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“From the quality of the content to the caliber of colleagues in legal marketing and business development, this is one of the standout events I look forward to every year.” 鈥� Ian Ribald, Chief Business Development & Marketing Officer, Willkie Farr & Gallagher LLP

“2024 was my first time at the CMBDO Forum鈥攁nd as we wrapped up, I knew it wouldn鈥檛 be my last. Connecting with peers, sharing real stories, and learning from their experiences was invaluable.” 鈥� Oliviana Mingarelli, Head of Marketing and Business Development, Fasken Martineau DuMoulin LLP

In the previous installment of this three-part series analyzing the recently released Am Law 200, we asked if the Am Law 200 is even a useful way to categorize law firms for purposes of strategy or even simple benchmarking. Spoiler alert: We concluded it was not.

The key empirical pillar upon which we rested that conclusion was the dramatic dispersion within the five quintiles of law firms in the Am Law First 100 among the ratios of their i) gross revenue to ii) lawyer headcount to iii) net operating income.

In a nutshell, the data showed that the top 20 law firms 鈥� the first quintile 鈥� were in the enviable position of achieving a greater share of net operating income than they had in revenue, and a greater share of revenue than that of lawyers. In other words, firms in this fortunate cohort (on average) were remarkably effective 鈥� more so by far than the other 80 firms 鈥� at converting lawyers into revenue and then revenue into profits. With apologies to the Gershwins, it鈥檚 .

At the end our last installment we promised to take a 10-year look back at the Am Law 100 and see if that pattern held. Well, we have done that analysis, and the answer is not exactly.

To be specific, the positions of the five quintiles in terms of their relative shares of the above-mentioned three key data series has migrated noticeably over the past decade. The 2020 pattern may be the logical evolution of what has gone before (and we believe it is), but it has indisputably been an evolution and not a steady state of affairs.

First, the numerical and graphical views into the numbers, and then, our thoughts on what might be going on underneath the covers.

Numerically^[*] the big news on all three bakeoffs 鈥� revenue vs. number of lawyers, net operating income (NOI) vs. revenue, and NOI vs. number of lawyers 鈥� is the surprising consistency across the five quintiles. Generalizing across the decade:

• • • The 1^st quintile performs the best by far, rising consistently on all measures;
    • The 2^nd quintile started strong but deteriorated steadily from there;
    • The 3^rd and the 5^th quintiles are very stable, and both show no real deviation from the 100-firm average; and
    • And the 4^th quintile convincingly finishes at the rear of the pack, showing the worst performance of all 鈥� starting in last place among the five quintiles and losing even more ground from there.

If you have digested that brief exercise in narrative numeracy, let鈥檚 now look at some pictures:

Now that we can visualize the big picture, here鈥檚 what we see happening at the macro level:

• • • The 1^st and 2^nd quintiles have traded places: At the beginning, the 2^nd strongly outperformed the 1^st on two of the three metrics and matched it on the third. Today, the 1^st appears almost insurmountably in the lead, especially on the two more purely financial series pairings 鈥� generating more NOI from revenue, and more NOI per lawyer.
    • The 3^rd and 5^th quintiles are living steady, relatively unexciting lives. They鈥檙e not taking gold or silver, but they鈥檙e very rarely at risk of bringing up the rear.
    • The Sad Cousin award then goes to the 4^th quintile, hands-down.

So, what do we make of all this?

First and foremost, no one among the 4^th quintile firms is suffering in the least. (Indeed, their relative position may be what whomever coined the term had in mind.) That one quintile or another would bring up the rear was effectively built into the techniques we used to analyze the data. They were designed to force-rank winners and losers.

Reflect for only a moment

For each of the 10 years in our sample, the Am Law 100 firms together generated a finite and defined amount of revenue and NOI using a finite and defined number of lawyers. If one cohort is up, another has to be down. This is truly a zero-sum analysis.

And if no one firm in the 4^th quintile is suffering 鈥� a self-evident truism if ever there were one since most Americans would trade their current station for a position in any of the 4^th quintile firms in a heartbeat 鈥� then, a fortiori, life in the 3^rd and 5^th quintiles is also splendid.

To us, the fascinating pattern we discovered when we turned over this rock was the 1^st and the 2^nd swapping places. To generate a hypothesis that might explain that, we took a detailed look at exactly which firms were in those two quintiles (yes, by name) over the years in question. Wisely or rashly, here鈥檚 what we think may have been happening.

