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Raymond Blyd

Pivot: Does Your Business Still Hold Value Without A.I.?

Pivot: Does Your Business Still Hold Value Without A.I.? 2560 1494 Raymond Blyd

While everyone sprinkles spicy AI sauce on their product, some realize that their customers’ appetites are changing. So, should you pivot?

TL;DR: AI presents current products with this warm blanket of extra usage. However, more AI usage on top will impact the bottom line. Tokens look cheap now, but they are heavily subsidized. Hence, the question: where is a product’s value if it needs AI?

Burn 🔥

Why are we talking pivots? We never did before, plus now is the best time. Between 2020 and 2022, about 953 companies raised funds in 1546 rounds, taking advantage of the low cost of capital. Yes, some raised multiple rounds due to low-interest rates. In 2023, the music stopped. However, those companies have to keep dancing. Now, focus on the little flame, where it says ‘Burn‘ in the dashboard above. The 120 number represents the median number of days between rounds during that stretch across all fundraising activity.

We’re proud of our Burn metric in our dashboard. It estimates when a company will need more capital after fundraising. Burn is influenced by the type of business and geography, and is not equal for any two companies. Case in point: a legal tech company in Brazil becomes profitable faster than its counterpart in the USA.

Ultimately, Burn is the most probable answer to performance for any segment, area, and geography. Probably, a large language model will come up with a better indicator for performance. Perhaps that will be a sign to pivot.

In general, we’ll keep track of fundraises especially of Debt Financing. Since Debt is a strong indicator of economic health over time. Debt also fuels the greatest power in our industry. Debt not only causes us to lose friends, but it may also cause friends to lose jobs.

Value 💰

We’re chatting off-air about me being invited as an expert on a radio show. I said: “nobody can realistically call themselves an expert in anything anymore”. AI has now surpassed our knowledge capacity in almost everything. I’m only slightly more reliable at processing speech in real-time. Many aspects of my value as a productive human are debatable compared to AI. A fact that some consultancy firms may already be experiencing. I made my peace and started focusing on what would be worthwhile. Part of this journey was looking at the most valuable legal business models in Data, Distribution & Deals.

Our analysis concluded that legal data will be completely absorbed by LLM’s and copyright can’t stop it. Distributing anything with AI looks cheap now because access to AI is heavily subsidized. With interest rates still high, deals are drying up. Currently, litigation is booming but mostly for Elon and OpenAI lawyers. Remember, ‘booming’ in legal is impacted by geography as well as in which season we are. More on where money is made in legal? Check the Capital & Conflicts analysis.

In short: At the moment, value in legal is fuzzy. Value is much clearer in segments like Governance, Risk, and Compliance (GRC) also known as Reg Tech. Hence, law firms building chatbots instead of buying legal tech. In case you missed our earlier hint: Legalcomplex will also pivot to a different model. Generating any deep insight is now firmly in the hands of machines. The future value lies in helping machines bring those insights closer to us.

Pivot 🌱

A pivot is a beauty and a beast. We can start fresh and shape products of the future. That means we have to start over and relive the pain of the past. On the one hand, we are not in a panic about allocating $8 Billion to rescue revenue. On the other hand, we don’t have that luxury either. We can not just tap $600 Million in credit to buy time. What does this all mean? Everyone is putting their chips on the table. With AI, everyone has a seat at the same table. The question is: Where will we place our bets?

We assist enterprises in finding opportunities by calculating investments. We use the Burn and Growth metrics to gauge each segment, area, and geography. There are roughly 1151 companies globally in Legal that need cash and are open to talk.

The image above shows us how many companies want to have a conversation each quarter. The number in each column represents when they burn out of capital and need to raise again. The quarter represents the time, we most likely can expect a call from them.

Call 📞

So we’re meeting with a few of the 1151. Helping them raise more capital or exit fast. During these talks, the topic of a pivot has come up. A pivot helps with pitching your product to investors and acquires. The reason is that a new direction would increase the value of a company. So how do we determine value?

The value of companies in legal relies on that other holy trinity: Targets, Talent & Technology. Presently, companies with income from customers (Targets), have more time to assess a new direction. Those with just tech and talent may need to pivot sooner. And steer away from burn faster.

To conclude: Eventually, everyone in legal has to pivot to value. The best time to pivot is now. Where too is up to you.

Layoffs: After A.I. Takes Our Legal Jobs, What Will Lawyers Do?

Layoffs: After A.I. Takes Our Legal Jobs, What Will Lawyers Do? 1792 1024 Raymond Blyd

Sports need rules and referees. So does society. Interestingly, technology gave us more referees. So will we get more lawyers after layoffs?

Queen’s Gambit

Here’s how we’ll structure this one: First, look at what lawyers do to understand why they become redundant. Then show what happened in other industries after tech came in. Finally, let’s explore the emerging roles impacting the legal field. Keep an open mind, and don’t let fear cloud your judgment.

Four years ago, we examined what lawyers do and why they will be displaced by GPT. Here’s a quick recap: a lawyer finds, drafts, interrogates, and explains legal data to a layperson. That is exactly the skills we saw GenAI master. A GPT can only perform these tasks accurately if it has access to correct legal data. To gain access, we have to change some laws. To improve accuracy, we should acknowledge our biases in legal data. Now, we are going through the displacement phase.

