Posts

Innovation versus distribution

The race between startups and incumbents
Philip I. Thomas Philip I. Thomas
Eiffel tower with olympic rings

Earlier this year, I attended a talk in NYC by Vinay Hiremath, co-founder of Loom. He explained a mental model that's stuck with me.

Here’s the model: When a startup competes with an incumbent, it has an innovative product but seeks distribution. The incumbent has distribution—all its customers—but seeks innovation. So, they race: the startup tries to capture the incumbent’s customers before the incumbent can develop a better product.

Sometimes, the innovator wins, such as when Google surpassed Yahoo or the iPhone overtook BlackBerry.

Other times, the incumbent prevails. In the case of Slack vs. Microsoft Teams, Microsoft Teams now reports about ten times as many daily active users as Slack. Salesforce has also stood the test of time against many innovators.

Some ongoing races include Linear vs. Jira and ChatGPT vs. Google.

To win with innovation, small companies need to be hard to copy (like Figma), have strong network effects (like Facebook), or be ignored by incumbents (such as Lyft eschewing taxi laws).

Big tech companies should not be underestimated. They have become skilled at building products and often let startups do the hard work of validating new markets before they compete. They sometimes engage in tactics that are unethical and potentially illegal, such as cloning features to stifle emerging competitors—a strategy Instagram notoriously employed against Snapchat and later TikTok. These actions often go unchecked because if the incumbent dominates the market, the startup may not have the resources or time to pursue legal action.

I often think about this model because it applies well to many markets. As a startup, you should always ask, “Can somebody just copy this?” As an incumbent, you should ask, “Are we nimble enough to keep our product competitive?” Either way, the first step to winning a race is recognizing that you’re in one.

Internal tools of Find AI

Technical presentation at an AI meetup
Philip I. Thomas Philip I. Thomas
Manhattan rooftop during the 2024 solar eclipse

This week, I presented at the Mindstone AI meetup in NYC about internal tools we built at Find AI. We use OpenAI extensively to build a search engine for people and companies - making millions of daily LLM requests.

In this presentation, I covered two internal tools we built to improve our understanding and usage of OpenAI. The first is a semantic search engine we built on top of OpenAI Embeddings to understand the performance and accuracy of vector-based semantic search. The second is a qualitative model evaluation tool we built to compare the performance of different AI models for our use cases. These tools are internal research products that have never been shown publicly.

I recorded the presentation, which you can watch on Youtube.

Wine craft

2024 harvest season in Alsace
Philip I. Thomas Philip I. Thomas
Rainbow in Colmar, France

Earlier this month, I traveled to the Alsace wine region of France to explore the craft of wine. Their harvest season had just officially kicked off, so winemakers were beginning to pick grades and produce their 2024 vintage.

I love finding people that focus on mastery of one skill. Winemaking is one of the classic crafts, and the Alsace region is a historic region filled with tradition. Many of the winemakers came from a multi-generational lineage of producers.

Even amid the tradition and rules, I saw innovation. In a region known for its white wines, four producers had successfully lobbied for the government to award grand cru designations to their Pinot Noir wines. I visited some of these producers and felt their renewed sense of autonomy.

I brought a DJI Pocket 3 camera to document the visit and turned my footage into a little video about a day in Alsace. Take a look:

Watch the video on Youtube.

How I use data to optimize AI apps

A video collaboration between Find AI and Velvet
Philip I. Thomas Philip I. Thomas
Flower shop in Paris

At Find AI, we use OpenAI a lot. Last week, we made 19 million requests.

Understanding what's happening at that scale can be challenging. It's a classic OODA loop:

  • Observe what our application is doing and which systems are triggering requests
  • Orient around what's happening, such as which models are the most costly in aggregate
  • Decide how to make the system more efficient, such as by testing a more efficient model or shorter prompt
  • Act by rolling out changes

Velvet, an AI Gateway, is the tool in our development stack that enables this observability and optimization loop. I worked with them this week to produce a video about how we use data to optimize our AI-powered apps at Find AI.

The video covers observability tools in development, cost attribution, using the OpenAI Batch API, evaluating new models, and fine-tuning. I hope it's a useful resource for people running AI models in production.

Watch the video on the Velvet Youtube.

Is fractional work the future?

A conversation with Taylor Crane
Philip I. Thomas Philip I. Thomas
Soho House Copenhagen

Today, I'm sharing a conversation with Taylor Crane, founder of FractionalJobs.io. Fractional work, loosely defined as "ongoing part-time engagements," has been a growing trend in the technology industry.

The label "fractional work" is relatively new, but I've been interested in part-time work for years. In 2016, I built Staffjoy using part-time contractors. In 2017, I founded Moonlight to help companies hire part-time contractors. Last year, I launched the FRCTNL community for part-time tech workers. Today, my current company, Find AI, has an official fractional work program and works with five fractionals.

