The Missing Middle Layer: Why AI Research Breaks Down Without Standardized Human Expertise and Model Access Infrastructure

Every serious AI project eventually runs into the same wall, and it is rarely the model. Research teams at universities and frontier labs can design a promising system in weeks, then lose months finding qualified people to label and judge its outputs, and more time still wiring up access to the handful of commercial models they want to compare it against. The market that supplies that labor shows the strain. The global data annotation tools market was valued at $1.69 billion in 2025 and is on track to reach $14.26 billion by 2034, growing close to 27% a year. The spending keeps climbing, yet the work behind it stays fragmented, costly, and hard to reproduce from one lab to the next.

Zihan Wang, a Forbes Technology Council Member, has spent his career on that overlooked layer between raw models and usable research. As co-founder and chief research officer of a Silicon Valley AI data infrastructure company, and the founder of an open research foundation that publishes evaluation benchmarks with academic partners, he works on the parts of the pipeline most teams treat as plumbing: where the data comes from and how competing models get compared on equal terms. His central claim is that the real bottleneck in AI research has moved off the algorithm and onto the infrastructure that feeds it.

The Human Layer Nobody Wants to Own

Modern AI systems still depend on human judgment at the points automated checks cannot reach: writing reference answers, grading model outputs, catching the mistakes a scoring script will happily wave through. That expertise is badly scattered. A lab studying medical reasoning needs annotators who understand clinical context; a team evaluating code agents needs working engineers, not generalists with a rubric. Most groups source these people ad hoc, through personal contacts or short-term contractors, which leaves quality uneven and nearly impossible to reproduce when the next project begins.

Wang's answer was to make that human layer a managed system instead of a scramble. He built a global network of vetted experts that research teams can draw on directly, covering everything from image and video to code, math, and specialized professional domains. People are sourced, screened, trained, and quality-checked before they touch a research task, with onboarding delivered through structured courses rather than a one-page brief. The network now spans more than 5,000 qualified experts, and it has become the layer that frontier labs and university programs at Stanford, MIT, and Harvard reach for when they need domain-qualified human judgment at scale.

“The hard part of evaluation was never the model. It is finding people who understand the task and will judge it the same way twice,” Zihan Wang says. “The moment you treat human expertise as infrastructure instead of a favor you call in, the whole process gets faster and a lot more honest.”

Fifty Models, Fifty Integrations

The model side of research carries its own tax. Comparing systems fairly means running the same test across many of them, but every provider ships a different interface, different limits, and different quirks, so one benchmark can burn weeks of engineering before it produces a single number. Concentration makes it worse. More than 90% of notable frontier AI models released in 2025 came from industry labs, each with its own access terms, which leaves academic researchers integrating and babysitting infrastructure they had no hand in building. For a small group without dedicated engineers, that overhead alone can decide which experiments ever get run.

To strip out that overhead, Wang built a unified access layer that puts more than 50 frontier models behind one standardized interface, including the major families from the largest labs. A researcher writes against a single API and routes the same prompt to dozens of models without rebuilding anything, while a normalization layer absorbs the formatting and behavior differences between providers. Setup that used to take weeks now takes hours. The same layer holds responses stable under load, which matters when an evaluation fires thousands of parallel calls and one flaky endpoint can quietly poison the results.

“Researchers should be testing ideas, not maintaining 10 different SDKs,” Wang explains. “When access stops being a project of its own, people finally run the comparisons they care about instead of the ones that were simply easier to set up.”

Proof That Holds Up in Public

Infrastructure earns its keep only through the work it lets other people reproduce. A benchmark built on inconsistent labeling or an unstable model pipeline yields numbers that look exact and signify almost nothing, because no outside team can reconstruct how they were reached. The signal that counts is published work, run on documented data under stated conditions, that survives scrutiny from people with no stake in the outcome. A strong score inside a private setup proves very little.

That standard runs through the research these platforms have supported. Wang co-authored a peer-reviewed benchmark that tests whether AI agents can carry what they learn across separate sessions and use it to make better decisions later, built on human-crafted tasks with university research collaborators. Its core finding is blunt: agents that score near the top on older memory tests collapse once they have to act on what they supposedly remembered, exposing a gap the field had been measuring around for years. The benchmark works only because the tasks behind it were written and checked by qualified people and the agents were run through one consistent model interface, the two layers his infrastructure exists to provide.

“A result that only works in your own lab is a marketing slide,” Wang notes. “We publish the benchmarks in the open because that is the only way to learn whether they hold up, and the only fair test of whether the infrastructure underneath them is any good.”

Where It Breaks

Building this kind of infrastructure is mostly a long fight with failure modes that stay hidden until scale. Recruiting experts in bulk is easy; holding the bar steady while doing it is not, because quality slips quietly long before the scores visibly drift. The model side has a mirror problem: the harder an interface is pushed, the more its weakest provider shows, and a single slow or inconsistent endpoint under heavy load can skew an entire evaluation without anyone noticing.

Wang's teams treat both as engineering problems rather than hiring ones. On the human side, every expert moves through the same pipeline of sourcing, vetting, training, and audited quality control, so the standard does not ride on who happened to pick up the work. On the model side, the access layer runs continuous checks on latency and consistency across providers and routes around any that degrade, so one unstable model cannot contaminate a batch of results. The lesson that keeps repeating, in his experience, is that quality matters far more than raw volume, and that the systems worth trusting are the ones designed to fail safely.

“Most teams underestimate how fast quality erodes when you scale people, and how much one unreliable model can wreck a benchmark,” Wang observes. “We put more effort into catching those failures early than into adding capacity, because a number you cannot trust is worse than having no number at all.”

The Cost of Building It Alone

The stakes land hardest on the institutions with the least room to spare. Frontier development has turned spectacularly expensive: training one leading model now reaches nine figures, with the compute behind a single flagship system, Gemini Ultra, estimated at roughly $191 million. Most universities and smaller labs will never train at that level, yet they are still expected to evaluate and build on the models that come out of it. The supporting infrastructure of expert review and multi-model access carries its own steep price when every group rebuilds it alone, and shared infrastructure is often what keeps those teams in the research at all.

This is the argument Wang keeps making for treating human expertise and model access as shared infrastructure rather than private overhead. When a smaller lab can reach the same vetted experts and the same standardized model interface as a frontier team, the thing that decides who gets to do serious work shifts back toward ideas and away from budgets. His plan is to keep widening that access, adding models and expert domains and pushing the onboarding and quality systems harder, on the bet that the next stretch of progress will come less from any single larger model and more from many more teams being able to test rigorously against the ones that already exist.

“The labs training the biggest models will keep making the headlines, and they have earned them,” Wang reflects. “But most of the useful research over the next few years will be done by people who could never afford to build any of this on their own. Giving them the same ground to stand on is the part of the work I care about most.”