What if the caliber of lawyers and of firms that make up the 1st and 2nd quintiles has slowly been preferentially migrating, in quality, elite reputation, and prestige, to the 1^st quintile? In other words, our impressionistic view is that there were some extraordinarily high-caliber firms in both quintiles 10 years ago, but now the 1st quintile is almost embarrassingly top-heavy on high-prestige firms and the 2nd has lost several through up-market (into the 1st) and down-market (out of the 2nd) moves.

Definitively proving or disproving that would require a minute analysis of all of (or a very large sample of) lateral partner moves, client defections and acquisitions, positive or negative media mentions, and perhaps more, across all those law firms for the past decade. If some intrepid forensic data analyst out there was casting about for their next project, be our guest.

In the meantime, consider this possible theoretical explanation: If labor markets are efficient 鈥� and there鈥檚 no reason to think the market for talented lawyers suffers systemic informational asymmetries or other modalities of market failure 鈥� then the past decade has seen the rational and efficient sorting of Big Law Firm partner talent from platforms that may be suboptimal towards platforms that are a better fit.

Understand that this should 鈥� and if our theory has legs, surely has 鈥� worked both ways. A Big Law partner can find themselves at the wrong firm because their skillsets and ambitions cannot find adequate expression and running room where they are or because they鈥檙e in highly rarefied air 鈥� too rarefied for their comfort.

Those who read us frequently know that the ever-upward spiral of lateral partner moves in the legal industry has left us a bit unconvinced, but what we may be seeing here in the swap between 1^st and 2^nd quintiles since 2011 implies it鈥檚 not all churn. There might be an efficient labor market out there after all.

Final thoughts

We cannot resist offering a thought in closing by way of, 鈥淲hat on earth do you think I should actually do with this?鈥�

The literature on leadership speaks with one voice on little; however, there is near-universal agreement that one prerequisite for strong leadership is knowing yourself. So, know your firm. The vast majority of readers will not be in the 4^th or the 1^st quintiles. But it doesn鈥檛 really matter where your firm is: Remember, even the 1^st and 4^th quintile patterns we鈥檝e been discussing are the averages for those quintiles. Individual firms differ.

So, forget where you are, and calculate how your own firm鈥檚 gross revenue, NOI, and lawyer headcount stack up against some plausible, publicly available benchmark data.

If you discover your firm looks like a 1^st or like a 4^th quintile firm, you have learned quite a bit already. Think of these patterns as packed full of information.

Which pattern your firm more closely lines up with will help determine, among other things, what talent you can recruit, and what degree of difficulty, in terms of client matters, for which your firm can compete. If you like where you are, reinforce the strategic, cultural, and economic conditions and aspirations that got you there 鈥� that鈥檚 easy (as if anything in leading a law firm were easy).

If you don鈥檛 like the pattern your performance resembles, you have one simple and one complex task ahead of you. The simple one: Figure out what strategic and tactical adjustments would help your firm pass advance to the level you envision; and the complex one: Persuade your partners to undertake these adjustments, and engineer the cultural shifts that would be required to make it stick.

Now, aren鈥檛 you glad you asked what to do with this?

[*] Methodological note: The data we focused on calculated each quintile鈥檚 performance relative to parity with the Am Law 100 average across each of the pairs of data series. In other words, matching the Am Law 100 average produced a score +/- 0.00%. A quintile that outperformed the Am Law 100 across the two data series under analysis would generate a positive number and an underperforming quintile a negative number. We deemed: i) more revenue from fewer lawyers; ii) more net operating income from fewer lawyers; and iii) more net operating income from less revenue all to be good things and therefore scored them as positive variances. The opposite outcome produced negative scores.

In our reflections on the release of the 2020 Am Law 200 data, we began with our basic instinct to think in terms of averages. In summation, we said: Don鈥檛 do it.

Now, we will challenge a different premise: That the Am Law 200 is a useful category.

鈥�category, n., a class or division of people or things regarded as having particular shared characteristics.鈥�

In the most trivial sense, of course the Am Law 200 is a category, in that its members are the i) 200; ii) U.S.-based; iii) law firms; that are iv) ranked highest in terms of 2019 gross revenue. And the greatest share by far of reporting and commentary on the Am Law firms is premised on looking at the 200 as a whole (or the Top 100, or the second 100). But for analytical purposes, are those useful categories? We think not.

To begin to explain why we say this, let鈥檚 step outside of the Law Land bubble and look at another very familiar gross-revenue based firm ranking, the Fortune 500. Here are the most recent Fortune 10 companies: Walmart, Amazon, Exxon Mobil, Apple, CVS Health, Berkshire Hathaway, UnitedHealth Group, McKesson, AT&T, and Amerisource Bergen.

