Daniel Kornev

The funniest thing about Apple finally launching a touchscreen MacBook Pro isn't that Windows laptops got there first (that's low hanging fruit). It's watching a company reverse course on something it resisted for years, knowing how fiercely people defended that position along the way. There’s a much bigger lesson here about tech fandom. Some thoughts on yesterday's Apple announcement: 👇 windowscentral.com/hardware/…

I joined @GoogleDeepMind to develop a research program on the economics of AGI. Today, together with Andrew Koh (@andrewjkoh) and Shane Legg (@ShaneLegg), we are incredibly excited to release The Economics of Intelligence, the first in a series of projects from our group on how AGI will change the economy. How do capability improvements transform the economy, and when? In this project, we define thresholds of economic capabilities, e.g., minimal AGI, based on how they transform the economy. We then track how the economy–market structure, employment, and wages–changes during the transition across capability regimes. We have seen exponential increases in what AI agents can do but the broader economy seems almost remarkably unchanged. Part of the reason is that the solo AI agent is the wrong economic object; economic transformation is an equilibrium outcome where organizations compete. Looking at how intelligence functions within organizations, transformation will depend on when two key capability thresholds are crossed: competence (gains on individual skills, e.g., coding, math) and coordination (gains on ability of agents to cooperate and work together). We show that what economic transformation looks like depends on whether AI becomes supercompetent, supercooperative, or both—the regime of artificial superintelligence (ASI). Tracking this transition shows why the real visible transformation may come from improvements in coordination—a capability most benchmarks don’t yet track. Past a certain threshold, AI capabilities can break the Coasian ceiling on firm size and lead to an economy that looks drastically different than the one we see today. Two main takeaways: 1. To understand the path of transformation, we should be tracking agentic coordination more closely. 2. The impact of AI will likely show up in wages before market structure or employment. We are really excited about this paper. Excited to hear people’s thoughts. Link: institute.deepmind.com/essay…

The Platonic Representation Hypothesis is now a Theorem ◼️ Train JEPAs separately, on any modalities: they provably learn the same representation up to one rotation. The result? A 109M JEPA-Text model that aligns with vision models better than 72B LLMs. w/ @ylecun 🧵

Agree. The founders I back are out of their depth all the time. The great ones don't know more than everyone else. They just trust they can figure it out when things get hard (h/t @drgurner).