Understandably, it’s difficult to accept our skills becoming obsolete. Imagine how Gary Kasparov must have felt when he lost to Deep Blue. Interestingly, chess has become more popular than ever since then. In 2013, we wrote about Freestyle Chess and the symbioses of humans and machines. More recently, the pandemic hit series The Queen’s Gambit caused a resurgence in interest in chess. Ultimately, games are the best simulation of society, and we can use them to envision the future.

Top Corner

The Africa Cup of Nations (ACN) hosted 52 football matches, with only one notable VAR controversy. This was an amazing outcome for a tournament that has a rich history of horrible officiating. VAR is the acronym for Video Assistant Referee and is our favorite analogy for legal. This analogy is useful to reveal the flaws when using technology to decide right from wrong. And now we’ll use the same analogy to demonstrate how technology can create new jobs.

Originally, football only required one referee. Now we have up to twelve (12) officials, plus a fully automated referee managing major matches. Let’s list them here to illustrate this point:

  • Main Referee (1)
  • Assistant Referees (2)
  • Fourth Official (1)
  • VAR – Video Assistant Referee (1)
  • AVAR Assistant Video Assistant Referee (4)
  • Offside VAR – OVAR (1)
  • Reserve Assistant Referee (1)
  • VAR Technician (1)
  • Goal-Line technology GLT (1)

That is eight (8) new human positions and eleven jobs created by technology. We haven’t calculated how many people work on developing and setting up VAR. Fun fact: GLT, is the only automated official, and also the only official with a spotless record. Makes you think, doesn’t it? So, why has technology led to more arbiters in sports?

Bending Rules

On the streets, everyone can call a foul, and no one cares. A Champions League final attracts 450 million viewers. For context, this dwarfs the 123 million viewers of the last Superbowl. That is a lot of angry customers when bad calls mess up a match. Contrary to fake football, we watch the game and not the TV ads or Taylor Swift. To avoid controversy, sports implemented rule changes and allowed more technology into a high-stake emotional experience. Unfortunately, in law, we aren’t ready to make those changes.

There are valid reasons why the law has not gone fully automated. In ALI, we discussed some academic observations and technical limitations. Notably, neither Perplexity AI, Gemini, nor ChatGPT with Webpilot were able to provide the 52-match number at the ACN. However, once we’ve fixed those bugs, and changed the rules, we’ll be watching an entirely new ballgame. In a world divided, can we agree to change the rules?

Let’s pick our favorite subject: Taxes. Why, and how much, has roughly remained the same for as old as civilization. To clarify “how much”: the poor always pay more than the rich. Even though the world is in constant conflict on everything, we did agree on one thing: a global minimum tax for companies. Imagine: 138 countries agreed they want this. This global tax treaty is a win for the legal profession. It shows that with our skills we can achieve peace. Especially now that peace has become so precious, and is in increasingly short supply.

Legal Layoffs

Lawyers and other legal professionals are trained peace-makers. This role extends into all facets of politics, business, and society. So the shift is not what we do, but how we do it. Currently, we’re seeing these tasks emerge in related areas:

Chief AI Officers (CAIOs) are gaining traction as artificial intelligence (AI) becomes more integral to businesses. A CAIO can help companies leverage AI to outperform rivals. Eventually, AI will build AI, so you don’t need to know what RAG is. What you do need is strategic regulatory and security expertise. Essentially, AI will replicate but will not regulate itself.

Customer Due Diligence (CDD) is a process used by financial institutions to prevent financial crime and uncover any risks. I overheard this during a radio broadcast: Dutch banks annually hire more fraud fighters than the government can hire police officers. Point: policing digital crimes is harder than we can handle.

Fact-checkers verify the accuracy of claims made in public discourse. Currently, lucky lawyers do the same while working with AI models trained for legal work. Their job is to double-check the outputs for inaccuracies. They are lucky because it’s now unaffordable for most. Eventually, legal professionals will be supervising inputs and verifying outputs from law bots. Remember in legal, hallucinations can also be a feature, not a bug.

Extra Time

Debatable studies showed that machines can negotiate and review contracts more efficiently than humans. Undoubtedly, machines draft faster. What about legal research and legal briefs? According to former president Obama, AI is already on a 4th-year law associate level. This is similar to what presidential candidate Al Gore said about eDiscovery over ten years ago. We have seen this movie before.

Inevitably, every traditional legal task will be done by a legal operating system. That’s what AI is: an operating system to perform actions at scale. The legal industry will develop its operating system, with special action models which can also run off-grid. Now imagine, who would we need to work on this?

To summarize: If sport is any indication, society needs about eight (8x) times more legal professionals than we currently have. We’ll also need a lot more automated legal arbiters to maintain peace. And they all need to be developed and sense-checked.

In closing: It’s disingenuous to keep saying to lawyers that they will always be smarter than AI, no matter how many trillion tokens it has. Legal professionals are the best-paid professionals on the planet. But only in London, New York, San Francisco, and other major metropolitan areas. And only involving very high-stake emotional transactions. So a lucky few get to play in the Champions League of Law. The rest of us will be running the streets, practicing the law we love.

Image Courtesy: DALL·E 3 by OpenAI using Glibatree Art Designer and the content of this analysis.

ALI: Can We Build Artificial Legal Intelligence?

ALI: Can We Build Artificial Legal Intelligence? 1792 1024 Raymond Blyd

We welcome you to the weird world of GenLaw with our 1600+ legal GPT store. Now, ponder: Can we build Artificial Legal Intelligence (ALI)?

Three years ago, we made a video that foreshadowed the struggle. The video below shows how much money is invested to influence the law compared to how little to improve it. In 2020, we already pointed at OpenAI, among others.

Academic Angle

In the previous post, we stated that legal data has the complete DNA to create autonomous legal services. So by adding legal data and aligning Large Language Models (LLM), we should already have Artificial Legal Intelligence. However, we have yet to see ALI. Notably, here are some academic observations as to why we haven’t:

I shared my views in the first and second study on LinkedIn and The Legal Tech StartUp Focus. Most importantly, my friend John Barker made another observation on GPT and Claude: they have been getting worse over time on specific legal queries. In this interview, Sam Altman acknowledges that out of the 10,000 answers an LLM will offer, maybe one of the answers will be perfect. GenAI relies on embedding vector databases, which results in a different output every time. Having 10,000 slight variations on the same topic will be a big deal in some areas like Health.

In Legal, we need consistency on every output. So using GenAI to dispense accurate legal answers every time will be challenging. Baffling to me: why can GenAI generate programming code consistently? Undoubtedly, progress will address these concerns.

Apps Aligned

Considering the above, have you tested any of the Copilots on Legalpioneer? I would love to hear your thoughts. Here are some, that may never get funded but matter to our everyday lives:

Contrary to the above, here are some topics that seem obvious candidates for funding:

  • Security
  • Human Resources (HR)
  • Anti-Money Laundering (AML)
  • Know Your Customer (KYC)

Of course, there is a large set of copilots looking at contracts in spaces like:

  • Mergers
  • Music
  • Real Estate

We’ve seen legal copilots with multi-modal support and graphic outputs to help you visualize laws or patents. Some might ask: where are the apps for lawyers? We have research and drafting apps, but again, do we dare to use them?


This brings us to the future. At the end of 2022, we told Law360 about the increase in consumer-facing legal. Now we’re seeing these client-centric apps proliferate. We will also see the emergence of apex apps. These apps aim to sit on top of the food chain. Perplexity AI and ARC Search are two examples.

Let’s recap: There is a blueprint for Artificial Legal Intelligence and a roadmap. We’ve posted the ‘road signs’ on LinkedIn. Here are five evolutionary stages we’ve seen, with the date we posted them:

  1. GenAI is an operating system (OS) – January 11, 2023
  2. The types of legal copilots – November 13, 2023
  3. Likely most popular legal copilots – December 15, 2023
  4. The power of Decentralized ActionAI – January 14, 2024
  5. Likely most dominant copilots on law (Apex) – January 31, 2024

Point #3 had the second most views on my feed ever, and point #4 had one of the fewest. The above reminded me of this famous quote:

The future is already here – it’s just not evenly distributed

William Gibson

In closing: I made the video below on a beach in Portugal during my summer break in 2023. This blueprint was part of our September 23 webinar. It looked weird when I made it, it makes more sense to me now.

Do you have thoughts or concerns about building ALI or Copilots? Let me know on LinkedIn.

Image Courtesy: DALL·E 3 by OpenAI based on the content of this analysis.

Anatomy of a Legal Acquisition

Anatomy of a Legal Acquisition 3750 2188 Raymond Blyd

We studied 4,172 mergers and acquisitions and distilled three insights on how founders exit their companies. So, what is peculiar?

This week we released our Mergers and Acquisitions (M&A) numbers. You can find the download links at the bottom of this post. In 2023, the most dramatic stat was the 85% drop in total transaction value. Furthermore, our analysis revealed M&A activity has been decreasing since 2023. Now, when you hear so-called experts say, ‘We’ll see more consolidation,’ remember: that is a lazy statement trying to look smart. However, despite the dramatic drop in transaction activity and value, one other peculiar trend caught our attention.

Acquisition Targets

First, let’s answer a straightforward question: Why would one acquire a legal tech enterprise? Looking at the 95 deals we registered, here are three insights that we could deduce from all:

  1. Acquisitions are mainly driven by targets, talent, or technology, in that order;
  2. Targets refer to customer bases that help boost the balance sheet with a robust ARR (Annual Recurring Revenue);
  3. Talents achieve the best outcomes through bootstrapping, as any outside funding makes them vulnerable to lockups;

None of the points above should be shocking for anyone. Indeed, acquiring a company that has already done the hard work is the fastest way to gain a fresh set of attractive customers.

Consequently, companies accomplished this by either being a better seller or building a better product. This leads us to question whether this was a talent or technology play. Now come the handy insights for founders: did you really build the fastest car, or are you just an excellent driver? Because once a founder has been in the driver’s seat, it’s almost impossible to get comfortable as a passenger.

Acquiring Tech & Data

We continue our tradition of not naming names. Yet, we can manage two examples to illustrate what looks healthy to us.

3E, a provider of chemical compliance solutions, has acquired Chemycal, a 7-person regulatory monitoring shop based in Rotterdam. Additionally, Chemycal aggregates chemical and product compliance data from over 1,500 sources and maintains a library of substances and materials to keep customers updated. Thus, the acquisition combines 3E’s expertise in environmental, health, safety, and sustainability compliance with Chemycal’s global regulatory monitoring capabilities.

The private equity firm Thoma Bravo is acquiring the German software company EQS Group, which specializes in compliance and investor relations software, for €400 million. This acquisition price, marking a 53% premium over EQS’s previous stock price, indicates that the deal is likely to be completed. Despite EQS being a relatively small public software company, this move has led TechCrunch to ponder whether compliance tech represents a good startup bet.

