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Where does your proprietary data go once AI agents start touching it? Yorke Ξ Rhodes III 🟧 🇺🇦, Director Digital Transformation, Traceability & Co-Founder of Blockchain at Microsoft, describes a realization spreading through the enterprise space, and what it means for anyone with a proprietary way of doing business.

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41,619 просмотров • 1 месяц назад •via X (Twitter)

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🧠 The #1 challenge of the AI era is trust. How do you know what's real? Yorke Ξ Rhodes III 🟧 🇺🇦 Co-Founder, Blockchain at Microsoft joins us this week on #GenC to discuss why digital provenance and verifiable data is essential to save the internet from synthetic media.
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🧠 The #1 challenge of the AI era is trust. How do you know what's real? Yorke Ξ Rhodes III 🟧 🇺🇦 Co-Founder, Blockchain at Microsoft joins us this week on #GenC to discuss why digital provenance and verifiable data is essential to save the internet from synthetic media.

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Your AI is only as smart as the context it runs on. Have you built your shared enterprise brain yet? At Databricks Data+AI Summit, I spoke with Prukalpa ✨, co-founder and co-CEO of Atlan, to go deeper on the AI Context Layer in practice. The concept of a shared enterprise brain resonated with me. It is a foundation that every AI agent draws from, learns from, and contributes back to. "The future of organizations are going to be the fastest learning organizations." - Prukalpa ✨ Sankar, co-founder and co-CEO of Atlan Watch the full interview on YouTube: Check out the live demo by Atlan on Building Context for Your AI Agents - #EnterpriseAI #ContextLayer #Data #AI #AtlanPartner Matthias Heidrich
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Ackman views $MSFT as having an "impossible to disrupt" position in enterprise software. The base Microsoft 365 package has 450M users at a very low cost per seat, roughly $200 per customer. It would be nearly impossible for an enterprise to replace these various components with 3P vendors, even at twice the price. Microsoft Copilot gives enterprise employees a secure way to access AI tools without putting the company at risk of extreme cloud computing bills or exposing proprietary data.
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Ackman views $MSFT as having an "impossible to disrupt" position in enterprise software. The base Microsoft 365 package has 450M users at a very low cost per seat, roughly $200 per customer. It would be nearly impossible for an enterprise to replace these various components with 3P vendors, even at twice the price. Microsoft Copilot gives enterprise employees a secure way to access AI tools without putting the company at risk of extreme cloud computing bills or exposing proprietary data.

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$ORCL founder Larry Ellison says next phase of AI will come from combining public models with proprietary enterprise data. These 5 names own some of the deepest proprietary data moats for that transition: 1. $PLTR sits inside some of most sensitive enterprise & government datasets in the world operationalizing AI across classified defense data, regulated healthcare data & Fortune 500 workflows that public models will never train on. 2. $META owns the world’s largest proprietary social graph with 3.5B+ daily active users generating text, image, video & voice data at a scale no competitor can replicate. 3. $GOOGL owns one of deepest consumer data stacks in world with 8.5B+ daily Search queries, 2.7B+ YouTube monthly users, 2B+ Maps users, 1.8B+ Gmail users & 3B+ Android devices feeding Gemini with proprietary data competitors cannot replicate. 4. $RDDT owns one of largest archives of human conversation, opinion & consumer behavior online with 20+ years of proprietary discussion data now being licensed to frontier labs like OpenAI with data licensing revenue could reach $400M+ annually by 2027. 5. $TEM owns one of largest clinical-grade oncology datasets in the world with 45M+ de-identified patient records across genomics, imaging & clinical data that healthcare AI structurally needs.
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$ORCL founder Larry Ellison says next phase of AI will come from combining public models with proprietary enterprise data. These 5 names own some of the deepest proprietary data moats for that transition: 1. $PLTR sits inside some of most sensitive enterprise & government datasets in the world operationalizing AI across classified defense data, regulated healthcare data & Fortune 500 workflows that public models will never train on. 2. $META owns the world’s largest proprietary social graph with 3.5B+ daily active users generating text, image, video & voice data at a scale no competitor can replicate. 3. $GOOGL owns one of deepest consumer data stacks in world with 8.5B+ daily Search queries, 2.7B+ YouTube monthly users, 2B+ Maps users, 1.8B+ Gmail users & 3B+ Android devices feeding Gemini with proprietary data competitors cannot replicate. 4. $RDDT owns one of largest archives of human conversation, opinion & consumer behavior online with 20+ years of proprietary discussion data now being licensed to frontier labs like OpenAI with data licensing revenue could reach $400M+ annually by 2027. 5. $TEM owns one of largest clinical-grade oncology datasets in the world with 45M+ de-identified patient records across genomics, imaging & clinical data that healthcare AI structurally needs.

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$SNOW is becoming data layer for the new agentic AI economy because agents need secure access to proprietary enterprise data and business context before they can do anything useful. That makes Snowflake even more valuable as models get cheaper and more interchangeable because it can bring the best model to the data while AI products like CoCo and CoWork drive a ton more storage, compute and core platform consumption underneath.
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📁 Ben Horowitz, co-founder of Andreessen Horowitz, says AI advantages no longer last. In traditional software, small teams could defend leads for years. In AI, with proprietary data and enough GPUs, money can erase any head start. Innovation no longer protects. Capital competes in real time. Leadership is not defended, it is funded.
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📁 Ben Horowitz, co-founder of Andreessen Horowitz, says AI advantages no longer last. In traditional software, small teams could defend leads for years. In AI, with proprietary data and enough GPUs, money can erase any head start. Innovation no longer protects. Capital competes in real time. Leadership is not defended, it is funded.

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Big pharma just handed the AI industry one of the most important reality checks of 2026 (Save this). david friedberg revealed that Anthropic approached major life sciences companies with a pitch, share your proprietary data, sign an NDA and we will give you early access to a specialized life sciences model and nearly every company they spoke with said no. Here is what these pharma companies understood that many enterprises still have not. A large pharmaceutical company may have spent decades and tens of billions of dollars generating proprietary datasets, clinical trial results, genomic sequences, drug interaction data, compound libraries. That data is the business and the competitive moat that separates them from every other player in the industry lives in those datasets. Handing it to an AI lab in exchange for early access to a model is essentially handing your most valuable asset to a company whose entire business model depends on combining your data with everyone else's and then selling the output back to you and to your competitors. Palantir CEO Alex Karp made this exact point that enterprise leaders are paying for AI tokens that generate no tangible business value while simultaneously surrendering their most sensitive operational data to external providers. He called this transferring a company's alpha, the unique advantage that secures the business directly to a third-party lab. Microsoft CEO Satya Nadella echoed the same concern independently, warning that entire sectors might find their accumulated knowledge commoditized if they do not build their own data and model ownership layers. The structural problem is not unique to pharma but it applies to every enterprise sector. Every time an employee runs a query through a third-party frontier model, proprietary workflows, customer data, and strategic processes pass through infrastructure the enterprise does not control. The data already shows the market moving, Open-source captured 67% of all AI tokens processed in the first half of 2026, up from a fraction of that just twelve months earlier. The performance gap between proprietary frontier models and open-source alternatives has nearly closed, DeepSeek costs approximately 1/36th of GPT-5 for comparable workloads. What pharma figured out and what enterprises across every sector are starting to realize is that the model is not the moat but the data is. And once you hand your data to a model company, you have permanently surrendered the asset that took you decades and billions of dollars to build.
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[MAKING SENSE] You ask AI to summarise a document. Where does the information actually go? Smart glasses can process what you are looking at. But once that data leaves the device, where is it stored, and how secure is it? Watch the full conversation:

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