Peter Sarlin’s Post

Yesterday, I attended the Information AI Summit in San Francisco, where I had the opportunity to connect with and hear from amazing leaders in the AI space. Here’s the 7 bets we all should make: 1. Stay Agnostic - Avoid the risk of proprietary lock-in by remaining flexible with your choice of LLM providers. 2. Build Unique Value - Add value beyond the models with frontier data procurement, post-training phases, and fine-tuning that develops your IP. It’s about Day 2 to Day 100+—continuously improving, evaluating, and building smarter, probabilistic software. 3. From Cost to Innovation - As LLMs plateau in raw progress, the focus shifts to making AI applicable/ 4. Higher-Level Services - Developers are seeking high-level services on top of models. The competitive edge lies in delivering rapid innovation, a faster product roadmap, and quicker time to market. 5. The Future of Models - Smaller, more data-efficient models are outperforming the larger ones, driving significant cost savings. Open models are catching up, offering flexibility and cost-effectiveness. The real battle is not just in size but in how well your stack is optimized. 6. Enterprise Lessons - Large models might shine in POCs, but production demands careful optimization. It’s not just about scale; it’s about smarter, more cost-effective deployment. 7. Your Competitive Edge - Your data is your secret weapon. Build custom models that leverage your unique assets, incrementally adding knowledge to outperform larger competitors at a fraction of the cost. The real opportunity in AI lies in the Application Layer. In the end, it’s about creating applications that don’t just work — they continuously evolve, adding real-world value in ways we’ve only begun to explore.

Yesterday, I attended the Information AI Summit in San Francisco, where I had the opportunity to connect with and hear from amazing leaders in the AI space. Here’s the 7 bets we all should make: 1. Stay Agnostic - Avoid the risk of proprietary lock-in by remaining flexible with your choice of LLM providers. 2. Build Unique Value - Add value beyond the models with frontier data procurement, post-training phases, and fine-tuning that develops your IP. It’s about Day 2 to Day 100+—continuously improving, evaluating, and building smarter, probabilistic software. 3. From Cost to Innovation - As LLMs plateau in raw progress, the focus shifts to making AI applicable/ 4. Higher-Level Services - Developers are seeking high-level services on top of models. The competitive edge lies in delivering rapid innovation, a faster product roadmap, and quicker time to market. 5. The Future of Models - Smaller, more data-efficient models are outperforming the larger ones, driving significant cost savings. Open models are catching up, offering flexibility and cost-effectiveness. The real battle is not just in size but in how well your stack is optimized. 6. Enterprise Lessons - Large models might shine in POCs, but production demands careful optimization. It’s not just about scale; it’s about smarter, more cost-effective deployment. 7. Your Competitive Edge - Your data is your secret weapon. Build custom models that leverage your unique assets, incrementally adding knowledge to outperform larger competitors at a fraction of the cost. The real opportunity in AI lies in the Application Layer. In the end, it’s about creating applications that don’t just work — they continuously evolve, adding real-world value in ways we’ve only begun to explore.

Yesterday, I attended the Information AI Summit in San Francisco, where I had the opportunity to connect with and hear from amazing leaders in the AI space. Here’s the 7 bets we all should make: 1. Stay Agnostic - Avoid the risk of proprietary lock-in by remaining flexible with your choice of LLM providers. 2. Build Unique Value - Add value beyond the models with frontier data procurement, post-training phases, and fine-tuning that develops your IP. It’s about Day 2 to Day 100+—continuously improving, evaluating, and building smarter, probabilistic software. 3. From Cost to Innovation - As LLMs plateau in raw progress, the focus shifts to making AI applicable/ 4. Higher-Level Services - Developers are seeking high-level services on top of models. The competitive edge lies in delivering rapid innovation, a faster product roadmap, and quicker time to market. 5. The Future of Models - Smaller, more data-efficient models are outperforming the larger ones, driving significant cost savings. Open models are catching up, offering flexibility and cost-effectiveness. The real battle is not just in size but in how well your stack is optimized. 6. Enterprise Lessons - Large models might shine in POCs, but production demands careful optimization. It’s not just about scale; it’s about smarter, more cost-effective deployment. 7. Your Competitive Edge - Your data is your secret weapon. Build custom models that leverage your unique assets, incrementally adding knowledge to outperform larger competitors at a fraction of the cost. The real opportunity in AI lies in the Application Layer. In the end, it’s about creating applications that don’t just work — they continuously evolve, adding real-world value in ways we’ve only begun to explore.

Today, the World Bank's Artificial Intelligence in the Public Sector Working Group held its second meeting, and as Microsoft's representative to the Working Group, I was pleased to be able to present a paper that we commissioned IDC to assist with, which focuses on helping the global public sector community move from "why AI" to "how to AI". At Microsoft, we have been committed to helping customers with the “Why AI” conversation for some time. However, as we move from the theoretical assessment to the practical application of the technology at scale, we are mindful that we need to do more to help our customers with how to procure and deploy AI. In that spirit, we partnered with IDC on this exciting research project, involving 330 key government procurement decision makers across ten markets. The resulting paper, Moving from “Why AI” to “How to AI” — A Playbook for Governments Procuring AI and GenAI, is a contribution to helping advance the considered procurement of AI by the world’s public-sector community. Knowing and understanding the challenges procurement teams face, in the context of AI, enables us all to develop solutions to address the challenges and realize opportunities. The paper applies lessons learned during the public sector’s hyperscale cloud journey, draws inspiration from the private sector (which takes a flexible approach to procurement), and provides some practical guidance in the form of a checklist for AI procurement in government.

Yesterday, I attended the Information AI Summit in San Francisco, where I had the opportunity to connect with and hear from amazing leaders in the AI space. Here’s the 7 bets we all should make: 1. Stay Agnostic - Avoid the risk of proprietary lock-in by remaining flexible with your choice of LLM providers. 2. Build Unique Value - Add value beyond the models with frontier data procurement, post-training phases, and fine-tuning that develops your IP. It’s about Day 2 to Day 100+—continuously improving, evaluating, and building smarter, probabilistic software. 3. From Cost to Innovation - As LLMs plateau in raw progress, the focus shifts to making AI applicable/ 4. Higher-Level Services - Developers are seeking high-level services on top of models. The competitive edge lies in delivering rapid innovation, a faster product roadmap, and quicker time to market. 5. The Future of Models - Smaller, more data-efficient models are outperforming the larger ones, driving significant cost savings. Open models are catching up, offering flexibility and cost-effectiveness. The real battle is not just in size but in how well your stack is optimized. 6. Enterprise Lessons - Large models might shine in POCs, but production demands careful optimization. It’s not just about scale; it’s about smarter, more cost-effective deployment. 7. Your Competitive Edge - Your data is your secret weapon. Build custom models that leverage your unique assets, incrementally adding knowledge to outperform larger competitors at a fraction of the cost. The real opportunity in AI lies in the Application Layer. In the end, it’s about creating applications that don’t just work — they continuously evolve, adding real-world value in ways we’ve only begun to explore.

Yesterday, I attended the Information AI Summit in San Francisco, where I had the opportunity to connect with and hear from amazing leaders in the AI space. Here’s the 7 bets we all should make: 1. Stay Agnostic - Avoid the risk of proprietary lock-in by remaining flexible with your choice of LLM providers. 2. Build Unique Value - Add value beyond the models with frontier data procurement, post-training phases, and fine-tuning that develops your IP. It’s about Day 2 to Day 100+—continuously improving, evaluating, and building smarter, probabilistic software. 3. From Cost to Innovation - As LLMs plateau in raw progress, the focus shifts to making AI applicable/ 4. Higher-Level Services - Developers are seeking high-level services on top of models. The competitive edge lies in delivering rapid innovation, a faster product roadmap, and quicker time to market. 5. The Future of Models - Smaller, more data-efficient models are outperforming the larger ones, driving significant cost savings. Open models are catching up, offering flexibility and cost-effectiveness. The real battle is not just in size but in how well your stack is optimized. 6. Enterprise Lessons - Large models might shine in POCs, but production demands careful optimization. It’s not just about scale; it’s about smarter, more cost-effective deployment. 7. Your Competitive Edge - Your data is your secret weapon. Build custom models that leverage your unique assets, incrementally adding knowledge to outperform larger competitors at a fraction of the cost. The real opportunity in AI lies in the Application Layer. In the end, it’s about creating applications that don’t just work — they continuously evolve, adding real-world value in ways we’ve only begun to explore.