Reflections on SF TechWeek 2025

Reflections on SF TechWeek 2025

#sftechweek #physicalai #deeptech | Bogdan Cristei 🇷🇴🇪🇺🇺🇸
What an amazing #SFTechWeek this has been! Had the privilege to attend most of the DeepTech and Physical AI events Some of the biggest takeaways from the week below 👇 === The Inflection Point === AI’s leap is driven by three converging forces: * Exponential compute beyond Moore’s Law (horizontal scaling via data centers). * Breakthroughs in GenAI + multimodal models enabling perception and control in the physical world. * Real-world pressure from labor shortages and reshoring. Energy — not chips — is emerging as the true bottleneck for scaling AI. Simulation accelerates iteration, but real-world data is the unlock for embodied intelligence. The Physical AI Stack is converging: sustainable energy → compute → data pipelines → robots. Until standards form, investors are backing end-to-end vertical systems; expect M&A and consolidation as interoperability improves. === From Labs to the Real World — Adoption & Culture === Cultural readiness beats technical readiness. Scaling from one factory to many fails when internal champions aren’t in place to sell ROI and replicate wins. Knowledge preservation matters. Veteran operators carry tacit data — “the smell, the sound” — that AI systems now aim to model. Reshoring + sustainability = new capacity. Automation is reducing labor arbitrage and enabling localized, distributed manufacturing. === Broader Reflections === Energy + Compute + Hardware + Software + Data are fusing into one Physical AI stack. The best opportunities sit where physical constraints meet digital leverage — logistics, manufacturing, energy, infrastructure. Ecosystems > point solutions. Interoperability will be the next competitive moat. ROI > novelty. Adoption happens when robots solve “must-do” problems. Sustainability isn’t a side story — it’s becoming the foundation for performance and cost advantage. === Favorite quotes from the week === 🧠 “We’re not at the end of Moore’s Law — we’re in the sequel.” 🤖 “You don’t need humanoids. You need automation that works.” 💡 “Customer love is the ultimate moat.” 😅 “The best companies don’t have customers — they have hostages.” --- Great to see both new and familiar faces including: Simon Lancaster 🇺🇸🇨🇦🇵🇹, Sabrina Paseman, Maxwell Wang, Ye Wang, Andrew Chen, Kevin Wu, Ghonhee Lee, Dr. Anika Stein, Webber Xu, Jim Zhu, David Sokolic, Sumay Parikh, Martin C., Bhavik N., Marco Marinucci, Megan Cain, Riley Rodgers, Iris Lee, Dave Anderson, Francesco Favaro, Clinton Smith, Ayush B Shah, Hasan Sukkar, Deep Patel, Phil Shea, Aanjanaye Kajaria, Clinton Smith, Max Myer, Brandon Barbello and others! #PhysicalAI #DeepTech

What an amazing #SFTechWeek this has been!
Had the privilege to attend most of the DeepTech and Physical AI events
Some of my biggest takeaways from the week below 👇


The Inflection Point

AI’s recent leap is being driven by three converging forces:

Exponential compute – we’ve gone beyond Moore’s Law, scaling horizontally through data centers and distributed networks.

Breakthroughs in GenAI and multimodal models – making perception, reasoning, and control possible in the physical world.

Real-world adoption pressures – labor shortages, reshoring, and the need for automation.

Energy — not chips — is emerging as the true bottleneck in scaling AI infrastructure.

Simulation is critical for speed and safety, but real-world data remains the unlock for embodied intelligence and dexterous tasks. Synthetic data fills the last 30%, but physical deployments close the loop.

The Physical AI stack is converging:

  • Energy + sustainable compute
  • Data centers and edge GPUs
  • Real-time data pipelines + interoperability
  • Robotics and embodied systems at the top

Investors are favoring end-to-end systems until standards mature. Expect M&A and consolidation as interoperability improves.


Real Adoption Requires Cultural Fit

Scaling from one factory to many often fails due to culture and technical readiness, not the tech itself.

Internal champions — those who can sell ROI and replicate success — are what make adoption stick.


Knowledge Preservation and Trade Skills

A key opportunity for Physical AI lies in capturing tribal knowledge from retiring workers.

“When a 20-year veteran knows a machine’s smell or sound — that’s tacit data we need to model.”

Reshoring, Sustainability, and Labor

Automation reduces the role of labor arbitrage, enabling localized, distributed manufacturing closer to demand centers.

Physical AI isn’t just about efficiency — it’s about rebuilding capacity.


Broader Reflections

  • The Physical AI Stack is converging — energy, compute, hardware, software, and data are merging.
  • Invest where physical constraints meet digital leverage (logistics, manufacturing, energy, infrastructure).
  • Think in ecosystems, not point solutions — interoperability will be the next moat.
  • Prioritize ROI and reliability — adoption follows when robots solve must-do problems.
  • Sustainability is now a core advantage, not an afterthought.

Favorite quotes from the week:

🧠 “We’re not at the end of Moore’s Law — we’re in the sequel.”
🤖 “You don’t need humanoids. You need automation that works.”
💡 “Customer love is the ultimate moat.”
😅 “The best companies don’t have customers — they have hostages.”


Read more