Do more!

There was a saying at Goldman Sachs that kept us focused:

There's no such thing as small deals. Just small fees.

It wasn’t arrogance. It's cold logic. Executing an M&A or financing transaction requires a team to do a ton of work: the industry overview, the merger math, the comps, hundreds of analyses. And that team costs the same whether the fee is $5M or $20M.

So we set hard floors depending on the environment and our resources. Half a billion dollar deal-size minimums, and in good times, higher. An entire tier of the market sat below it. Not because Goldman lacked the capability or desire. Because the economics didn't work.

Until now.

I'm going to use banking as my case study here because it's the world I know best, specifically M&A, one product among many, but a visible and instructive one. But the thinking applies across every industry. I've spent the last several months in conversations with a fitness founder, the Board of a major consulting firm, the C-suite of an airline, and senior partners at multiple law firms, and the same pattern keeps showing up. But first, the Goldman story.

Start with the core question: why do companies hire Goldman Sachs for M&A?

Not because Goldman executes mergers better than anyone else. The definitely have experts, but so do other top-tier firms. If you have a great business and want to run an effective sell-side process, you have options.

What Goldman actually sells is market insight, creative problem solving, and trusted networks. The ability to identify buyers, back-channel through a powerful web of relationships built over decades, navigate complexity, and bring the right people to the table. That is the kernel, the true differentiating asset. Everything else, the processing, the formatting, the analysis, is important and helpful, but it isn't what wins the big deals and generates the premium fees.

For decades, Goldman needed junior people to do that processing. Analysts and Associates spending the bulk of their hours building models, running industry overviews, formatting materials. Necessary work. Not the work that wins the business.

And here's where it gets interesting.

AI can now do much of that processing. If it can't do all of it today, it will tomorrow. Applied correctly, junior people are empowered to level up. Senior people gain considerably more leverage and capacity to apply their expertise more broadly. And the fixed cost that made smaller deals uneconomical has largely disappeared.

Goldman can go downstream. $300M deals, $100M deals, $50M deals. They can considerably expand their addressable market, not by hiring more senior bankers, but by redeploying the people they already have toward the work that actually matters.

The companies that get this right will stop using junior talent as processors and start deploying them as thinkers and connectors. AI gives them the leverage to become conductors rather than musicians, orchestrating work rather than just executing tasks. They spend their time working closely with senior bankers, absorbing the wisdom, building their own pattern recognition. A return to the true apprenticeship model that builds culture and durable franchises.

Here's the part that gets overlooked - younger companies have younger talent but thanks to AI and tech innovation broadly, they rapidly become meaningful players. Their founders and board members aren't in the Goldman MD's contacts. They're in the Analyst’s and Associates LinkedIn networks, same clubs, same graduating classes, same circles. The juniors can become front and center with the solid foundation, air-cover, and support of the senior team.

Junior hires still need to learn the product and understand the trade deeply enough to earn a seat at the table. But that knowledge is now the means, not the end. The end is what you do in the room once you're in it. The organizations that build this culture early, that reward creative thinking, problem solving, and relationship development the way previous generations rewarded modeling speed and PowerPoint fluency, are making a long-term bet that will compound.

Enable them. Elevate them. That is the move.

The Goldman example is one application of a principle that applies to any company sitting with an AI-driven efficiency gain right now. Here’s a simplified C-suite framework that can be applied across industries:

I had breakfast recently with the founder of a well-known fitness brand. Real scale, real distribution, a household name. He's stuck. AI-generated content is flooding his market, and he's staring at the ceiling, wondering what his moat is.

I asked him: What's your vision? Not what's your product. Your vision.

He didn't hesitate: to help people realize their potential.

That is a much bigger idea than what he's currently selling. Biceps and supplements are a product. Human potential is a mission. He's always believed that a healthy body and a healthy mind were connected, but the economics of building for that mission were never there. Personalization at that level, behavioral coaching, mental performance programming, none of it was feasible on his budget. Until AI repriced the cost of delivering it.

He has the distribution and the trust that took decades to earn and cannot be manufactured overnight. The question is whether he's willing to point those assets at the real target. That's a different company. One worth the next twenty years.

I spoke to the board of directors of a leading consulting firm about this same tension. They've built a business on delivering exceptional work: presentations, frameworks, advice. Rigorous work that clients value. But the conversation kept circling back to the same friction. Clients receive a beautifully crafted strategy and then struggle to execute it, sometimes returning twelve months later with the same problems repackaged.

The opportunity isn't just doing the same work faster. It's being present through implementation, working alongside clients rather than handing off a document and wishing them luck. That's a different business model, and one their expertise earns the right to pursue, assuming they can evolve from words to outcomes.

I led a discussion with the C-suite of a major U.S. airline where we kept coming back to a basic question: what business are they actually in? The practical answer, reflected in their metrics and incentive structures, was operating an efficient winged germ tube. The aspiration they kept coming back to was something else entirely, genuinely connecting people with the people they love.

Those aren't the same company. One optimizes margins. The other engineers moments. AI doesn't resolve that tension, but it does remove the economic excuse for not pursuing the harder and more valuable version of the mission.

Through my work helping portfolio company Laurel connect with law firms, I spend a lot of time with senior legal professionals working through a genuinely hard problem. The resistance to AI tools isn't primarily about quality concerns. It's about the billing model. If AI compresses the hours required to deliver a result, the traditional hourly structure doesn't just shrink. It calls the whole value proposition into question.

For generations, their job has been to opine and refine. The firms that win the next decade will be the ones that learn to creatively solve and deliver outcomes.

Completely different businesses, one framework underneath both. The question is the same every time: do you have a differentiated asset? And do you have a vision it can serve?

If yes to both, do more with the same. Or better yet, do a lot more with more.

If you have the asset but not the vision, doing the same with less is a rational and respectable answer. Not every company has a bigger idea waiting to be unlocked. The mistake is never asking the question.

In a world where processing is rapidly approaching free, human skills are the scarce resource.

Across every industry, the person prized for processing speed, for grinding harder, staying later, producing more, is being replaced by something that doesn't sleep. That is not a tragedy. It's a reallocation. It's a liberation. The question is whether the people inside these organizations are ready to be reallocated toward something more valuable.

What can't be replaced is judgment, the ability to build trust, and the instinct to recognize that the person sitting across from you at dinner might matter in ways you can't yet see. That is not a junior skill or a senior skill. It is a human skill. And in a world where processing is rapidly approaching free, human skills are the scarce resource.

This is the post-AI career. Not faster hands, better instincts. Not more output, more impact.

Long-term greedy, as Gus Levy intended, was never really about money. It was about understanding where durable value actually lives, and having the patience to build toward it.