Adding Debt

Ultimately, this brings us to the reason you probably clicked on this analysis: DocuSign. It was first reported on December 15, 2023, that DocuSign was working with advisers to explore a sale. According to the Wall Street Journal, it could potentially be one of the largest leveraged buyouts in recent times. While debating this on LinkedIn, one thing dawned on me: why private equity (PE)?

As always, there is an upside and a downside. Notably, Private equity firms offer founders and owners a generous way out of their businesses. The downside: a private equity acquisition is not the same as Facebook buying Instagram or Google buying YouTube. The Financial Times reported that private equity firms often resort to buying back companies after IPO flops. If you wonder why private equity buy companies, check our Power post. The TL;DR: private equity firms are investment vehicles that enable high returns for their investors. And they are able to achieve that by fancy financial engineering.

This perhaps explains why private equity firms acquire companies with little to no prior venture capital (VC) investment. After examining M&A transactions we have registered, it’s clear the majority of companies acquired by PE do not have any VC capital. Even when a PE-backed company goes on a shopping spree, they actively avoid VC-backed companies. Very peculiar.

You can download the M&A report here and schedule a chat for a one-on-one deep-dive.

What Are Legal Agents, And The Next Stages Of Legal Services

What Are Legal Agents, And The Next Stages Of Legal Services 1940 827 Raymond Blyd

We discovered our first legal agent on the first of November. First of December, we counted 559. What are Agents, and what can they do?

Intro: This is our first-ever double header. The previous one was a bit dark, and we want to end the year on a bright note.


Before we throw any numbers at you, a bit of context. We covered a similar eruption of new apps twice before. In 2016, we recorded 44 new companies in a couple of months. We called it the Hottest Summer. During the crypto craze of 2018, we found 55 ‘legal tech’ companies raising a Billion in coin. Back in 2015, ProductHunt report, we uncovered a growth of 43 apps in roughly the same amount of days. In the early days of the Apple App Store, we noticed a similar growth. But nothing quite like this.

We were warned four months ago when we found 81 apps in 28 days. 550+ new solutions in one month set a new record. Now, will these new interfaces all be displacing current legal solutions and services? Not yet, and we’ll explain when this displacement will happen. These new apps will face similar struggles as the App Store, and ProductHunt platform posed. However, now there are different market conditions. In “Data, Distribution & Deals” we discussed market conditions that affect supply. This analysis will explore the demand side. Why do we need agents, and are they better?


In order to answer, here is a brief history of how we got here. We first discussed agents in 2011: Call My Agent. Basically, we wanted systems to learn from our content, derive instructions and tasks, and complete these tasks. They could perform their tasks autonomously in the background. This would allow us to access the results instantly at runtime.

In 2013, we created Loupe as a concept that would track regulations and instantly update affected contracts or briefs remotely. You would need a dashboard (DESH) to keep track of all edits deployed on each document. We even made an Apple Watch interface.

The same principle applies to legal research. We could dynamically convert phrases in documents to background queries. That was called Monocle and even made a teaser video. We submitted eleven patent proposals. Little did I know that each submission costs a fortune. Only one was patented, making me an inventor.

Why did we bring this up? Cooking these concepts in the lab, we discovered a few fundamentals of legal work. First, autonomous legal research is about anticipating legal issues, and calculating them beforehand. Second, the legal issues are already present in legal content. You just need to add the right context, as shown in the image above. In short: legal data already has the instructions and tasks baked in.

The biggest problem back then is actually what everyone now knows: calculating has immense costs. The OpenAI breakthrough is that no one has to pay that much. By creating GPT, OpenAI has democratized Legal Agents. Give GPT the right legal data, and anyone can create a Legal Agent.


What is a Legal Agent? Based on a given context, a legal agent can serve up a legal end-product like a contract, or research brief. If you ask GPT, it will provide a more technical answer. Something like “entities that can perceive their environment through sensors and act upon that environment using actuators. They’re designed to achieve specific goals or perform certain tasks.”. Not helpful, except it does provide the key ingredient for agents to work: context. Legal Agents get Legal context from instructions and legal data.

Before GenAI, every legal technology solution had to derive context from the user manually. As a Legal Knowledge Engineer, I had built legal decision trees and the process was very tedious. The tools were cumbersome and we could never encompass all cases. We just focused on the most complicated ones for commercial purposes. Now, with the right instructions, and a fined-tuned model can figure it out. Better yet, you can ask to explain the answer. This was the real breakthrough of TaxGPT. So are we there yet? Not yet. Let’s highlight one key difference we uncovered while covering GPTs: Copilots answer your questions, Agents take actions. So what is the next stage?


The launch of GTPs on November 6, 2023, made instructions more explicit for ChatGPT. However, OpenAI demoed instructions months earlier on March 14th, 2023. Remember TaxGPT? Yes, the first agent by OpenAI was targeted at Tax. Now there are around 90 TaxGPTs and counting. However, none can submit a tax form to your local IRS. While the current generations of legal agents can give you perfect answers, most can not take any action. Part of this constraint is security by design. Large Language Models will prevent any kind of code execution outside a sandbox. However, developers, that have APIs for actions ready in their apps, can now use GPT output as input. As the Copilot Battle states, this is the footrace, so hurry. Wait! There is one more thing.

Besides structured legal issues, we made one more discovery in our Ancestor era. A quick step back: The best definition of Artificial Intelligence I heard here at 4:59 min on the Waveform Podcast. The short version: AI is planning tasks in the right order to execute them. Sticking with the TaxGPT, here is a simple plan:

  1. Upload your bookkeeping*;
  2. Classify entries;
  3. Find the right deductions;
  4. Fill out the tax form and;
  5. Submit tax form to Tax Office.

