Michael Tuszynski

Something unprecedented happened in AI this week. The companies most aggressively competing to dominate the AI landscape—OpenAI, Anthropic, Google, Microsoft, Amazon, and dozens of others—just agreed to collaborate on open standards for how AI agents should work together. The Agentic AI Foundation (AAIF), announced December 9, 2025, represents

Last month, a senior ML engineer with nine years of experience posted a question that resonated across the industry: "Is senior ML engineering just API calls now?" The post attracted 385 upvotes and 176 comments from engineers experiencing the same existential crisis. Their work had quietly transformed from

The AI industry just hit a counterintuitive inflection point that should concern every CTO deploying large language models in production: the more sophisticated our reasoning models become, the more frequently they hallucinate. This isn't speculation or vendor FUD. It's measurable, documented, and admitted by the companies

The conversation around AI infrastructure costs has shifted dramatically in the past year. Every CTO and engineering leader is asking the same question: should we run our own LLMs or stick with cloud APIs? The answer isn't as straightforward as comparing GPU prices to token costs. After analyzing

The enterprise AI landscape is experiencing a seismic shift that most CTOs haven't fully grasped yet. While we've been laser-focused on AI implementation and performance metrics, a more fundamental crisis has been brewing beneath the surface: enterprise AI vendor dependency is creating the next generation of

After decades in the consulting trenches, I've witnessed the full spectrum of Statement of Work disasters – from scope creep that doubled project costs to payment disputes that ended partnerships. The data backs up what many of us have learned the hard way: 85% of project managers acknowledge that

The software development landscape just shifted. While most teams are still debating whether AI coding assistants are worth the investment, a growing cohort of engineers is shipping complete applications in under an hour. The difference isn't the tools—it's the methodology. The 90% Reality Check Here&

The numbers are staggering: generative AI training clusters consume 7-8 times more energy than traditional computing workloads. That's not a typo. Seven to eight times. As data center power requirements in North America jumped from 2,688 MW at the end of 2022 to 5,341 MW by

The question haunting enterprise boardrooms today is deceptively simple: "How do we avoid becoming the next AI platform cautionary tale?" With organizations committing tens of millions to their AI strategies, the stakes couldn't be higher. I've been in this industry for 25 years, and