Now answer this: What 鈥� the tautology of revenue aside 鈥� do these firms have in common? You might counter that Walmart and Amazon are both retailers, but could their strategies and their business, revenue, and operating models possibly be more different? (Half of Amazon鈥檚 profits come from their cloud-computing platform, not retail sales.) And let鈥檚 not even start with Exxon vs. Apple.

Managing and leading one organization that brings in $1.5 billion or more is a lot more similar to leading another firm of similar scale than it is to leading a firm that鈥檚 1/10th of that size…

Here鈥檚 an even more striking thought: Coincidentally, a brand-new lineup of Ford鈥檚 F-Series pickup trucks is launching this summer, the first complete revamp in six years. Auto junkies out there know that the F-Series has been the best-selling vehicle in the U.S. by far since, well鈥� forever. How big a deal is it for Ford to get this launch right?

• • • The F-series generated about $42 billion in revenue last year, not far behind the iPhone鈥檚 $55 billion.
    • That was more revenue than the four major professional sports leagues (MLB, NHL, NFL, and NBA) combined.
    • As a standalone company, the F-series would be solidly in the Fortune 100, ahead of companies like McDonalds, Nike, Coca-Cola, and Starbucks.

Why are we talking about a pickup truck in an article on the Am Law 200? Simply to make the point that when you see a 鈥渃lass or division of things鈥� ranked by revenue you need to begin probing a lot harder.

Shall we perform a similar dissection of the Am Law? Here are the top 20 Am Law firms: Kirkland & Ellis; Latham & Watkins; DLA Piper; Baker McKenzie; Skadden Arps; Sidley Austin; Morgan Lewis; Hogan Lovells; White & Case; Jones Day; Gibson Dunn; Norton Rose; Ropes & Gray; Greenberg Traurig; Simpson Thacher; Weil Gotshal; Mayer Brown; Sullivan & Cromwell; Davis Polk; and Paul Weiss.

Are all 20 of these firms plausible substitutes for one another in the eyes of clients? For every kind of matter, no matter how high-profile? How low-profile? If not, to what extent are all 20 really competing head to head? And if they鈥檙e not (really) competitors, to what extent does it make sense to lump them all into the same market or into the same category?

Size matters

That said, managing and leading one organization that brings in $1.5 billion or more is a lot more similar to leading another firm of similar scale than it is to leading a firm that鈥檚 1/10th of that size (See the Am Law #170 firm, for example, with annual gross revenue of $150 million).

This isn鈥檛 just our opinion.

When we started looking at the 2020 data more closely, an odd anomaly emerged. It became apparent when we zeroed in on the Am Law 100 by quintile, and specifically at the distribution of i) gross revenue; ii) net operating income (NOI); and iii) lawyer headcount in each quintile. (In our first installment in this series, we sliced the full list of 200 by decile, so put that out of your head for a moment.) We selected these three data series, out of all the series the Am Law 200 provides, both because all are very important metrics for a law firm and, candidly, because they are hard for firms to cosmetically adjust.

To be specific, for the 20 firms in each quintile we compared what percentage share that quintile represented for the Am Law 100 totals for each of the three data series. So, for example, the formula for the top quintile鈥檚 share of total revenue: (Top 20 share) = (Sum of Top 20 firm鈥檚 revenue) 梅 (Total revenue of the Am Law 100).

Here鈥檚 the odd pattern we found:

• • • For the top quintile, the share of total NOI across all 100 firms > the share of revenue > the share of lawyers.
    • And the fourth quintile is the exact opposite: Its share of NOI < its share of revenue < its share of lawyers.
    • (The second quintile is like the first but less so, the fifth like the fourth, but less so, and the third comes in pretty much even-steven on all three measures. We鈥檒l address this in a minute.)

Graphically, it looks like this:

All else being equal, one would expect the shares of each data series across each quintile to be the same. Your lawyer headcount should correlate with your revenue, which should correlate with your NOI.

The fact that the first group of firms is so anomalous requires some plausible explanation.

Our hypothesis is this: The business models of the top 20% of firms generate a disproportionate share of revenue from fees based on bonuses, the investment-banking pricing model (percentage of deal value), success premiums, and so forth, rather than from the plain old billable hour, compared to the other quintiles. Despite the differences among those top 20 firms in prestige, geographic footprint, practice mix, historic path, and all the rest, they are without question extraordinarily capable organizations that can bring massive resources to bear on any legal matter 鈥� fast and at global scale.