In this conversation, Taylor and I discuss:

  • Why companies hire part-time workers
  • What fractional workers do with the rest of their time
  • Productivity and whether 40 hour/week employment applies to knowledge work
  • Whether junior workers should pursue part-time work
  • How tech companies may structure themselves in the future to take advantage of fractional workers

Watch on Youtube. Listen to a recording of this conversation on Apple Podcasts, Spotify, or other podcast players.

How to self-publish a programming book

Ayush Newatia shares his journey of making the Rails and Hotwire Codex
Philip I. Thomas Philip I. Thomas
Buckingham Palace, as viewed from its gardens.

Today I'm excited to share a conversation with Ayush Newatia, author of the Rails and Hotwire Codex.  He describes the project as “the most challenging and rewarding professional work I’ve ever done.”

I bought his book a couple of years ago to help me learn more about building full-stack applications with the Ruby on Rails and Hotwire frameworks. It bridges the gap between beginner-level tutorials and building Rails applications in a professional setting.

Ayush and I met in London last week to collaborate on some work with Find AI, so I thought it would be fun to record a chat about how and why he published this tome about full-stack development with Ruby and Rails.

We cover:

  • How he came from the iOS + Android mobile apps to Ruby on Rails
  • Why he decided to write a book unifying Rails + mobile apps
  • How he had never made a mobile app with Rails before starting a book on this topic
  • How he motivated himself to finish the book and get it shipped
  • Why writing is a superpower for programmers
  • The value of doing hard things without taking shortcuts
  • How doing hard things is the best way to stand out as a modern knowledge worker

Watch on Youtube. Listen to a recording of this conversation on Apple Podcasts, Spotify, or other podcast players.

The next iteration of Contraption Company

From indiehacker back to venture-funded founder
Philip I. Thomas Philip I. Thomas
Coffee at Allpress in Shoreditch

Listen to a recording of this essay on Apple Podcasts, Spotify, or other podcast players.

When I left Webflow two years ago, I started working full-time on Contraption Company, a product studio that makes tools for online work. I sought to build independent software, and I launched products like Postcard, Booklet, and FRCTNL.

Two years later, my role with Contraption Company is changing. I am now focusing full-time on Find AI, a venture-backed startup I co-founded.

I started Find AI earlier this year, but the story precedes that. Around the time I left Webflow, I began working with an AI lab as a fractional head of product. This part-time work funded my studio while I built its first applications.

Over those two years, the AI lab experimented with several product ideas, ranging from enterprise software to consumer mobile apps. We built many cool experiments that pushed the edges of LLMs. I also enjoyed working with the AI lab founders—they contrasted my skills, and I learned from them.

Earlier this year, the AI lab shut down its last product, and the founders began exploring other ideas. One hatched plans to build a massive GPU data center. He hired an outbound sales company to find customers, and he was frustrated by how much time the agency seemed to waste just clicking around LinkedIn. So, I got a call - "Could we use OpenAI to automate this?"

I started by scraping the YC startup directory and then used OpenAI to analyze the data, asking, "Find startups that might want to rent GPUs." The script was slow—it took hours and thousands of dollars to run. But the results were excellent —and this system far outperformed human lead generation. Instead of using LLMs to write text or build a chatbot, this application used them to analyze data.

"This could be a product," we thought. I set up a website, Find AI, and got to work. I added more data, optimized the searches to be faster, and developed techniques to make them cheaper. As I did that, people began to find the website, sign up, and even pay us. Before launch, we had customers ranging from prominent investors to a Fortune 500 company. It was clear that we were solving a problem.

Initially, I continued building Contraption Company projects while working on Find AI. But, as time passed, I began waking up each morning with more excitement for Find AI than my indie projects.

As Find AI began hiring, I applied the future of work theories I had been developing at Contraption Company. I set up a Booklet for async communication, launched an official fractional work program, and began hiring from my FRCTNL community.

We launched Find AI two months ago, and the response was overwhelming. On launch day, we had about 50,000 visitors, made millions of requests to OpenAI, and gained more customers. I've spent the weeks since launch scaling the software, developing new features, and hiring more people.

Along the way, I realized - I'm not building indie software anymore.

I started Contraption Company to build the software I wanted. When it was clear that the market didn't love those products, I chose not to pivot because I was bored by the alternatives. In hindsight, I chose to build an "independent" business instead of raising money because, given the choice between interesting work and commercial success, I preferred interesting work.

With Find AI, everything changed. I found a project I want to work on and a project that the market wants. That's a rare and valuable confluence, so I've gone all-in on this startup and stopped building a product studio.

