Jeremiah Owyang

The Enterprise Capital Gate (Part 2) Part 1 argued that enterprise AI projects share the characteristics of venture investments but are funded like plant expansions.... https://lnkd.in/gTaYVGyb #aistrategy #aistakeholdersqueeze #enterpriseAI

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Institutions don’t fail all at once. They fail when design can’t keep up with change. In Davos this week, I’ll be joining the inaugural gathering of the Institutional Research Network, a new initiative convened by The Digital Economist. The focus is not another set of conversations but the patient work of connecting research, markets, and governance so ideas can take institutional form and hold under real-world pressure. Some initiatives are about visibility. This one is about durability. #Davos2026 #InstitutionalDesign #IRN #TheDigitalEconomist

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The difference between building the next Tesla and becoming the next Solyndra? Quantifying a technology's long term unit economics with data, AKA understanding its learning curves. Breaking the thesis that FOAK is new, when it is just new combinations of common building blocks - pumps, compressors, vessels, power electronics, control systems. Christian Okoye's new company, Occam-Edge.com is building a framework for analyzing budget risks and learning curves, how costs decline as production scales, is giving founders, investors, and policymakers a common language for what has long been discussed in vague generalities. NextGen Industry Group, Generate, Electric Hydrogen, DCVC https://lnkd.in/gHUqmH7K

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It feels almost poetic: the year that began with market-wide panic after DeepSeek R1’s surprise January drop is ending with the equally disruptive December launch of DeepSeek V3.2. In January, R1 cracked open the idea that aggressively scaled RL - not just larger and larger pre-training runs - can push a model into frontier-level cognitive behavior at a radically lower cost. Now, months later, V3.2 bookends the year with an even louder message: open-source is no longer trailing by quarters, it’s operating on a near-synchronous innovation clock. And in some benchmarks, it’s outright leading. We now have a publicly available model with gold-medal performance across IMO 2025, CMO 2025, IOI 2025, and ICPC-level tasks. No Western lab has open-sourced anything in that tier. It’s early. Independent benchmarking will come, along with the usual debates about framing, cherry-picking, and reproducibility. But you don’t need perfect clarity to see the shape of things. DeepSeek’s story has always been about discipline. While the frontier race spirals into billion-dollar training runs and million-token contexts, the team has stayed focused on a narrower, almost stubborn question: how far can you push intelligence per dollar. This model delivers frontier grade performance at a fraction of the cost (30x cheaper than Gemini 3 Pro, 50-75% cheaper than prior Deepseek models). Defending against a cost advantage is easy if you can point to a performance gap. But if a competitor matches your performance and undercuts your price, the defense collapses. That’s the corner V3.2 pushes frontier labs toward. Most of the world - nations, small enterprises, scrappy startups - will never train trillion-parameter models. And crucially, they don’t need to. They need models that are: - cheap to run - fine-tunable on commodity hardware - good enough to support agents, search augmentation, and code workflows - predictable on inference cost V3.2 sits precisely at that intersection: high-enough capability, low-enough cost. This is why a growing number of Silicon Valley startups are building on Chinese open-weight models. The logic is straightforward: they can download the weights, fine-tune locally, deploy on smaller hardware, avoid vendor lock-in and keep the price of inference predictable. For a startup with limited runway, this matters more than a marginal accuracy edge. DeepSeek’s trajectory transforms “Chinese open-source” from a curiosity into a default path for cost-sensitive builders. The innovation frontier is being pulled sideways, not upward. Today: ▪️ U.S. frontier labs chase maximal capability - climbing vertically up the y-axis. ▪️ Chinese labs chase maximal cost-performance - scaling horizontally across the x-axis. The model with the highest peak will win prestige. But the model with the widest base will win global adoption. DeepSeek V3.2 shows that efficiency is not a consolation prize, it is a competitive moat.

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Very exciting to see the future of light based AI infrastructure becoming very very real. The next big wave is starting.

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