This week I kept circling back to the same idea: the tools are getting smarter, but the real advantage is still how fast you and your team can learn.
One thread is where LLMs are headed next. The other is what it looks like to stay sharp when the ecosystem moves under your feet.
🎯 Strategic Leadership
I have been thinking a lot about what happens when LLMs stop acting like fancy autocomplete and start actually learning from new data and feedback loops. Not “remembering” in a prompt sense, but updating behavior over time.
If that shift lands the way it seems to be landing, a bunch of leadership defaults break. You cannot treat AI like a static dependency you version once a quarter. You need guardrails, evaluation, and a clear owner, because the system is going to change even if your code does not.
The practical bit: your job becomes setting up the learning loop. What gets measured, what gets reviewed, what triggers a rollback, and who gets paged when the model drifts.
I also worked through the AWS Generative AI Developer Professional certification path, and it was a good reminder that “keeping up” is mostly about reps. Not reading threads, but building enough small things that the patterns stick.
The certification prep pushed me to get more concrete about Bedrock, model choice tradeoffs, and how AWS expects you to think about safety and architecture. The early adopter badge is fun, but the real win is having a cleaner mental model when a team asks, “Should we use Bedrock for this, or roll our own?”
If you are leading a team, this is the kind of learning that pays back fast. You can make decisions quicker, spot hand-wavy vendor claims, and ask better questions in design reviews.
If you are dealing with models that change over time (or you are trying to build a learning loop into your org), hit reply and tell me what you are seeing. I read every reply, and it helps me pick what to write about next.
Triple Insight #2 – Learning Loops
This week I kept circling back to the same idea: the tools are getting smarter, but the real advantage is still how fast you and your team can learn.
One thread is where LLMs are headed next. The other is what it looks like to stay sharp when the ecosystem moves under your feet.
🎯 Strategic Leadership
I have been thinking a lot about what happens when LLMs stop acting like fancy autocomplete and start actually learning from new data and feedback loops. Not “remembering” in a prompt sense, but updating behavior over time.
If that shift lands the way it seems to be landing, a bunch of leadership defaults break. You cannot treat AI like a static dependency you version once a quarter. You need guardrails, evaluation, and a clear owner, because the system is going to change even if your code does not.
The practical bit: your job becomes setting up the learning loop. What gets measured, what gets reviewed, what triggers a rollback, and who gets paged when the model drifts.
Read more
🤖 AI & Automation
I also worked through the AWS Generative AI Developer Professional certification path, and it was a good reminder that “keeping up” is mostly about reps. Not reading threads, but building enough small things that the patterns stick.
The certification prep pushed me to get more concrete about Bedrock, model choice tradeoffs, and how AWS expects you to think about safety and architecture. The early adopter badge is fun, but the real win is having a cleaner mental model when a team asks, “Should we use Bedrock for this, or roll our own?”
If you are leading a team, this is the kind of learning that pays back fast. You can make decisions quicker, spot hand-wavy vendor claims, and ask better questions in design reviews.
Read more
If you are dealing with models that change over time (or you are trying to build a learning loop into your org), hit reply and tell me what you are seeing. I read every reply, and it helps me pick what to write about next.
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January 18, 2026Triple Insight #2 – Learning Loops
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