*Note: Another Agent did my bookkeeping.

What tax and legal data provide is an understanding of how to structure tasks and actions to solve legal problems. Legal data has a complete recipe. Given this recipe and the ability to take action, Agents will be able to act autonomously. So the next stage of legal solutions is autonomy. And now you know why Sam was fired.


On the roadmap to autonomy, we have to travel through the Three Universes from “Will lawyers be replaced by GPT” which are:

  1. All legal data needs to be processed in GPTs. As stated in the previous post, it eventually will;
  2. Learn how to construct Agents to allow them to safely take actions, so allow autonomy;
  3. Interceptors do not squeeze developers, so allow fair competition;

This third milestone is, economically, the most difficult one because of Interceptors. Example: Google Bard now supports extensions and they are all Google apps. OpenAI was supposed to be open, but now it’s just Microsoft Copilot. Ideally, GPT becomes an open utility, just like GPS. Politically, it seems we are beyond that possibility. Potentially, agents can save the world, if we agree we want them to.

Meanwhile, we counted over 24,000 GPTs created since November 6th. That is 750 GTPs a day.

Data, Distribution & Deals: How Money Is Made In Legal With Tech

Data, Distribution & Deals: How Money Is Made In Legal With Tech 1792 1024 Raymond Blyd

Around $40 Billion of business supporting legal relies on copyrights, complexities, and expensive distribution. Now that’s all in jeopardy.

TLDR: This is twice our usual length, so if you’re in a hurry, skip to the Recaps and Daybreak.

Premise: Any business needs a model for making money. A tech company model is to use software to solve problems and then sell this solution with a profit. The problems in legal usually involve complexity. So the business models for legal involved finding and fixing complexities. Now, this is tricky: Finding complexities in legal with tech is very lucrative. Fixing them, not so much. Well, guess what happened?


Based on 2022 annual revenue, the three biggest companies supporting legal work arguably are:

  1. RELX (owner of LexisNexis) – $8.55 Billion;
  2. Thomson Reuters (TR) – $6.63 Billion;
  3. Wolters Kluwer (WK) – $5.45 Billion.

That is $20.6 Billion of revenue for 2022. They did not achieve this staggering feat overnight. Each company was founded in the late 1800s. They achieved gradual growth by accumulating essential legal content. Two key factors drove their model: Copyright on content and competency requirements for legal professionals. In essence, they monopolize the data that lawyers need. Eventually, RELX, TR, and WK diversified their product portfolio away from legal. Yet, the legal market made this model sustainable for over a century.

Historically, distributing legal data was expensive because it required printing on paper. The printing press was owned by publishers and allowed the monopoly. Lawyers also have a monopoly on legal work, which allows them to maintain complexities. For these reasons, the legal industry could keep raising prices and making profits. This flywheel effect resulted in ‘legal inflation‘ and years of wealth in the legal market.


Non-Legal Data

With the internet and cloud, the distribution of legal data got cheaper. More legal professionals got smarter research portals and e-Discovery platforms for non-legal data. In particular, e-Discovery platforms managed enough revenue growth to go public. Here is a list of the ones we’re tracking:

  1. Open Text$3.49 Billion;
  2. Palantir$1.91 Billion;
  3. KLDiscovery$317.43 Million.
  4. Information Services Group$286.27 Million;
  5. Intapp$272.07 Million;
  6. Model N$219.06 Million;
  7. Nuix$152.31 Million;
  8. CS Disco$135.19 Million;
  9. Fronteo$73.17 Million;

That is $6.8 Billion of revenue for 2022 and remember that their revenue streams are also diversified. Why e-Discovery is the biggest cohort is due to a craze, we experienced in the early 2000. The Exit and Funding videos show this historic rise and fall in graphic detail. We’ll spare you the legal drama some encountered during their public run. Same for the earnings per share (EPS), an indicator of profitability.

Other Legal Data

Primary legal data monopolies prompted others to search for secondary legal data. They focused on areas like Practice, IP, Billing, and Government. Here are some we track that support legal professionals:

  1. Constellation Software$6.62 Billion;
  2. Dye & Durham$474.81 Million;
  3. PEXA$279.84 Million;
  4. Clarivate$2.66 Billion;
  5. Upland Software$317.30 Million;
  6. FiscalNote$113.77 Million.

That is $10.4 Billion of revenue for 2022 and about 60% goes to Constellation. Bear in mind, that Constellation has a bunch of businesses under their umbrella. So pure legal is a considerably smaller piece of this revenue pie. PEXA deals in real estate legal transactions.

Other Legal Stuff

Most of the models above rely on catering to legal professionals. Hence, they have slow but steady growth. Now, newer models offer faster growth. To achieve fast growth, you need a faster deal flow. Faster deals happen when you can cut out the middlemen. Therefore, these models did not sell directly to the profession, they sold to the business. Marketplaces, Tax, Cookies, and e-Signature were designed as a direct-to-legal-consumer business. Their model used the ‘land & expand’ method for growth. Here’s a list:

  1. Docusign$2.11 Billion;
  2. LegalZoom$619.98 Million.

That’s $2.7 Billion. All of the above is $40+ Billion in revenue for 2022, and we would be generous to say it is all legal. All links go to Google Finance so you can check, and track it yourself. Most stocks are trading way below their initial public offering. Meaning, they are not doing as well as expected. Hopefully, by the time you read this, they’re doing better.

