Andrew Ng published a map of the four most valuable AI engineering skills this weekend, drawn from more than 10,000 postings plus dozens of structured interviews with AI experts, hiring managers and recruiters. Prompting is not on the list yet two of the four, using coding agents and shaping the build, describe work that had no hiring category in 2022, and both are judgment skills rather than technical ones.
Global venture funding reached a record $510 billion in the first half of 2026, with more than 70 percent of second-quarter dollars going to AI companies and starups. OpenAI and Anthropic absorbed $217 billion of that, 43 percent of all startup capital, per Crunchbase. Application-layer founders compete for what remains, in a market where money is plentiful and selection is severe. AI Fund, Ng’s venture studio, holds more than $370 million from Sequoia Capital, NEA, SoftBank and Nikkei, and co-founds companies at pre-seed instead of writing passive checks. Closing a $190 million second fund, Ng credited the model to prototyping costs collapsing under AI-assisted coding.
The agent systems that collapsed those costs are now repricing themselves in ways founders should read closely. Meta introduced Muse Code, a command-line agentic harness, alongside the Muse Spark 1.2 model behind it. Standard access costs $1.25 per million input tokens. A contributor tier costs $0.10, a 92 percent discount, in exchange for Meta’s right to train on the prompts and outputs passing through the agent. That tier caps at 100 requests per minute per team against 3,000 on standard, confining it to individuals and small teams. Those are the teams whose entire product often sits in the repository the agent reads.
On Vals AI’s Finance Agent v2, which assigns models the work of entry-level financial analysts, Muse Spark 1.2 at maximum reasoning ranked first of 45 models at $0.77 per task, ahead of Claude Opus 5 at $5.12. On the broader Vals Index it placed fifth at $0.70 per task, above Claude Opus 4.8 at $7.52. Architecture explains part of that; subagents persist across a session inside isolated worktrees and edit in parallel, Mark Zuckerberg wrote on X earlier this month, and the harness logs every model call and file edit locally so a crashed run resumes instead of restarting.
Meta’s motive is supply. High-quality code is scarce training material, and public crawls such as Hugging Face’s 4.9 trillion-token Stack v3 contain finished code without the reasoning, errors and repairs that produced it. A working harness captures that process. Ng’s counter-position shipped three weeks earlier. OpenWorker, his MIT-licensed desktop agent, runs locally, delivers finished work across everyday tools, and accepts roughly 30 models through the user’s own API key.
What nobody can currently produce is a defensible number for what any of this is worth. METR’s randomized controlled trial found experienced open-source developers took 19 percent longer on real tasks when allowed AI tools, while estimating afterward that the tools had made them 20 percent faster. METR abandoned that design in February 2026 once control groups became impossible to recruit, because developers would no longer work without agents. Its replacement, a transcript analysis of seven staff engineers, measured time savings of 1.5x to 13x, with the author stating plainly that the true productivity multiplier is substantially lower. Founders pitching 10x engineering leverage are making a claim the field cannot yet verify or falsify.
For investors, the four skills convert into diligence questions with checkable answers; which evals gate a deploy, what the error analysis loop looks like when an agent ships a regression, who writes the spec, and whether that person has spoken to a customer this month. Teams that answer crisply are the ones Ng’s data says will compound.
A market that pays for spec-writing judgment and evals discipline values a five-person team differently from a fifteen-person one, and seed rounds already reflect it, with dollars deployed rising while deal counts fall. If small teams begin trading repository access for a 92 percent token discount, the cost of building drops again and the ownership of what gets built turns considerably murkier.