Finding people who can effectively deploy AI agents is hard. Instead of sifting through LinkedIn for "AI experience," the best talent pools are top-performing Forward Deployed Engineers from AI companies or intellectually curious internal employees who deeply understand the business context.

The most in-demand skill at labs like Google DeepMind is low-level engineering for accelerating LLM runtime. This involves creating efficient, custom software artifacts (kernels) for new neural net architectures and serving techniques at scale.

The most effective path to a first product management role is often within one's current company. By leveraging existing credibility, relationships, and organizational context, aspiring PMs can bypass the hyper-competitive external hiring process and make a smoother transition into the role.

To ensure AI adoption is a core competency, formally integrate it into your team's operating system. Webflow is redoing its career ladder to make AI fluency a requirement for advancement, expecting team members not just to use tools but to lead, own, and push the boundaries of AI in their work.

Getting hired at a premier AI lab like Google DeepMind often bypasses traditional applications. Top researchers actively scout and directly contact individuals who produce work that demonstrates excellent "research taste." The key is to independently identify and pursue fruitful research directions, signaling an innate ability to innovate.

To transition into AI within your company without prior experience, proactively seek out nascent AI initiatives. By raising your hand for the "messy middle" where no one is an expert yet, you can learn on the job and establish yourself as a key player.

To pivot into an AI PM role without direct experience, create a case study by analyzing a past project you shipped. Articulate how AI could have enabled different features, improved outcomes, or changed the approach. This demonstrates applied thinking and initiative to recruiters.

By embedding product teams directly within the research organization, Google creates a tight feedback loop. Instead of receiving models "over the wall," product and research teams co-develop them, aligning technical capabilities with customer needs from the start.

Perplexity's talent strategy bypasses the hyper-competitive market for AI researchers who build foundational models. Instead, it focuses on recruiting "AI application engineers" who excel at implementing existing models. This approach allows startups to build valuable products without engaging in the exorbitant salary wars for pre-training specialists.