Will there be more? We’re tracking about, 1033 private companies. They have raised more than $20 Million in total from investors. In June 2022, Clio announced it had reached $100 Million in annual revenue. Based on our calculation, Clio is not the only ‘Centaur’. We see a couple doing Tax or Cookies that should be near $100 million. Nope, Contract Tech isn’t there yet. Unfortunately, their outlook is not rosy either. Check out Deals and Daybreak to see why.

After decades of ‘innovation’, and about $118.58 Billion invested, just seventeen joined the traditional three. In any case, the rest have three options: raise again, get acquired, or go public. They can stay private forever if they have profits. Profits come from growth and growth comes from Deals. So what about Deals?


We did not include a list of publicly traded law firms, Reg Tech, and Tax Tech. Most law firms are doing well. How well? Twitter’s M&A lawyers effectively charged $122K an hour. While this may seem excessive, it represents just 0.2% of the $44 billion price tag paid by Elon Musk. These deals rely on connections or an effective M&A prospecting tool. In Capital & Conflicts, we addressed dynamics that drive deals in legal. Those dynamics were driven by the demand for legal solutions. Now, we have a supply issue. Specifically, an oversupply of legal solutions, and here is the cause.

First, large language models (LLM) will break data monopolies. Meaning, that all data will eventually disappear in a LLM. Most hope for some kind of copyright or regulatory arrangement. Well, search engines ignore copyright and cookie policies basically broke the web. Note Sarah Silverman’s AI copyright infringement lawsuit against Meta stumbled. In general, monopolies do not work well for consumers. Moreover, the main LLM providers offer a copyright shield. So regulation will be futile. This brings us to our second and bigger concern.

We enjoyed cheap internet and cloud to distribute our solutions to customers. Now we’re getting these expensive chip clusters to run LLM’s. Before, we only needed to write code and distribute it. Copilots are different. The biggest shift is that generative pre-trained transformers (GPT) will intercept most end-user interactions. There are only three Interceptors currently capable: Google, Microsoft, and Apple. They have the chips, brains, and devices to capture all questions. And they will monetize it, too. Due to these additional costs, legal solutions become more expensive. This will further dampen demand and deals. This decrease in demand and increase in supply is a perfect storm.


In 2020, we wrote, “Will lawyers be replaced by GPT? Yes“. Wonder why Sam was fired from OpenAI? Read the second sentence in ‘Universe Three‘ from the analysis. Well, if we saw that coming, here’s what we see next:

  • Transformers will vaporize copyrighted data monopolies;
  • GenAI will fix most legal issues they intercept;
  • Interception will not come as cheap as code and cloud;

Curious, who is eagerly funding Interceptions? Investors made their VC Legal Copilot so they may not be allies. GenAI is the new printing press, and no legal tech company owns a printer. That is why we called the Casetext acquisition a bargain. To protect $6.6 Billion in revenue, a $650 Million investment (9.8%) is reasonable. Not realistic but reasonable.

What Are Legal Agents, And The Next Stages Of Legal Services

Image Courtesy: DALL·E 3 by OpenAI based on the content of this analysis.

Power: The Impact of Private Equity on The Legal Industry

Power: The Impact of Private Equity on The Legal Industry 1792 1024 Raymond Blyd

In the midst of this credit crunch, there is a candy store of capital called Private Equity. What does this mean for the legal industry?

TLDR: Venture Capital (VC) focuses on supporting early-stage or high-growth companies. Hence, a dip in growth means a drop in VC funding. To stay afloat, legal tech companies as well as law firms turn to private equity or debt funding. The catch? A shift in power from founders to private equity analysts.


Private Equity (PE) involves investing in mostly privately held companies, rather than publicly traded ones. PE firms raise capital from institutional investors, such as pension funds, endowments, and high-net-worth individuals. The goal is to generate significant returns over a specific period. In order to achieve this goal, a PE firm will often take control. They need to drive growth and increase the value of their investments. Obviously, who doesn’t, so what’s the worry?

Any copilot can explain what the PE model is. We offer you a specific calculation of why we wanted to know. Since 2020, we counted an uptick in companies raising through debt financing or private equity. In 2021, it drove us to discuss the difficulties of debt. The Q3 2023 post, elaborated on differences with venture fundraising.

What did the calculations reveal? As of January 2022, we tracked $1.5 Trillion in capital raised by investment firms for various reasons. In our June ’22 report, it was still just $50 Billion. Moving the slider above left will reveal how much money went to PE firms (66%). That translates to over a trillion raised in just 277 PE deals out of over 1,200 deals. How crazy is this stat? On average, this is $1.7 billion a day. This is not just power, this is concentrated power. Now, how does this power present itself?


All of the above made us wonder: In these difficult times, how can PE firms raise so much? They are able to offer better returns. How is this possible? Well, we have to explain the most peculiar trick these financial instruments employ: Leveraged Buyouts (LBO’s). In LBO’s, companies are acquired using a combination of equity capital and loans.

Imagine a private equity firm wants to acquire a company valued at $100 million. When a firm uses an LBO, it might use $30 million of its own capital and borrow $70 million to cover the total cost.

Now, let’s say the private equity firm improves the company, and a few years later, it’s able to sell it for $150 million. Subsequently, the firm repays the $70 million of borrowed money, and the remaining $80 million is their profit. Now, look at the return on their own invested capital: They invested $30 million and got back $80 million, which is almost 3x their investment.

