Introducing Fractional AI

I am thrilled to announce the launch of Fractional AI, led by Chris Taylor and Eddie Siegel (two superstars who I worked with at LiveRamp).

Fractional AI is an elite dev shop focused on enterprise applications of generative AI. We believe that many of the biggest benefits of generative AI will come from automating workflows at large companies; we expect trillions of dollars of value will be created in the economy over the next decade by increasing automation and giving talented employees more leverage. But the companies with the biggest automation opportunities often are not designed to hire the talent needed to accomplish this. Our goal is to bridge this gap — and ensure that elite engineering talent is deployed to the highest-impact automation opportunities.

This post walks through our founding thesis of Fractional AI, and why we believe this is such a valuable opportunity.

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A decade from now, we believe large incumbents will be 2–10x more productive per capita — primarily by automating existing workflows.

There are now thousands of AI startups in Silicon Valley, many of which are chasing after becoming the next AI product company.

While some new products will emerge, we believe that the most revolutionary impact of generative AI for enterprises will be helping to automate existing workflows for incumbents. These incumbents are the companies with strong market positions and long-standing problems measured in billions of dollars that were unsolvable before the rise of AI. Even modest enhancements in productivity or automation can result in huge value creation when multiplied by their large scale. And unlike other technology shifts, AI is uniquely suited for automating workflows — everything from improving call center operations to medical billing to customer service has a realistic path to automation.

And these companies are keenly aware of this — C-level executives and board members at every company of consequence are thinking hard about this AI-led wave of disruption as both an enormous opportunity and an existential threat.

The challenges for incumbents to take advantage of generative AI are real.

So why haven’t these companies adopted GenAI in a meaningful way? We could cite Gartner reports all day; 79% of corporate strategists see AI as critical to their success over the next 2 years, yet only 20% report using AI. Worse yet, only 10% of enterprises used AI in production in 2023. And this isn’t a simple case of large companies moving slowly — conversations with leaders at small, fast moving tech companies tell a similar story. Everybody has ideas for high impact AI projects, but very few projects have made it past the starting line.

If you zoom in on the ways that these companies have actually adopted AI, it’s usually not the transformative, billion-dollar overhauls you see. They’re starting with low-hanging fruit — using ChatGPT internally, making simple OpenAI API calls, or maybe attempting a PoC of some internally-facing RAG applications. But the truly seismic opportunities, like embedding custom models deep into customer interactions or overhauling call centers, remain largely untapped.

These are complicated: they are often risky projects with high uncertainty and cost, they don’t have natural owners or budgets and span multiple parts of organizations, and they require bespoke solutions that aren’t readily served by the market. But we believe that the biggest challenge is a talent bottleneck.

The biggest bottleneck is talent.

Few of the largest enterprises have focused on hiring the “best of the best” for engineering talent. Most of the time, this talent is expensive and difficult to hire at scale, and great engineers are motivated by engineering-centric cultures, high talent density, interesting technical challenges, and high compensation. While many tech startups focus on recruiting this kind of talent, the needs of a large enterprise simply don’t warrant specializing in this type of hiring.

But this moment is different: to really take advantage of AI for automation, you need elite engineers. The best engineers will be on the forefront of AI best practices when they exist, and will be able to succeed in the face of uncertainty when they do not. For those uncertain projects where a clever approach is the difference between success and failure, great engineering is a crucial ingredient.

So large enterprises are simultaneously the best positioned to reap the productivity benefits of AI, and poorly suited to hire the talent needed to accomplish this.

Fractional AI

Fractional AI exists to bridge this gap. We’re building a world-class engineering team that is focused on unlocking massive value using AI. We do what the big companies can’t by building an in-person team in San Francisco and fostering the culture that attracts and retains highly skilled engineers. We challenge them with meaningful work, invest in their growth, pay them well, and surround them with impressive people. We’re building the kind of startup culture that makes the best engineers actually want to come to work every day.

Our mission is to inspire an elite team of engineers to solve the world’s most valuable problems using AI

Our model

This high caliber team works hand-in-glove with the world’s most consequential companies to solve their most valuable problems with the help of AI. Companies hire us for a blend of consulting and hands-on software creation, tailored to each client’s unique needs.

This services-oriented business model has a number of advantages for our clients:

• It enables us to embrace the bespoke nature of these challenging AI problems; rather than building a product that solves 80% of a need for many companies, we can focus on getting all the way to 100% for a single client.
• It lets us see how real-world problems are solved with AI across many companies, which gives us the vantage point needed to be on the cutting edge of best practices.
• For risky projects with uncertain outcomes, we can share in the risk and share in the upside. It will align incentives and help our clients keep costs down for projects that don’t pan out, paying more only when massive value is unlocked.

Another thing we love about this vantage point is how success feeds our ability to invest in our employees:

• Being on the cutting edge makes for a great learning opportunity. Employees can join with strong engineering skills but limited AI experience and leave as AI trailblazers.
• We have a unique opportunity to experience a best-of-all-worlds work environment: large, impactful, interesting projects with recognizable brands, but an exciting, fast-paced startup culture.
• By seeing valuable projects across many companies, we’re well positioned to spot patterns. When clients experience common pain points that can be alleviated by products that don’t exist yet, we can invest in creating them. Many of our best team members will have an entrepreneurial nature, and this will give us pathways to feed that in the form of side bets, spinoffs, and joint ventures with our clients.

While these two elements of our model feed each other, they also represent separate and distinct commitments. Our dedication to our clients and our team are both first-class attributes of the company, and neither takes precedence over the other.