Crowdsourcing knowledge is the biggest opportunity in enterprise AI. Most expertise in a company is not in systems. It lives in decisions, conversations, and the unwritten know-how people carry. That insight is powerful, yet it often stays hidden. Our latest blog explains why

1/4 @McKinsey's study (Rethink management and talent for agentic AI) shows a clear gap. Agentic AI adoption is high, but real value is still missing. The reason is management and talent, not the tech. See slide:

AI is the greatest technology equalizer of our time. Those who use it will outperform those who don’t. Jensen Huang @nvidia explains why in @theallinpod clip.

TIME magazine named “The Architects of AI” as its @TIME 2025 Person of the Year. That choice matters. This moment is not about a single model or breakthrough. It is about the people designing the systems underneath modern work. AI has crossed an important line. It is no longer

You can feel the momentum when the right minds gather. A sharp, focused planning session today with a few Austin AI heavy hitters.

Google just opened the @GoogleAI Gemini Deep Research agent to developers via the Interactions API, bringing advanced reasoning and multi-step synthesis into real products and workflows. • Built for long-running research with planning, search, and analysis • Powered by Gemini

The @cleoabram and @sama exchange raises a question many feel but rarely say out loud. If AI removes the struggle, do we lose the growth that comes from it? Cleo uses time under tension from weightlifting to frame this. Sam counters with a sharper point. The struggle does not

Most teams don’t struggle because of complexity in the work. They struggle because of the cognitive pressure around the work. AI reduces this pressure. It frees people to focus on contribution instead of confusion. This shift is not about replacing people. It is about giving

Key takeaway: Enterprise AI is entering a new phase. • From experiments to infrastructure • From isolated apps to workflow-native systems • From early wins to scaling across teams The edge goes to companies that turn AI into a governed, integrated, end-to-end capability.

Adoption is rising everywhere: tech, healthcare, manufacturing lead the surge, while finance and professional services scale at high volume. A gap is widening. Frontier firms send far more AI messages per worker and use more AI per seat. That gap is the opportunity.

AI is delivering measurable results. • Workers save 40–60 minutes/day • Data science, engineering, and comms teams save 60–80 minutes/day • 75% say AI improved speed or quality • Non-technical staff now build, code, and analyze with AI This is not marginal efficiency. It is

Enterprise AI usage is exploding. • Weekly messages up 8× YoY • Reasoning token use up ~320× • More companies moving to multi-step, repeatable workflows • Custom GPTs rising as persistent workflow engines The shift is real and accelerating.

Enterprise AI usage is exploding. • Weekly messages up 8× YoY • Reasoning token use up ~320× • More companies moving to multi-step, repeatable workflows • Custom GPTs rising as persistent workflow engines The shift is real and accelerating.

Sam Altman says AI is the fastest-adopted technology in history, and it's only three years old It is a general-purpose tool that can cure diseases or be used for harm, and the rapid change in jobs is inevitable "the critical challenge is ensuring humans have time to adapt"

Bottom line: The question isn’t “cloud or on-prem.” The question is “what does this workload require?” Leaders who think this way build AI systems that are faster, safer, and easier to govern. Read our detailed blog on our website.

The real future is hybrid. Run low-risk tasks in the cloud for agility. Run high-sensitivity workloads on-prem for security and control. Match the environment to the workflow. That’s where efficiency and compliance meet.

The drawback? On-prem can feel heavy without the right orchestration layer. Enterprises need a platform that coordinates models, tools, agents, and knowledge sources inside their own environment.

On-premises AI changes the equation. Enterprises gain predictability, privacy, and tighter integration with internal systems. This matters when workflows rely on: • sensitive data • low-latency decisions • compliance boundaries • deterministic performance

Cloud AI gives teams speed. You can experiment quickly, scale easily, and use managed infrastructure without heavy lift. But convenience comes with tradeoffs: • variable latency • limited control • data residency constraints • dependency on third-party availability

On-Premises AI or Cloud AI? Enterprises are rethinking this choice as AI moves deeper into regulated, high-stakes workflows. Here’s a quick 5-minute breakdown for leaders making architecture decisions for their organizations. 🧵