Why would a PE firm not use its own capital to fund an acquisition? If the firm had used all its own capital to acquire the company (i.e., $100 million), a sale price of $150 million would still give them a profit of $50 million. However, the return on their invested capital would be 50%. In this case, the return on investment is just 0.5x.

By using debt, the private equity firm magnified its return on invested capital, even though the company’s total value increased by the same amount in both scenarios. This is why leveraged buyouts are more profitable.

Still confused? We left out the key part of this strategy: The debt acquired to purchase the company is then placed on the company’s balance sheet. This makes the company responsible for repaying the $70 million loan. The private equity firm’s capital remains mostly intact and can be used for other investments or acquisitions. Isn’t that sweet.


Let’s recap: If companies need outside capital to operate, there is a giant pile of cash called Private Equity. Specifically, getting access to it requires giving up control. To ensure PE firms get their return, they will exert pressure to increase the value of the company. When there is little growth, the value can only increase by raising prices and cutting costs. The fastest shortcut is cutting staff. The hardest staff to cut is sales and core operations. Everyone else is expendable, including founders. If you have been following legal tech, you have seen this play out in several companies.

Perhaps you are wondering: Who’s the sugar daddy? In order to find out, we had to rebuild parts of our datasets. SQL gymnastic aside, complicating matters is that one company can have a mixed bag of grants, VC, and PE capital. Here’s what we found: 951 investors in 281 deals in legal tech and law firms. Surprisingly, neither Insight Partners nor HG but K1 is the most active Private Equity investor in Legal, according to Spark Match. Better yet, if ranked by participation, TA Associates bubbles to the top.

Sticking to our policy on private companies, we don’t name them in our analysis. However, we did share that public companies, like Legalzoom, hold the largest loans. Note that law firms in the UK can be listed as public companies. Law firms taking loans isn’t new, yet we also found a few that raise venture capital. Previously, we only registered tech companies having access to VCs. A sad side effect: These nuances are causing us to lose friends. These numbers don’t fit their narratives. Especially, narratives indirectly made possible by…private equity.

In closing: Our worry isn’t just pressure on the bottom line, as stated in our previous post. It is the fact that founders of legal ventures are losing control. We are at an inflection point in the history of the legal industry. For over 5,000 years, legal counsels have been the engineers of a safe society. This is the worst time for legal to lose their independence.

The way to wrestle back control is to know. Know where to get capital to survive. Know where to find customers to thrive. Schedule a chat.

**updated Oct 27, 2023 – 15:01: LBO equity checks top 50% for the first time, thanks to high interest rates – Axios**

Editor’s note: The analysis uses Legalcomplex Spark data to artificially generate parts of it.
Image Courtesy: DALL·E 3 by OpenAI based on the content of this analysis.
Copyright notice: Your © guess is as good as mine.

Why Worry? Five Insights from Legal Funding and M&A Activity after Q3 2023

Why Worry? Five Insights from Legal Funding and M&A Activity after Q3 2023 2560 1494 Raymond Blyd

What were the Q3 2023 results, and what is the current market climate? How is AI impacting the Legal Tech industry?

TLDR: Legal Funding in Q3 2023 is normalizing, with Europe surpassing North America in capital raised for Legal Tech. AI’s impact is more evident in consumer-focused companies.

Housekeeping: we skipped our September analysis, but we had a good reason. That is why you’re getting a double shot this October. We’re also generating our analysis with GenAI. Basically, we’re feeding the machine our stats and teaching it our style.

Ok, curious about this year’s Legal Funding and M&A activity compared to 2022? Did you see our input on Law360’s analysis? Then let’s dive right in.

Legal Funding Q3 2023

1 Legal Funding for Q3 2023 stood at $590 Million, a 26% decrease. Deals numbered 85, marking a 11% drop. Interestingly, the number of investors plummeted by 45% to 145. These numbers do not include Governance, Risk, and Compliance (GRC). If we did include GRC, the total would be $4.8 Billion, and it would still be down 26% from last year.

Now, what’s Legalzoom doing up there? Aren’t they a publicly traded company? Yes, but they did raise privately. Just like law firms are also raising capital on private markets. Since this train has been picking up steam, we are keeping track. Why? Keep reading…

Debt vs. Venture Funding

2 If we ignore 2021 and 2022, the 2023 funding numbers look in line with expectations. However, if we look at the types of funding, we see a pattern emerging. Debt funding for the quarter was $243 Million, 41% of the total. Over the past year, debt funding reached $2.18 Billion (image above). Venture funding was $1.52 Billion in the last 365 days, but is significantly less by comparison. Remember, we discussed the burden of debt all the way back in 2021. That is the year we registered a record $2.6 Billion of debt raised by legal ventures.

What does Debt tell us?

3 It’s essential to clarify our funding classifications. Anything not labeled as venture or seed is considered debt. This includes equity financing, which technically isn’t debt. Why? To determine two scenarios: a company’s stage and a market’s climate. These metrics tell us: are we early, thriving, or merely surviving?

  • Seed and Early-stage Funding: Companies lack a product-market fit. It’s too early to judge them because they have yet to bloom.
  • Series and Venture Funding: These companies have revenue, yet they burn capital. Therefore, companies need to raise for rapid growth, indicating a thriving stage.
  • Private Equity or Debt: These companies need cash and can’t secure venture funding. They go into survival mode when growth stalls.

