Feeling Behind on AI? How Dharmesh Shah Stays Up to Date on AI News

AI moves fast, much faster than any of us can realistically keep up with. New models, new capabilities, new agents, and new breakthroughs land daily. Trying to follow all of it can feel overwhelming even for people who work in AI every day.

That’s why we loved a recent edition of Dharmesh Shah’s Simple.ai newsletter. In it, he shared the method he personally uses to stay informed on AI without drowning in information. It’s practical, it’s simple, and—most importantly—it actually works.

Below is a recap of Dharmesh’s three-part strategy. If you want insights like this directly from Dharmesh, you should subscribe to his newsletter at Simple.ai.

Don’t just study AI. Use AI. (Every day.)

Dharmesh’s most important point is also the simplest: The best way to keep up with AI is to actually use AI.

Reading every research paper or headline won’t give you the same intuition as hands-on experience. When you use AI tools in your real day-to-day work, you start to naturally understand what’s possible, where the edges are, and how the technology is evolving.

Dharmesh highlighted a few examples of real AI agents already delivering value today that you might want to try in the Simple.ai newsletter, and you can find more on Agent.ai if you browse the marketplace. But my personal favorite is the Company Research Agent. It generates a detailed company research report in seconds, and is useful for anyone in sales, customer success, marketing, even if you’re interviewing for a job soon. Give it a whirl.

Of course, you don't need to use an Agent.ai agent. You can hop into ChatGPT to summarize a long document you don't want to read, or create a Claude Project with your company style guide to help you create content faster. Every few minutes you spend experimenting with AI builds real fluency, and it goes a lot further than just scrolling through AI headlines.

Schedule regular AI “opportunity reviews.”

Set aside a dedicated, recurring time (quarterly works well) to re-evaluate your biggest business challenges through the lens of what AI can now do. This is important because often companies evaluate an AI solution once, decide it’s not right for them, and then never revisit the decision again.

However, technology advances at a rapid clip, especially AI. A quarterly AI opportunity review gives you a chance to revisit whether a tool is appropriate now, even if it wasn’t three months ago.

Here’s how he structures these reviews:

1. Identify your biggest obstacles: What are the biggest issues slowing business growth?
   
   
2. Research new developments: Have any new tools launched in the last quarter that could help with that? Have any old tools we looked at in the past released new features that could help with that?
   
   
3. Run tiny experiments: Test any promising ideas with a small investment, so you’re not betting the farm on something that might not work. Note what works, and what doesn’t
   
   
4. Scale what works: When something shows promise, double down.

This isn’t building a perfect or complete AI strategy—it’s just making sure you don’t miss an opportunity simply because you forgot to look again.

Try new tools with a specific problem in mind.

When a new model, tool, or agent drops, it’s tempting to test it for its own sake. But Dharmesh argues that this leads to scattered impressions and wasted time. Instead, always test new AI tools against a specific problem you want to solve. This framing gives you:

• A concrete way to assess whether the tool is actually useful
  
  
• A more focused, productive way to experiment
  
  
• A clearer sense of the tool’s real business impact

Try new things—but always try them for a reason.

Avoid the “now isn’t the time” trap.

One of the biggest strategic risks Dharmesh sees is assuming AI can’t yet handle a specific business problem—and then never checking again. He’s seen this pattern repeatedly: A company tests AI for a use case, they conclude the tech isn’t ready, they don’t revisit the decision for a long time, and competitors who kept quietly testing pull ahead.

Instead of pushing these use cases off forever, keep a list of "not yet, but soon" ideas, the ones where AI is close but not fully ready. Review that list during your quarterly sessions. You might be surprised how quickly "not yet" becomes "definitely yes."

Buying into overblown AI hype is risky. But so is completely ignoring AI. The question isn’t if AI will impact your business—it’s when and how significantly. You don’t need to become a professional AI researcher. You just need to be sure you don’t miss meaningful opportunities simply because you weren’t looking.