While it felt anything but in the moment, the SaaS era was a simpler time. We knew how things worked — and many of those things had worked the same way for decades.

Take the talent market: Talent fueled growth. When the market was hot, companies needed people. Demand rose, and it got easier to find a job. Talent gained leverage and used it to get better comp, better benefits, better roles. When the market cooled, the opposite happened.

In the AI era, it’s not that simple.

By most measures, tech had a great 2025: Market caps up. Revenue up. VC, PE, and exits — all up. Yet, many of us have friends in tech who are having a tough time finding a new job. 

I’ve personally heard the words “this is the toughest job market,” many times. Then again, I’ve heard the words “this is the hottest job market ever” many times, too. Usually one is right, but today both are! Like I said, this isn’t a simple talent market. 

Since this is my bread and butter at STA, I spend a lot of time trying to understand what’s happening. And while I can’t take us back to a simpler time, I can share what I’m seeing. Let’s go!

We’ve read a lot about the various raw materials that AI needs to work. Data. Compute. Electricity. But we overlook the most important variable — the person using it. The intelligence, expertise, and judgment of the person using AI carry a lot of weight, and to discount that influence is a mistake.

Every day I learn this lesson when my partner Sherry shows me a new and better way to use AI. She isn’t in tech, but she’s smart as a whip and she uses it a lot — a magic combo.

If the Zaman household is too small a sample for your liking, Zuck just announced this week that AI makes Meta’s average engineer 30% more productive and their best engineers 80% more productive.

Anyone who has tried to implement AI at a company has learned a similar lesson. The better the person using it, the better the results.

This is having a significant effect on the talent market: AI-driven companies demand the very best people. Those who can help you get the most out of these new, powerful, and less than perfect tools.

The promise of AI is that it makes people and hence, companies, more productive. We have proof of this:

In our world of GTM, Kyle Norton’s Owner.com team has reps averaging 3x productivity of the best teams he previously managed, and Matt Braley’s EliseAI team has 80% of MM and Enterprise reps hitting quotas that are 2x the norm for those roles.

So, yes, AI does make people more productive, which means companies need fewer people per $1M in ARR. But that automatically means that the value of each person goes up. Even more so when you take into account that to get the benefits of AI, you need the best people. 

“Doesn’t that just mean there are far fewer open jobs out there?”

Not really! 

Firstly, AI companies are growing revenues faster than ever before. So, while they need fewer people per stage of growth, they’re moving through stages faster than before.

Secondly, in 2024, there were 585K tech companies in the U.S. In 2025, it was 702K. AI is driving more company formation, which is driving demand for talent. 

Lastly, there’s a belief that Big Tech isn’t hiring. That head count is steady. On the contrary, in 2025, NVIDIA grew headcount by 21.6%, Meta grew 6.5%, and Alphabet grew 3.7%. They’re hiring and firing at the same time, as they remove bloat, benefit from AI, and readjust to the new world. 

Our research at STA shows that currently there are 428K open tech jobs in the U.S. Demand is there. Lots of it, actually. But it’s concentrated at the very top. That’s why the talent market is both hot and not at the same time.

Hiring the very best was never easy, but in the SaaS era I saw a lot of tech companies give up on the idea of always hiring the best, and instead, figure out how to make it work with B-Players.

Many of those companies never made it. Some did, though. 

The belief in the Valley and in NY is that now you’ll certainly not make it with B-Players. 

So everyone is trying to hire the best. Making it much harder than before.

How does one win in this world? 

It’s hard! But there is a way.

These days, people tend to look at competing firms to try to find the best person in the same job to join them. If you’re a category leader like OpenAI, Cursor or Harvey, you can probably do that and win. 

But, many non–category leading firms are trying this approach, too. 

The problem with this strategy for the latter group is this: Why would the best person at your competitor leave to do the same job for you? Usually, there isn’t a good reason. And usually, you end up with the person who wasn’t the best. 

The GTM version of this hiring strategy is over-optimizing for things like: 

These are the wrong things to focus on, and have been the source of many B-Player hires in GTM. 

Instead, you need to get more creative. Spend some time thinking about what these attributes are supposed to tell us.

Let’s pick one: Deal size. It’s a way of figuring out if the rep can manage the same complexity that they’ll be dealing with at your firm. 

Ask yourself: Can some $50K deals be more complex than $100K deals? Yes, they can. 

If that’s the case, then using deal size as a filter is flawed.

An alternative: Think about which attributes enable a person to manage complexity. You’ll come up with things like intellectual curiosity, strategic thinking, composure, and resourcefulness.

And, guess what? Now, you can use AI to help you in this process! Just ask AI to help you understand the attribute and give you ideas on how to qualify those attributes in an interview process. Trust me, it’s really powerful. 

But very few are doing this. They’re trying to hire A-Players with the strategies that built B-Player teams in the SaaS era. 

This is an opportunity for you to be different. To become the leader who spots excellence where no one else is looking.

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