Most founder-led companies don’t get stuck because of competition or market conditions. They get stuck because the architecture that got them to $30 or $50 million wasn’t built for what comes next. Three patterns show up consistently: the founder who’s become a bottleneck instead of an accelerant, operational systems held together by workarounds and tribal knowledge, and a customer base that no longer fits the business they’ve become. These aren’t effort problems. They’re structural—and they require structural solutions.

LLMs improve speed and average quality, especially for novices, but they also invite automation bias and shallow processing if you let them think for you. The gains are uneven across roles: students and junior pros improve fastest, experts risk persuasive errors on complex work. Treat the model as a co-worker, and keep humans as the decider. This dynamic takes advantage of LLM benefits while reducing the potential for cognitive decline.

AI is shifting leverage in retail supply chains. Suppliers that build real demand intelligence can forecast, spot trends early, and walk into buyer meetings with stronger evidence and better terms. Used well, AI becomes a negotiation tool that raises margins and moves power from sellers to makers.

AI and data science are now essential for CPG sales and marketing as phones, email, and LinkedIn deliver less reach and weaker results. Early adopters use predictive models to target, forecast, and launch faster, with leaders like Unilever, PepsiCo, and P&G showing clear ROI in campaigns, innovation, and supply chains. Companies that delay face higher costs and slower growth. The path forward is simple in concept and hard in practice: reorganize around data, build AI skills, deploy predictive outreach, and iterate in real time.

I understand there is AI fatigue at the moment. LinkedIn, X, and yes, blog content is jammed with everyone shouting their opinions and professional expertise. This one is technically about quantum computing and its inevitable impact on AI.

Keeping up to speed on advancements in AI can be a full time job these days. Here are some sources of information I have found to be a good mix:AI Research Journals and Tech News Outlets: Check out the Journal of Artificial Intelligence Research and Nature Machine Intelligence. I personally enjoy Ethan Mollick’s One Useful Thing on Substack, the TLDR AI newsletter, and McKinsey publishes a ton of insightful content.

Advancements in AI—especially in LLMs and agentic systems—are accelerating at an almost unbelievable pace. Week by week, these tools become more capable, more integrated, and more accessible. Alongside these improvements, the cost of compute continues to drop. Sam Altman recently shared that the cost to use a given level of AI drops roughly 10x every 12 months. To put that into perspective, the token

The AI industry has long been dominated by a few prevailing assumptions: building cutting-edge AI systems requires enormous computing power, vast datasets, and a war chest of capital.