BCG Report: IT Firms Lack AI Talent to Capture $200B Opportunity (a) BCG evaluated 50,000 engineer profiles (b) Only 10% doing Applied AI work at IT firms (c) Urgent need to build Applied AI workforce (d) INSIGHTS for IT engineers:. Market Is Ready; IT Firms Aren’t a. Boston Consulting Group (BCG) has published an extensive report on how IT companies can build Applied AI workforce from the people they already employ. b. Opportunity: BCG estimates in the next 4 years AI will add $400B of new revenue streams for IT services globally, but wipe out $200B of old revenue streams. So, the net gain is $400-$200 = $200B. c. Problem: IT firms claim to offer “AI-first” services, but in reality they have not built an AI delivery model. These companies only have a fraction of talent needed to capture the vast opportunity. d. IT companies tag their workforce as “AI engineers,” but in reality only 10-15% do Applied AI work. The rest merely use AI to do old work faster. At AI-native firms (“tech companies”) 90-95% do real Applied AI work. Infused AI vs. Applied AI a. Infused AI: It uses AI tools to do existing work faster. An IT engineer’s task is still fundamentally software development, which is now done faster with AI tools. b. Applied AI: It involves building and operating AI agents that do the work. Examples: enterprise AI agents, agentic workflow automation, production AI platforms, and AI systems for end-to-end business processes. c. Infused AI eliminates the need of humans for repetitive tasks. If previously 3 engineers worked for 8 hours to develop and test a feature, now only 1 engineer can do the same task. So, two-third of the workforce is gone. d. Applied AI engineers create new sources of growth & revenue for clients by building and operating AI agents: new services, new products, new customer problems that can be solved economically, and new ways to increase productivity. e. IT companies lack the capacity in Applied AI. BCG says time is running out, and market opportunity will be grabbed by firms that move fast to build massive “Applied AI” talent. Applied AI Job Roles BCG has identified 40 distinct Applied AI roles. Four categories, which IT engineers and IT firms should target are: a. Forward-Deployed Engineer (FDE): Works closely with the client to design & deliver a working solution. b. AI Engineer/AI Architect: Builds and scales enterprise AI and agentic systems. c. Semantic Engineer: Develops data representations, vector embeddings, and knowledge structures that AI agents can use. d. AI Ops Engineer: Monitors AI systems for performance, accuracy, drift, and bias. In AI-native firms, 45% of the workforce comprises forward-deployed engineers (FDEs). In traditional IT firms, the share is less than 5%. 5 Questions IT Firms Must Answer BCG wants IT companies to answer these five questions right now: a. Has the company selected the AI use cases (agentic systems) it intends to build to solve client problems? Otherwise it may train people in general AI skills without knowing where those skills will be used. b. Is there consistent role architecture across the company? A common architecture makes job roles, skill requirements, and career paths easier to compare across teams. c. Has the company made deliberate hiring and training decisions for all 40 Applied AI roles? A blanket hiring and training policy won’t sustain for long. d. Has the company assessed its existing workforce properly? Accurate assessment prevents the company from overestimating or underestimating its current capabilities. e. Can the company’s pay and career structures survive competition from AI-native firms? Otherwise the company will lose existing talent or fail to attract top talent. Endpiece: The Clock Is Ticking BCG report’s final message is that building AI capability takes time. Winners will be those IT firms that begin early and enable their employees to develop skills and experience before the opportunity matures toward 2030.

I am doing a project about elderly vibecoders If you are a vibecoder and over 25 please DM

Palantir cofounder @JTLonsdale: "Everyone now wants FDEs—everyone in the Valley." Creator of the FDE Palantir CTO @ssankar: " It really started with Karp's intuition." "That part did have foresight—we are not going to succeed as a company by just having salespeople interacting with the customer. There's a lot more to learn from the customer. You need the substance provider to be co-located with them." "You had to take wildly more ownership." "You have to have really technical people sitting with the customer, understanding and figuring out the right product, have the ability to mutate and ship that product, and then anneal those lessons back into the core product." "FDEs are kind of hackers. Their dopamine hit in life comes from solving the problem." Via @AmOptimistShow

Every religion in history has promised miracles and warned of the apocalypse. Anthropic is the first to file both with the SEC.

This is a LP letter that we recently wrote with our lessons on spotting talent from a decade of investing at the frontier. docsend.com/view/p7wm6mezzbq… The most common thing you hear about early stage investing is that "It is 90% about the founder". We agree but have always been unsatisfied by the lack of falsifiability, specificity, and repeatability of that statement. What are the dimensions of excellence? How do they translate into companies? It was a pleasure to collaborate with @gopalkraman on this.

After a decade of investing, I've been writing down a few notes for my team. I've no idea if there is broader interest in this stuff. Nor if it's particularly useful. But posting for those who have an interest in venture and appreciate perspectives on it.

Elon Musk on why his companies move faster than anyone else. "I'm constantly addressing the limiting factor. Whatever the limiting factor is on speed, I'm going to tackle that. If capital is the limiting factor, I'll solve for capital. If it's not the limiting factor, I'll solve for something else." He then said something most managers never figure out: "If something is going really well and making good progress, there's no point in me spending time on it.The irony is if something's going really well, they don't see much of me. But if something is the limiting factor, they'll see a lot of me." He spends his time entirely on whatever is blocking the next step. Not on what's interesting or what he's best at. But on whatever is the bottleneck right now. Most leaders do the opposite... They gravitate toward what they're comfortable with and away from the hard problem.

Ambassador applications for @spacexsi / @cursor_ai are open Details + apply here: anysphere.typeform.com/space…