Understanding the Casetext deal requires us to take a helicopter view of the landscape. We’ll need both a copilot and a landing pad.
A startup with only one employee and no outside funding sells for $54 million. Wargraphs was founded 10 years ago, and the reported revenue last fiscal year was $12.3 million. If you don’t have ten years, consider the following. Enter Dumb Money, the story behind Roaring Kitty and GameStop. Roaring Kitty reportedly made $20 million within four months betting against short sellers. Now what do Wargraphs, Roaring Kitty, and Legalcomplex have in common? Despite the different markets and outcome, it’s a single person behind a single PC doing data analytics.
The theory that it takes an army to build a profitable company is slowly being debunked. The underlying idea of technology is to leverage it as a tool. Even though these tools are getting more powerful, they’re also becoming more affordable. And now every company is looking at their human capital and wondering, how much do we actually need? If you’re in one of these companies, I empathize but remember: you are free to fly solo. You may only need a copilot.
This brings us to the reason you’re here: Casetext will be acquired for a staggering $650 million by Thomson Reuters (TR). By the way, Casetext was the second acquisition by TR in the week the deal was announced. Imagen was bought at a 1.9x return. A company we’ve been tracking since 2019. So there is a bigger picture, we’ve yet to see. Pencil this date into your calendar: Wednesday, 2 August 2023, and join us at the Q2 earnings call. In Supercycle, we explained why we sit in on calls.
Now let’s proceed by answering two questions:
- Why now?
- And is it too much?
We classify Casetext as ‘Legal Research’. According to the TC article, they eventually morphed into more than just search & retrieval of legal data. Now one can use a Generative Pretrained Transformer (GPT) to search, retrieve, analyze, and draft texts all at once. Hence, many refer to this as “generative AI”. Unfortunately, we’re allergic to the term AI. Yes, we’re taking pills and eventually, the sneezing (bullsh*t) will stop. Instead, we use the term ‘Text Analytics’ because it highlights the key additional capabilities: recognition and analysis.
Based on the reasoning above, we classify companies as follows:
- Text Analytics;
- Data Analytics (which can also handle numbers and calculations);
- Audio Recognition;
- Image Recognition.
Transformers take the data inputs above and transform them into drafts, drawings, or any format fit for human consumption. Actually, transformers are much better at providing computer code than human content. They essentially generate food for bots. We endearingly call these bots ‘copilots’, and every industry will have many flavors. For programmers, content writers, doctors, and legal professionals. Ultimately, transformers opened the floodgates for anyone to build copilots. So now we’re all in a footrace to distribute them. Hmm, distribution.
This brings us to $650 Million, and we ran this through GPT using multiples and DCF calculations. Most came back with unrealistic assumptions of revenues and returns. So here’s a simple angle: Casetext raised $64 million from investors and is acquired for ten times that amount. That is 10x return in ten years, resulting in a 29.2% CAGR. The investors get 10 times the invested $64 Million. Here’s where we’re going with this: what does Thompson Reuters expect to get in return?
If Thomson Reuters paid 10 times, logically, they should expect to gain at the same rate of return. This means by 2033, they should expect to earn $6.5 Billion from this investment. This logic only connects if the growth of paying customers remains at or above a 29.2% CAGR in the coming ten years. Now this gives us something to look for by August 2024 earnings season. We love to know: will demand grow?
Since consumers will get most copilots for free, professionals will likely want the premium stuff. Sophisticated users desire solutions that offer a complete package. This means that when we offer a pilot, the pros will also want a plane and an airstrip for takeoff. In short: building the infrastructure for pilots to thrive doesn’t come cheap. From that perspective, the value makes sense. In fact, the deal may even be a bargain. Heck, everyone will want to fly private eventually. Therefore, we aren’t worried about demand nor supply. However, what about distribution?
To circle back to my GPT-4 factor question on LinkedIn: it was a rhetorical one. OpenAI owns GPT, and Microsoft paid $11 Billion to push it onto every lawyer’s cockpit. Google, has a $1.5 Trillion market cap and arguably the deepest bench of AI brains. And they still haven’t caught up. So, becoming the Legal Copilot with just $650 million might be a brilliant move ten years from now. Note: Thomson Reuters had $1.3 Billion in free cash flow by the end of 2022. So it’s not like they broke the bank to broker this deal. They’ve been pretty quiet on M&A up until now.
Still, it leaves us wondering one thing: why didn’t Thomson Reuters go directly to OpenAI? FiscalNote did, and if you’re curious about how that turned out, watch their video on our TV channel. Stick around for the second video, showing us another interesting pilot. Sadly, that plane disappeared from our radar. After you’ve watched the video, you’ll understand why it may have been shot out of the sky.
I hope you had a pleasant flight, and a special thanks to our flight crew: