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@chamath @Benioff @satyanadella And most data leakage isn't malicious, it’s convenience. Every time a workflow is built outside the system of record, a piece of institutional knowledge is lost. That's why AI sovereignty is an architecture question long before it's a policy one.

When we look back, Alex Karp may have initiated an important preference cascade around AI sovereignty. It’s worth noting what @Benioff and @satyanadella are both saying: Your knowledge, as a company, is your sovereignty. If you lose it to someone else (anyone else) you are hollowing your organization out. There are many ways to accidentally leak intelligence so you need partners and tools who can sign up for the complexity required to give it to you. See Benioff below and see Satya’s essay linked below.

Got ZDR? 🚀 The currency of AI is trust. Since launching Agentforce Trust Layer with Zero Data Retention (ZDR) on June 12, 2023, we’ve drawn a hard line with our model suppliers: Your data is YOUR data — it is NOT our product or their product. 🛡️ We have never used customer data to train AI models. Ever. Learn more: salesforce.com/ca/artificial-… #Trust #Agentforce #ZDR #Salesforce

Agreed. And the shape of work is changing too. Six months ago people were using AI to write a function, now we're describing a whole experience for an agent to assemble. And as the coding part gets faster and easier, it's becoming more apparent that the new bottleneck is what the agent codes against: data, workflows, permissions, etc.

It's mind-blowing how fast agentic coding has progressed in the past 6 month. It's a completely different world now.

See what happens when AI goes off-script, and learn how to map your brand values directly to agent behavior: sforce.co/4w8aJLu

Customers are abandoning AI agents that feel unpredictable A clever chatbot persona isn't enough to drive adoption—brands need reliable AI that acts like a trusted employee Here are 3 ways to build agents customers actually trust: Define core company values upfront to dictate agent behavior before development begins Rigorously test edge cases and build plain-language fallbacks for seamless human handoffs Measure qualitative user satisfaction instead of just relying on closed support tickets Agentforce builds these guardrails directly into your agents using Agentforce Builder and Testing Center, ensuring every interaction stays secure, reliable, and perfectly on-brand.

@matei_zaharia The tasks that actually matter to each company also sit inside their unique data, workflows, and policies. So, every company needing its own loop also means every company needs its own eval set built from real work.

One reason we built custom coding and data agent benchmarks internally at Databricks (e.g. databricks.com/blog/benchmark…). Academic benchmarks are great and people will build better ones, but you also care about YOUR tasks, which are often different. Each company needs its own "loop".

Want to dive deeper? Here's more on how smart companies are winning with agentic commerce: sforce.co/3Rrx87w

39% of consumers now let AI do the product discovery Shopping behavior has officially shifted, and agentic commerce changes how brands get discovered Here are 3 ways to stay visible to LLMs: → Clean up product data with structured details and natural search terms → Feed catalogs directly into AI platforms instead of relying on web scraping → Drive third-party reviews on social platforms to signal trust to AI models Salesforce B2C Commerce automatically syncs product catalogs to OpenAI and Google through new direct feed protocols, ensuring inventory sits right in front of the agents making the purchase decisions.

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