The last decade belonged to SaaS. Now, we’re seeing a shift from SaaS to workflows shaped around AI agents. These agents carry context, plan next steps, call APIs, surface anomalies, and act with minimal human intervention.
Companies saw an opportunity to optimize processes and maximize their efficiency. Even non-tech businesses are adapting, implementing AI to compress timelines, cut costs and reduce manual load. Startups built in this paradigm have an edge.
The Pros and Cons of the AI Staffing Shift
Artificial intelligence is rapidly transforming the workplace as it reshapes workflows. The advantages of this include the introduction of new lines of development, new roles like AI reviewers and prompt engineers and upskilling resources. However, this has also led to overaggressive staffing reductions in some situations and the loss of capital when automation isn’t carefully approached.
Yes, that often means doing more with fewer people. But it’s not new. The ATM, the spreadsheet and the cloud each did the same: they all killed jobs and created new ones. That’s the nature of progress.
Staff reductions that follow optimization may feel harsh, especially when they affect hundreds or thousands of specialists across an industry. So, this raises an ethical dilemma: is a startup harmful if it automates a workflow and displaces a certain number of workers?
Understanding the AI Staffing Shift
I don’t think it’s harmful if a startup wants to automate workflows that lead to staffing reductions. The value it creates circulates back into the economy. That money will get reinvested into new strategic goals, new lines of development and new roles. We're already seeing them: AI reviewers, domain experts and prompt engineers. People who sit between models and outputs: guide, audit and adapt automation. The pattern is familiar: new capabilities create new gaps, and those gaps attract talent.
What’s different about this technical shift is the access to knowledge we all have today. In the past, workers displaced by new technologies had few retraining options. Today, the AI revolution gives people something they didn’t have, not even 30 years ago: near-unlimited access to learning.
Anyone with internet access can tap into centuries of accumulated human experience for free or for a few dollars a month. If you want to reskill, open Coursera or ask ChatGPT. The tools exist and people who are motivated enough can find a way forward.
That doesn’t mean transition is painless. But the baseline has shifted. Still, how companies manage those transitions makes all the difference.
Not every staffing reduction is smart. We’ve already seen companies cut too aggressively, only to scramble and rehire when demand returned. We’ve also seen the inverse: companies that scaled headcount too fast during the COVID surge, only to cut thousands of jobs once demand cooled. Short-term efficiency at the expense of long-term resilience is its own kind of failure, and markets usually punish it. Both overreactions (cutting too deep or hiring too much) end up the same way: in wasted capital, lost talent and slower execution.
Our Real Job as VCs
As investors, we’re often funding the very changes that trigger those workforce transitions: products designed to reduce friction, compress costs and automate workflows. Which brings us to another hard question: is it wrong to invest in a company that offers to optimize human labor?
In most cases I’d say no, it’s not. It’s wrong when automation strips away accountability — when decisions are handed over to systems in ways that break customer trust or create hidden risks. For critical sectors like healthcare or financial services, it’s wrong when efficiency is prioritized over safety. Replacing humans entirely without guardrails can multiply risks. It’s also wrong when there’s no real problem being solved, when automation is just a hammer looking for a nail. Those companies are just burning capital, the market filters them out quickly, and those aren’t the businesses we back.
Outside of those cases, our job as VCs is to fund startups that solve real problems and can scale. I’m accountable to limited partners. It’s my job to determine the course innovation takes, then invest in the teams best positioned to ride that wave, and deliver returns. That means supporting the most focused, scalable startups I can find, that solve real problems of businesses.
That said, we don’t just back enterprise tools or automation engines. We at AltaIR Capital and Pre-seed to Succeed also invest in teams building adaptation infrastructure: startups like Final Round AI and Global Work, which help people navigate career transitions and find work faster deserve no fewer attention. Such companies are active players in the future of work landscape, and they have real market potential. But our decision to invest wasn’t based solely on a desire to employ the unemployed. It was based on product-market fit.
VCs aren’t moral arbiters, but that doesn’t mean we’re blind to impact. I also make personal investments. In some cases, I support founders building social projects that may never return capital. If a project fails, the team still learns. The skills remain. That’s part of the return, even when the capital is gone. However, that’s a choice I make as an individual. I don’t bring those to my fund’s investment committee.
Venture capital has always been a catalyst of technological change, and of course we can’t sit outside AI transformation. We’re in the thick of it. While driving the innovation financially, the venture capital industry has experienced the influence of tech evolution itself. Not only do we fund the companies building AI tools, but also our own operations start increasingly run on them too. To stay competitive, we’re automating workflows, using AI for faster analysis, scoring and research. VCs are market players like everyone else, and the slowest and least efficient will fall behind. After all, the rules of the game apply to all players, don’t they?
