David Halley (@TheDavidHalley) header
David Halley (@TheDavidHalley) profile picture
Joined November 2023
Photo by David Halley (@TheDavidHalley)Photo by David Halley (@TheDavidHalley)Photo by David Halley (@TheDavidHalley)Photo by David Halley (@TheDavidHalley)Photo by David Halley (@TheDavidHalley)Photo by David Halley (@TheDavidHalley)Photo by David Halley (@TheDavidHalley)Photo by David Halley (@TheDavidHalley)Photo by David Halley (@TheDavidHalley)Photo by David Halley (@TheDavidHalley)
David Halley retweeted
Honestly, I think most enterprises won't really make the transition, and they'll just get gradually replaced by new, leaner entrants. The models could get twice as smart, and most enterprises still wouldn't benefit if they can't access their data. For example, stuff locked inside spreadsheets or employees' heads.
Dwarkesh is 100% correct that the main thing holding AIs back from being able to completely substitute for human workers, in software at least, is that it doesn't have access to the full business context, and its ability to learn 'on the job' is far more limited than its trained-in capabilities. But giving it full context doesn't require any technical leaps, just organisational ones. Basically you 'provide it full context' by making sure it can access all company data and systems, including conversations (by recording them). At the moment 'learning on the job' is limited to building 'memories' in text files (at least for claude), which feed into its context. These ARE very useful, but are a pretty basic technology. But I'm sure it won't take massive advances to convert these text file memories into something more effective. nytimes.com/video/opinion/10…
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David Halley retweeted
Dwarkesh is 100% correct that the main thing holding AIs back from being able to completely substitute for human workers, in software at least, is that it doesn't have access to the full business context, and its ability to learn 'on the job' is far more limited than its trained-in capabilities. But giving it full context doesn't require any technical leaps, just organisational ones. Basically you 'provide it full context' by making sure it can access all company data and systems, including conversations (by recording them). At the moment 'learning on the job' is limited to building 'memories' in text files (at least for claude), which feed into its context. These ARE very useful, but are a pretty basic technology. But I'm sure it won't take massive advances to convert these text file memories into something more effective. nytimes.com/video/opinion/10…
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Companies House and related websites must be the worst designed UI/UX in history. Every minor change takes 2 minutes to do but 2 hours+ to navigate to the correct place. I am sure they spent ridiculous sums building this rubbish.
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David Halley retweeted
Databricks CEO @alighodsi went off on @a16z pod about enterprise AI adoption: "They're just so far behind in the adoption curve of actually automating things and getting value out of this stuff." Ali says most companies are still just using chatbots. There's hardly any agentic transformation. Why is that? "The models are smart enough, but they just don't have the context that exists inside of any organization." "They have not been in every meeting. They don't know what's in everybody's heads. They don't know all the processes." "If you just fused that and gave that context into the AI models...there's so much productivity gains you could get for any organization on the planet." Context Creation is the biggest opportunity in AI right now.
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Does anyone else think the massive money we have spent on R&D tax credits is a total waste of billions? Accountants and lenders take a huge cut of it and it really isn't helpful particularly to start ups as paid in arrears. Need to use more direct assistance if any and ones which support all industries. EG USA has SBA loans
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@Burgess_Hodgson charged me so much for this service as apparently it is too difficult to navigate. These vampire companies do not help the economy.
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David Halley retweeted
Marc Andreessen (the guy who called it when “software ate the world”) just pointed out what AI is doing to coding next. “Everyone assumes AI coding means fewer hours… or leaving the profession. But almost everyone I know is working more hours.” “There’s a new term in the Valley: the ‘AI vampire.’ You’re up all night AI coding because you’re so productive you can’t shut it off.” “The top AI coders make $50M a year. They’ve found the philosopher’s stone.” “Every company has a thousand projects they’ve wanted to build but never had the bandwidth. Now they can. This isn’t a blip—it’s going to intensify.” PS: If this was useful, like + repost this tweet and follow @AiEvolutio58513 for the latest AI news. See you in the next one:
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David Halley retweeted
⚡️Karp is naming the real enterprise AI split. The frontier labs sold intelligence as a universal product. Enterprises are discovering that raw model access is not enough, and in sensitive environments it can become a sovereignty problem. The issue is not only performance. The issue is control. Who owns the model behavior? Who owns the weights? Who owns the prompts? Who owns the data exhaust? Who owns the workflow knowledge? Who owns the company’s operational alpha after the model has observed it? That is the center of the rant. Karp is saying the frontier model layer is trying to become a tax on every enterprise’s private knowledge. Companies pay token fees, hand over context, expose workflows, and then fear the model provider can learn from, replicate, commoditize, or eventually compete with the very business it serves. Even if the lab says it will not do that, the trust problem remains because the customer does not control the full stack. That is why Palantir plus Nvidia matters. The pitch is: keep compute, models, ontology, data, and operational logic under customer control. Use models as replaceable components. Do not let OpenAI, Anthropic, or any single frontier lab become the owner of the enterprise brain. The strongest line is “they want to know they own the means of production.” That is the whole architecture war. In consumer AI, users tolerate dependence. In enterprise, battlefield, manufacturing, healthcare, intelligence, energy, and regulated finance, dependence is unacceptable. The