You can critique law firms鈥� blind adherence to conformity and rejection of even the most incremental change all you want 鈥� and we do 鈥� but this data points to a truly impressive capability. When that鈥檚 what a client needs, a 鈥減remium鈥� may be rather beside the point.

In the meantime, do you still think the Am Law 200 is a meaningful analytic category?

In our third and final installment of this series, we will go back to unearth the past decade鈥檚 historical record of the Am Law data and see whether this phenomenon existed in the past and whether it has changed over the years. Stay tuned.

鈥淒on鈥檛 think of elephants,鈥� runs the childhood taunt, with the immediate effect that elephants are the only thing you can think about.

At the risk of defeating our own efforts before we start, then, if we had to reduce our guidance on the 2020 Am Law 200 list to one phrase, it would be, 鈥淒on鈥檛 think of averages.鈥�

Why not averages? The word itself (we checked) appears 14 times in the June 2020 issue of The American Lawyer, which published this year鈥檚 complete Am Law 200 listing. And being presented with a list or ranking seems to call forth in those of an analytic bent the irresistible impulse to start asking about averages. We鈥檙e here to tell you that would be mistake of the first order when looking at the Am Law 200.

Why? Primarily because averages can be a helpful and informative component of generating a summary or overview of data distributed over a standard or normal or bell curve. However 鈥� and this is the key 鈥� the Am Law 200 represents data distributed over a power curve. With this type of distribution, averages don鈥檛 just mislead; at times, they may in fact lie.

What鈥檚 the difference?

Here is a bell curve we drew in Excel:

Looks familiar. Now check out the power curve:

Rather than us asserting this, permit us to show you.

Three of the key data series in the Am Law numbers are i) gross revenue; ii) total profits (known as net operating income); and iii) lawyer headcount. Here鈥檚 what each of those series looks like by deciles 鈥� the 200 firms in 10 groups of 20 firms each:

All three charts, we submit, tell essentially the same story: Beginning at the top of the pie charts and moving clockwise, you can see that the first two deciles account for more than half (about 53% on average) of the entire 200, and the bottom four deciles account for roughly 10%. Another way of expressing the same point 鈥� and to see how strongly skewed this distribution is 鈥� is that the top five firms generated nearly as much revenue ($16.6 billion) as the bottom 90 firms ($17.1 billion).

All very interesting, of course, but how does that make a point about averages? The American Lawyer reported that 鈥渁verage revenue and profit growth for the Am Law 200 were both 5% last year.鈥� Fair enough. One鈥檚 mind inevitably jumps to the presumption that the vast majority of the 200 firms therefore grew pretty close to that 5% rate in revenue and profits. But there are a host of other ways of generating a 5% average for those critical and high-profile data series that would reflect no such reality.

For example, here are a few other ways to end up with that 5%:

• • • The top 10% of firms each grew 10% and the other 180 firms grew 1.5%
    • The top 20% of firms each grew 9% and the other 160 experienced zero growth.
    • The top 100 firms each grew 17% and all the other firms went out of business鈥攁nd were not replaced at all in the Am Law 200.

Obviously these three scenarios 鈥� granted, some more surreal than others 鈥� describe quite incongruous states of the world. But all dovetail perfectly with a 鈥�5% average.鈥�

What鈥檚 the moral?

In analyzing power curves, you have to toss out the familiar Stats 101 playbook and think harder. One should ask, 鈥淎re there meaningful and informative generalizations one can draw about this dataset of firms?鈥� (Don鈥檛 assume the answer has to be yes; maybe it鈥檚 mostly noise with only a very weak and tenuous signal.)

Other questions include, 鈥淲hat am I really trying to figure out?鈥� or 鈥淚f doing a straight-up comparison of revenue, net operating income, or lawyer headcount is not actually revealing, what would be?鈥�

鈥淒o I need to compare firms within subsets and not across the entire 200?鈥� 鈥淲hat classification mechanism would be helpful in defining the borders of those subsets?鈥� And most importantly: 鈥淲hat information (if I could derive it) would actually change the way I manage and behave?鈥�

One of our core beliefs is that data is almost always trying to tell a story, and our job is to figure out what that narrative is.

Coming up, we鈥檒l suggest some of our own hypotheses about that story, and in the process ask you to question whether the Am Law 200 鈥� or Am Law 100 or Second Hundred, for that matter 鈥� are even useful categories at all.

Meanwhile, get the elephants out of your brain.