What day of the week would you want to attend a live workshop with Igor & Dean to learn next level AI tactics & strategies?

There's a specific and easily overlooked pattern in how AI-assisted ideation actually plays out in practice. AI generates a plausible first option remarkably fast. That option is usually reasonable, competently constructed, and immediately available. And because it's immediately available and reasonable, there's a strong pull toward accepting it and moving on, rather than pushing further into genuinely better territory that would have required more iteration to reach. This pattern is quietly narrowing the range of ideas that actually get considered before a direction gets locked in, and most people doing it have no idea it's happening, because the first option genuinely is good enough to feel complete. ------------- Context ------------- Before AI, generating a first option for anything, a strategy, a piece of creative work, a solution to a problem, required real effort. That effort created a natural incentive to keep working with what you'd produced rather than starting over, but it also meant that the ideation process itself often surfaced better ideas along the way, because thinking through a problem carefully to produce even a first option involved genuine engagement with its complexity. AI changes this dynamic in an important way. The first option is now nearly free to generate. There's no natural effort barrier discouraging you from generating more, but there's also no forcing function requiring the kind of deep engagement that used to happen automatically while producing that first option manually. The speed of AI's first response can create the feeling of having done the ideation work, when in fact very little genuine ideation has happened yet. The AI generated something plausible quickly. That's different from having explored the actual space of good options. This creates a subtle trap: because the first AI-generated option is reasonable and immediately available, there's less felt need to push further, even though pushing further, in a world where generating additional options is nearly free, would often surface genuinely better ideas with very little additional cost.

Anthropic finally brought Fable 5 back and in the same week, they also launched the new Sonnet 5 model. In this video, I break down everything you need to know about these models and explains which one you should be using. Enjoy!

Individual AI adoption inside a team almost always looks reasonable at the individual level. Each person picks tools that work for them, develops prompting habits that feel natural, applies their own sense of what good output looks like. None of this seems like a problem in the moment. It's just people using tools the way people use tools. But viewed from the outside, from a client's perspective looking at the collective output of a team, the picture often looks different. Different tools, different quality bars, different tones, different levels of AI reliance across team members can add up to a business that sounds inconsistent, even when every individual is doing perfectly reasonable work on their own terms. ------------- Context ------------- Before AI, teams naturally converged toward a somewhat consistent voice and quality standard, partly because there were fewer tools shaping output and partly because most content and communication passed through some form of shared review or house style. AI has introduced significantly more variability into that picture, because AI tools shape output in ways that are specific to the tool, the prompting approach, and the individual using them. Two team members working on similar client deliverables, both using AI assistance, can produce noticeably different results: different sentence structures, different depths of analysis, different default tones, different levels of polish, depending on which tool they favor and how they've learned to use it. Individually, both outputs might be perfectly good. Collectively, if a client sees work from both team members, the inconsistency becomes visible in a way that erodes the sense of a coherent, unified business. A small consulting firm discovered this when a client who had worked with two different team members on related projects mentioned, gently, that the two deliverables felt like they'd come from different companies. Both were high quality individually. But the tone, structure, and analytical style were different enough that the client noticed and found it slightly disorienting. Neither team member had done anything wrong by their own standards. But the firm's collective output lacked the coherence that clients expect from a single business.

We want to better understand what people are TRULY trying to accomplish when it comes to AI so we can make our products better. We know it’s broad and there are so many different lanes, but if you had to pick one of the 2 options below, which one would you choose?