model cannot be a black box that absorbs secret workflows and charges tokens forever. The model has to be embedded inside a controlled operational layer where the customer owns the data, governs the model, controls compute, switches providers, and preserves institutional alpha. This is extremely bullish for Palantir’s ontology thesis. Karp is basically saying the value is not just the model. The value is the application layer that turns models into usable, safe, governed action inside real institutions. That is exactly Palantir’s lane. Frontier labs provide cognition. Nvidia provides compute. Palantir provides the operating structure that makes cognition useful in environments where mistakes, leaks, hallucinations, IP loss, or data exposure are existential. That is also why he is attacking token economics. Token billing works when AI is a tool. It becomes offensive when AI is sold as transformation but the customer cannot see value, cannot control the stack, and suspects the vendor is capturing the business’s hidden knowledge. Enterprises do not want to rent intelligence from a potential future competitor. They want sovereign capability. The AI bubble point is more precise than “AI is fake.” Karp is not saying AI is fake. He is saying the frontier-lab business model may be mispriced, overtrusted, and structurally wrong for serious enterprise deployment. Compute plus application layer plus model is real. Raw model subscription sold as enterprise transformation is weaker. That is the knife. So the likely outcome is not enterprise AI demand collapsing. The likely outcome is enterprise AI spend migrating away from generic chatbot/token subscriptions and toward controlled stacks: private compute, open or controllable models, Nvidia infrastructure, secure application layers, ontology, governance, auditability, and domain-specific deployment. That is bullish for Nvidia. Bullish for Palantir. Bullish for sovereign AI stacks. Bullish for open models when paired with secure deployment. Bearish for frontier labs that assume they can own the customer relationship, the model layer, the data interface, and the economics forever. The deeper geopolitical read: battlefield AI cannot be outsourced to consensus Silicon Valley. Karp is saying American warfighting, critical infrastructure, and industrial command systems need AI, but they cannot be dependent on model providers whose governance, safety ideology, product incentives, and data posture are misaligned with sovereign use. That is why he keeps saying Department of War, Ukraine, Israel, critical infrastructure. He is framing Palantir as the patriotic enterprise operating layer between uncontrolled frontier labs and national power. The cleanest read: The AI stack is splitting. Model layer: powerful but commoditizing and politically distrusted. Compute layer: scarce, profitable, strategic. Ontology/application layer: where enterprise value, trust, control, and workflow ownership live. Karp is arguing that the model companies overplayed their hand by acting like the model owns the future. Enterprise customers are realizing the future belongs to whoever controls the operational layer around the model. That is exactly Palantir’s thesis. And he is probably right.
here is the entirety of Palantir CEO Alex Karp's televised nervous breakdown this morning on CNBC
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David Halley retweeted
Replying to @Zero_4
We could have chosen to follow Sweden's non-lockdown example for COVID. We could have avoided running up a big deficit in the second Blair term, and could have properly regulated the banks. We could have not implemented sanctions on Russia which caused us more harm than them. All self-inflicted.
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The next decade of enterprise AI won't be won by the biggest model. It'll be won by the leaders who keep their data, compound their learning, and answer to their values. That's what we're building at Yentra. yentra.ai
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Most AI tools pattern-match. Yentra reasons. Ontological engineering means your AI actually understands the structure of your business — the entities, relationships, and rules that make your domain yours. The result: answers you can trust, decisions you can defend, and automation that compounds instead of breaks. That's the Yentra difference. → yentra.ai
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Our new site is live at yentra.ai We aim to take companies to a place beyond a few copilot or Claude accounts and to full ai enablement, starting with ourselves. Any CEO’s who want to look to the future for their company please DM.
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Agentic advertising is gaining traction. pubmatic.com/news/pubmatics-…
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South Korea has now overtaken the United Kingdom to become the world’s eighth-largest stock market. The total market capitalization of Korean equities has risen more than 45% in 2026 alone to roughly $4.04 trillion, while the UK has barely moved, rising about 3% to $3.99 trillion (en.macromicro.me/series/4227…). As recently as the end of 2024, the UK market was about twice the size of South Korea’s. Capital markets are essential for a healthy economy, we are losing ours. The government is silent.
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The agentic move in advertising continues. It is the logical path and CIQ stands to benefit. CTV and online video is the future of media, and understanding the contents of millions of hours of video is the only true way to advertise there compared to linear tv (defined programs, easy context eg Coronation Street) and websites (first party data is enough) pubmatic.com/news/pubmatics-…
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Connected IQ welcome @PubMatic as our newest data partner! As the leading SSP and trailblazer in agentic advertising with their groundbreaking AgenticOS, PubMatic is redefining programmatic with AI agents that autonomously plan, execute, and optimize campaigns across premium supply. This partnership supercharges our AI-powered contextual video targeting with richer data, smarter insights, and next-level performance for brands and agencies. The future of Connected TV agentic advertising is here #AgenticAI #Programmatic #CTV #ContextualTargeting
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Can you imagine Rachel Reeves having a clue or anything to say at this? Understanding all the second order events or even the futures curve. Probably the first chancellor i would have said this about.
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