For the past two years, I have treated Contraption Company as a mix of both art and business. Now that I've picked a business pursuit, I can unbundle—with Find AI for business and Contraption Company for my creative interests.

Contraption Company will now be more of a media brand where I'll write essays, share conversations, and publish fun projects. I intend to pursue my interests here without applying the filter of commercial viability.

If you want to follow along with my journey and work, subscribe to get updates.

In most cases the recipe for doing great work is simply: work hard on excitingly ambitious projects, and something good will come of it.
- Paul Graham in "How to Do Great Work"

The opportunity of tech talent agents

Philip I. Thomas Philip I. Thomas
The opportunity of tech talent agents

With the shift to remote work, companies unlocked a global labor pool. Job posts began receiving hundreds of applications each. In the past months, AI tools accelerated this problem by enabling candidates to spray-and-pray applications to hundreds of jobs at a time. Companies are struggling to hire amid a sea of noise.

According to the Paradox of Choice, when faced with multiple options, people either approach the problem as “maximizers” seeking the best option or as “satisficers” who settle for a “good enough” choice. The status quo is fine for companies looking for “good enough” candidates, such as big corporations. But for startups and small businesses that care about finding the best talent, hiring in the current environment is a nightmare.

Sometimes, unexpected answers can be found by looking at how things are done in other industries. In the case of tech hiring, the solution might be Hollywood. Movie studios don't hold open castings for the starring role of every film. Instead, they seek out proven talent. To find that proven talent, studios go to talent agents.

Compared to ten years ago, far more startups hire contractors instead of employees. This shift started with the rise of remote work, where companies structured almost all foreign hires as contractors to simplify compliance. The recent tech downturn drove more contractor and fractional hires because these workers were more flexible and expendable than employees. The slashing of middle management across companies like X and Meta further reduced incentives to hire "good enough" employees because managers became judged on output instead of headcount.

As AI has driven a recovery in the tech industry, many companies have stuck with contractors because they get work done. Contractors tend to be experienced professionals who focus on output instead of politics. And, the contractor process bypasses the slow and bureaucratic process of hiring or firing an employee.

Historically, companies leveraged external recruiters to find employees. These recruiters earned a sizeable fee per hire - typically 25% of the employee’s first-year salary. This pricing made sense in an era when employees expected to stay at a company for years. However, over time, recruiters became incentivized to have candidates change jobs frequently. By the mid-2010s, as companies such as DeveloperAuction offered employees $2,000 and a bottle of Dom Perignon to switch jobs, companies began pushing back on high recruiter fees. Recruiters have been struggling ever since.

Modern recruiting fees are rooted in US tax law. Companies can write off the fee as a business expense, but employees can’t. So, it’s cheaper for the company to pay the cost because it comes from pre-tax dollars.

Attempts to have employees pay the recruiter fees have largely failed. Lamba School popularized Income Share Agreements (ISAs), and Free Agency tested them in tech recruiting. Both companies struggled because fees were calculated on pre-tax salaries but paid post-tax, leaving employees with sticker shock as their 25% ISA ate up 40% of their take-home pay.

Hollywood originated the talent agent model. Talent agents help their clients find work, negotiate better terms, and navigate their careers. In exchange, they clients pay the agent 10% of all of their earnings. (If you work in tech recruiting, definitely read Powerhouse).

Pricing is inexact for freelance labor in tech, deal flow is inconsistent, and career growth is uncertain. A talent agent could help with all of these—setting the right price, avoiding feast-or-famine work cycles, and finding the best opportunities instead of taking the first one that comes along. The value of those services offsets a 10% fee for an independent contractor, and the contractor can classify the fee as a business expense.

Tech companies aren’t used to working with talent agents. But, they act like no-fee recruiters who can curate the best matches for a company. And that’s what startup hiring managers want right now - a curated selection of two to four outstanding candidates. Talent agents can provide the Goldilocks zone between the noise of job boards and the high fees of recruiters, with a more personal and human relationship than any marketplace can provide.

I think it’s time for people to try becoming tech talent agents. With a 10% fee, you only need to represent nine clients to make the same income as them. Finding nine clients seems more straightforward than building a job board or making one-off placements, and the revenue is recurring. Start by finding freelancers, offering to help find them clients, and setting up a deal referral agreement.

Remote work has flattened the global labor market, and hiring managers need help navigating a world of candidates. Recent tech layoffs began shifting knowledge work to be more transactional—hiring managers are no longer sitting in an office with their team, so performance matters more than “culture fit” when hiring. The most exceptional talent has realized they have more leverage than ever and can demand higher rates and more flexible terms. Companies seeking the best talent will increasingly find that talent agents are the best way to hire them.

Thanks to Aaron and Emma for reading drafts of this.