Hey Practitioners,
I hope that your new year is off to a great start, and that you took some time over the break and into 2026 to outline your specific AI Engineering career goals.
It's getting wild out there for everybody, and you know what they say about not having a plan 💀.
📈 By all accounts, it's going to be a great year for us AI Engineers and AI Engineering Leaders. According to LinkedIn, the AIE is the fastest-growing job out there. Interestingly, the number 2 fastest-growing job is "AI consultants and strategists."
TL;DR: The most important (and valuable) set of skills out there is to know how to build agentic AI systems, but the second most important skill is to understand the business problems they should be solving, the why behind building the agents.
It's safe to say that if you can do both, you'll be unstoppable 🦾.
I believe this is true for individuals and companies. The ones who will win in the marketplace in 2026 can answer two important questions:
- "What agents should we be building and why?"
- "Given that, how should we prototype our agents, get it into the hands of our users, and ship it to production with code that will scale with usage?"
It turns out that the best AI Engineers - the unicorns 🦄, I suppose you might say - can, of course, do both. They can span the spectrum between Product, Engineering, and Data Science, as Agent Engineering requires. Said differently, they can play the role of AI Engineer or of "AI Scientist," a term coined (most recently, at least) by Certified AI Engineer Jared Rand this week.
The AI Engineering space is challenging to navigate. The consultants, scientists, researchers, and product managers are filling engineering skills gaps, but at the same time, software engineers are learning data science and product management!
AI enables us to do more. So, we'll have to do more.
But what should we do?
Here's how I like to think about it, and how I've been recommending to people and companies that they might think about: don't ask what to do. First, ask what not to do.
Are you an individual considering your career? Ask "what do I definitely not want to do most of the time?" If you don't love coding, then learn enough to vibe code an agent with something like LangSmith's Agent Builder. If you don't love product management, then at least be able to brainstorm a use case using something like ChatGPT Use Cases for Work, and then use a reasoning model to help you write a plan for it. If you're not obsessed with defining the next evolution of the data scientist like Jared, at least be able to tinker with some automated fine-tuning. If you're personally struggling with your journey, feel free to reach out to Coach Mark, our new Student Success Manager, or to me directly, and we'll give you our best advice for your situation.
Are you an enterprise considering your 2026 strategy? Ask "of the many ideas we've come up with so far, which ones am I not sure will work?" Nix those ideas for now to remove unnecessary "nondeterministic" technical risk and go for low-hanging fruit. Focus on quick wins to build momentum by solving problems that people already solve well. Ask "of our current projects, which ones will definitely not require agents?" Then focus on triaging the other ones! If you're looking for more customized support for your enterprise, reach out to our consulting team about custom solutions.
In summary, there's never been a better time to jump into a new field and get your hands dirty by building 🏗️, shipping 🚢, and sharing 🚀 in an AI-enabled way. If you do this, you can stay on offense while focusing on all of the opportunities - the new jobs and many problems to solve - rather than worrying about all of the potential downside risks.
Until next month!
Cheers,
Dr. Greg