When assessing potential investments, private market investors usually consider a combination of the following factors:

Not all criteria matter the same and the weighting depends a lot on the stage of the target company. Yet, based on my own investment experience and talking to various industry veterans over the years, there seem to be two primary camps: the founder first versus the market first investors.

“A-founders can build big companies in B-markets, but B-founders can’t in A-markets” versus “If the tide is rising, it lifts all boats. No matter if you’ve backed an A-founder or a B-founder, if they’re in the right market, they’ll build huge companies”. Two quotes, two perspectives.

No matter what camp you fall in, it’s a sign that these two criteria are top of mind for investors, making them constantly search for top tier founders and attractive markets.

For me, having focused the past decade almost exclusively on AI/ML - from an operator, research, and investor perspective - I’m most excited about meeting the best talent across the AI stack, but also thinking about which markets might be disrupted (or just evolve) by AI.

I write a lot about these topics here and via LinkedIn, and one of my recent posts sparked a controversial discussion:

Today, I want to extend this post and tell you why I believe consulting as we know it is dead and why it creates a massive opportunity for AI-native firms.

A blueprint for disrupting traditional markets.

First off, let’s clarify what an attractive market is. As investors, we see two kind of markets:

Clearly, the consulting market falls into the first group of existing markets. Blending different sources, the market is roughly $1tn in size and is expected to grow around 2-5% p.a. The market is dominated by giants such as McKinsey, BCG, Bain, and a few others like the big 4, but also has a longtail of specialized firms, either with industry, tech, or geo focus.

While all consulting firms advise their clients how to leverage technology to achieve better outcomes, no matter if that means growing the topline or cutting costs, few of them leverage tech themselves to change the way how they deliver their services.

Sounds like a suitable opportunity for disruption, no?

Reuters wrote a thought-provoking article on exactly this topic last week. They argue that global consulting firms face a “Kodak moment” as AI threatens to upend their traditional, labor-intensive business model.

With AI enabling competitors or even the clients themselves to perform tasks at a fraction of the cost, incumbents are losing pricing power and margin strength. The piece warns that unless firms rapidly adopt AI and reinvent delivery models, their valuations could collapse much like Kodak’s during the digital shift.

Looking at their valuations, it’s actually not a hypothetical future scenario but already a bitter reality. In the past 12 months, most consulting firms have lost between 20-50% of their market cap.

So why are these companies actually losing market cap? Are they really threatened and at risk to survive?

I wrote before about “The impact of AI on the cost of starting and running a business”. Key takeaway:

“Clearly, companies will get more done with less resources. This will have two implications. Firstly, considering a fixed output (users, revenue, growth etc.), the input and required resources can be reduced. This would mean less hiring, less replacements and more lay-offs to reduce the workforce. Secondly, considering a fixed input (workforce), the output can be increased. In this scenario, it’s questionable whether there will be natural limits to growth. In any case, both scenarios mean more efficient operations.”

Applying these two scenarios to the consulting industry, we quickly find that it’s an established industry growing at 2-5% p.a. On an industry scale, it seems difficult to increase the output (number of projects multiplied by revenue per project = collective industry revenue) significantly, and as a result, productivity gains will be translated in decreasing the input (or at least being able to).

Said differently, the industry can at best continue to grow their revenues moderately (=output fix), yet is able to reduce the cost to achieve it (=input down). As consulting firms increasingly leverage AI (see the nice token of appreciation from OpenAI for McKinsey above), reducing the input practically means enabling less employees to achieve the same outcome, which in turn means less consultants for the same project outcomes and more projects handled per consultant.

The “downsizing numbers” from the chart above hide one important detail: most positions impacted by layoffs are junior consultants. Uncountable comments below my LI post described the obvious reason in different words: AI is already able to generate “reports and pretty slides” - the part of the consulting job that’s mostly done by juniors.

Parting ways with employees is always difficult and nobody ever wants to be in the position being required to decide or execute it. If necessary, however, it’s in most cases easier to let go junior people who might’ve been with the firm for few months or years compared to seniors and peers who’ve been with the firm for decades.

And here comes the structural problem:

So in a nutshell, AI increases productivity which allows firms to achieve the same outcome/revenues with less input/employees. While in the beginning it’s sufficient to let go junior people, the relationship between seniority and salary will soon require these firms to let go senior people to cut relevant costs and stay competitive from a financial margin standpoint. As firms are too top-heavy and have no mechanisms for large-scale senior layoffs in place, it might - if they won’t be able to transform themselves otherwise - be a slow but gradual end for many firms.

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While incumbent firms are slowly adopting the new technology and debating the consequences for their workforce, like if and whom to let go, a new breed of tech-enabled consulting firms are rising.

These firms start on a white sheet of paper and design their teams in an AI-native environment. They hire significantly fewer people but quickly compete with incumbent firms for the same budgets. The tech-enabled firms cannot only undercut the prices (if needed) but also ensure faster delivery and more customization.

First examples include German firm “Strategy Frame” that handle 100+ SMB clients with 3 employees (and made big headlines on their AI-first approach across national media earlier this year), Morning Brew founder Alex Lieberman’s new AI-native consulting firm Tenex, or my friend Georgiy’s new AI-automation firm Overdrive (who support me with lots of stuff here at DDVC too). The list goes on and the new breed is growing - leaner than any of their incumbents will likely ever become.

The opportunity is now as legacy firms struggle to transform and the field is wide open for new AI-native entrants. Surely, some established firms - specifically the ones with top tier brands or hyper niche experience - will be able to transform themselves, but for the majority of generalist incumbents it’ll be a tough fight.

“While all consulting firms advise their clients how to leverage technology to achieve better outcomes, no matter if that means growing the topline or cutting costs, few of them leverage tech themselves to change the way how they deliver their services”

“Most consulting firms are run as partnerships and few/none have mechanisms in place to part ways from partners when needing to cut costs”

Working at the cutting-edge of tech but not using tech themselves + structured as partnerships… sounds familiar? Fairly enough, Aaron commented:

Indeed there are lots of structural similarities between consulting and VC - but also one key difference: Consulting firms charge for output, either by the hour or for result delivered. VC firms in contrast charge (=management fee and carry) for investment performance and as we all know, VC return distributions follow a Power Law where input and output are disconnected.

Said differently, top VC investors need to consistently pick and invest in outlier companies. Theoretically, they could just work a few days per year and invest in one outlier company that drives the superior performance of their fund. While this is obviously unrealistic, it helps me make the point that AI and its efficiency gains are ultimately less relevant for VCs as output and input are rather disconnected.

Instead, it’s more about effectiveness and some key aspects that are harder to automate such as deal access (via brand, reputation, etc.) and human relationships (founders want to talk to people when shit hits the fan).

Those who’ve been reading this newsletter for long know how much I push the use of AI for VC to drive efficiency, effectiveness, and inclusiveness, but I also came to the conclusion that for early-stage VC, there will be a lot of human, hard-to-automate parts remaining. And yes, that’s different for later-stage growth or PE, but that’s a topic for another episode.

Excited to hear your thoughts :)

Stay driven,
Andre

PS: Take the headline with a grain of salt and make sure to test Kruncher AI ;)