional photographers are finding that AI’s most significant benefit isn't image generation, which threatens their craft. Instead, it’s automating mundane business tasks like culling thousands of photos, qualifying clients, and managing customer relationships, freeing them up to focus on artistry.
YipitData had data on millions of companies but could only afford to process it for a few hundred public tickers due to high manual cleaning costs. AI and LLMs have now made it economically viable to tag and structure this messy, long-tail data at scale, creating massive new product opportunities.
In businesses with tight 5-8% margins, like retail, AI-driven efficiencies in areas like customer support aren't just incremental. They become extraordinarily powerful levers for profitability and scaling, fundamentally altering the cost structure of the business.
Traditionally, service businesses lack scalability for VC. But AI startups are adopting a 'manual first, automate later' approach. They deliver high-touch services to gain traction, while simultaneously building AI to automate 90%+ of the work, eventually achieving software-like margins and growth.