Except for grants, the financing options mentioned involve borrowing against the future. There is no such thing as free money. Nonetheless, Seed and Series funding are luxuries, while Debt is a necessity.

Exits & Geography

4 Exits totaled $5.39 Billion, down by a whopping 68%. Notably, Magnet Forensics led the exit deals, not CaseText. The number of exit deals was 76, a 30% decrease.

If we looked at where capital was raised, Europe, including Great Britain, outshone North America. EU plus GB raised $1.16 Billion in 2023, while North America secured $1.01 Billion. We can not recall this ever happening.

Areas of Growth

5 Excluding Tax, the top categories that raised capital were: Practice Management (30%), Contracts (13%), and Claims (13%). AI did not necessarily turn a company into a desirable object for investors. To the contrary, we have yet to figure out the true costs of copilots. AI’s influence is in creating new products with a much broader reach. That’s what we discovered after capturing 81 new companies in just 28 days. In total, we added 1549 companies plus 18 new cities in a 333-day timespan on As explained at the 4th annual Global Legal Forum, these companies were not all founded recently.

What are the Patterns?

In case you missed our inaugural event, these were some of the revelations. To recap:

  • Legal funding is back to realistic levels;
  • Better yet, early stage companies have access to more capital;
  • The worry is revenue growth is stalling, and it has been since 2021;
  • By creating new products, Legal may find new revenue.

Listen to The Equity pod where Crunchbase explains the word: realistic (hint: context is everything). If you did listen to the pod, you’ll hear some deviating results. For example, that seed funding is down. In the context of legal startups, the opposite is true. This historic fact, we revealed to the University of Leiden 11 months ago. Contrary to later stages, young companies are yet “too early to judge”.

Who is being judged? Stalling growth is currently most noticeable with Legal Tech stocks. Remember, the legal numbers above do not include GRC. Which may explain the legal tech stocks outliers (again: context). In short, the legal space is more nuanced than others would like you to believe. So what are others saying?

  • 25% increase in caseload due to better Practice Management by Clio;
  • 64% of in-house legal counsel haven’t used AI for legal work by;
  • Wheels Of Justice Slow To Accept Legal Tech As Funding Falls by Crunchbase;
  • The Q3 venture capital market explained in five charts by Techcrunch;
  • Global Venture Funding In Q3 2023 Falls Again Despite Late-Stage Rebound Led By Huge AI Deals by Crunchbase;
  • VC continues to stumble through 2023 by PitchBook.

In short, we do our own homework. If you value context around numbers. If you need to know, what it all means for your area of operations. Schedule a chat.

Note: This analysis is partially artificially generated using numbers and calculations captured by Legalcomplex Spark.

From Rejection to Resilience: Five Insights Turning ‘No’ into ‘Next’

From Rejection to Resilience: Five Insights Turning ‘No’ into ‘Next’ 1211 739 Raymond Blyd

I wrote these down to build courage and confidence. Here are five insights on how I try to transform rejection into traction.

1. Rejection is Feedback, Not Failure

Every “no” you encounter offers a chance to refine your approach. However, it’s also essential to remember that sometimes you might be right, and the rejection might be wrong. Instead of solely viewing rejection as a failure, see it as feedback. Perhaps your pitch didn’t resonate with a particular audience, or maybe your product needs adjustments. Embrace rejection as an opportunity to learn, but also trust your instincts and the value of your proposition.

2. Persistence is a Superpower

Some of the most iconic entrepreneurs faced a barrage of rejections before their ideas found their footing. Howard Schultz, the visionary behind Starbucks, was rebuffed by banks 242 times. Prepare to receive refusals from both capitalists and customers. The entrepreneurial path is rife with highs and lows. Patience and persistence are inseparable allies. Each rejection is a stepping stone, underscoring that success often demands both time and tenacity.

3. Diversify Your Approach

If one avenue seems blocked, it’s time to explore another. Build a targeted investors list that hasn’t invested in your market but understands your business and aligns with your personality. This not only elevates your chances of finding a receptive audience but also enriches your understanding of your product’s versatility. Moreover, having alternative financing options, such as debt or grants, can build resilience, ensuring you’re not solely reliant on one funding avenue.

4. Emotional Resilience

As an entrepreneur, safeguarding your emotional well-being is paramount. While rejections can be disheartening, it’s vital to disentangle personal feelings from business outcomes. Empathy, understanding others’ perspectives, and seeking mentorship can be invaluable. Remember, rejection doesn’t diminish your worth; it often signals that certain elements need recalibration. And while these challenges can be daunting, it’s essential to maintain perspective; these are challenges of privilege and not matters of life and death.

5. Celebrate the Opportunity

Amidst rejection, it’s tempting to overlook the very opportunity to pitch. Regardless of its outcome, every pitch, is a chance to share your vision. Celebrate these moments as wins, for they signify progress and the continuous journey of entrepreneurship.

In conclusion, rejection, while discouraging, is an integral facet of the entrepreneurial journey. By confronting it with patience, understanding, and a proactive mindset, entrepreneurs can metamorphose these setbacks into stepping stones. As the adage goes, “Success is not final, failure is not fatal: It is the courage to continue that counts.”

I’m curious: how do you overcome rejection? Happy to hear your thoughts.

Cockpit: The Battle for Copilot Supremacy in Legal

Cockpit: The Battle for Copilot Supremacy in Legal 570 344 Raymond Blyd

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:

  • ChatGPT;
  • Voxscript;
  • Webpilot;
  • Pluginpedia.
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