The NIH Funding Process According to John Ioannidis: “Conformity” and “Mediocrity”?

NIH funding is the oxygen of U.S. biomedical researchand the grant review process is the mask that decides who gets to breathe easy.
That’s why when John P. A. Ioannidis (famous for calling out shaky science) suggests the NIH system can reward “conformity” and drift toward
“mediocrity,” people don’t just shrug and go back to pipetting.

But is that critique fair? Is peer review a necessary gatekeeper that protects taxpayer dollars, or a well-meaning machine that sometimes
sands down bold ideas until they look like safe, fundable furniture? Let’s walk through how NIH funding works, what Ioannidis argued,
what critics of the critique say, and what the whole debate reveals about how science is incentivized in real life (where “innovation”
is beloved in speeches and interrogated in spreadsheets).

First, a Quick Map of How NIH Funding Actually Moves

Step 1: You submit a grant application (and a small novel)

Most academic scientists know the drill: specific aims, significance, innovation, approach, investigators, environment, budgets, biosketches,
letters, complianceplus a strong desire to become a houseplant. The most iconic NIH mechanism is the R01, often treated like a career
milestone and, in some departments, a borderline personality trait.

Step 2: Scientific peer review in a study section

NIH applications are evaluated by Scientific Review Groups (study sections) made up of experts in relevant fields. Assigned reviewers
critique applications, the panel discusses a subset in depth, and the group votes.

Reviewers score proposals on a 1–9 scale (1 is best), and discussed applications receive an overall impact score that ultimately lands on a
10–90 scale after panel voting is averaged and multiplied by 10. Along the way, applicants receive a summary statement that can feel like a
performance review written by five different personalities sharing one keyboard.

Step 3: Advisory Council review and programmatic decisions

NIH uses a two-level review system. After the study section, an Institute or Center’s advisory council provides a second-level review and
recommendations. Then the Institute makes funding decisions that consider scientific merit, budgets, priorities, and portfolio balance.

Step 4: Paylines, percentiles, and the “fundable range” reality check

Many Institutes use paylines (often expressed as percentiles) to guide what can be funded. A percentile compares your application to others
reviewed in that study section over recent meetings. Lower percentile = better relative ranking. In lean years, the “fundable” slice can get
brutally thinturning excellent science into an Olympic sport where “almost” is the most common outcome.

What Ioannidis Is Really Critiquing When He Says “Conformity”

The incentive problem: reviewers must predict the future

NIH peer review asks humans to forecast which projects will produce sustained, meaningful impact. That’s hard even when you’re psychic.
It’s harder when you’re accountable for being “responsible” with public money. The system naturally rewards proposals that feel:
feasible, well-supported, methodologically tidy, and low-drama.

In practice, that can tilt reviews toward projects with lots of preliminary data, familiar methods, and incremental stepsbecause those are
easier to evaluate and easier to defend. A truly weird-but-brilliant idea may read like genius to one reviewer and like a lab fire to another.
The safest consensus often wins.

“Innovation” is a criterion, but “Approach” often drives the mood

NIH includes “Innovation” in review criteria, and many panels genuinely value novelty. But reviewers also have to judge rigor and feasibility.
If a project is innovative but the approach feels risky, reviewers may score it as “interesting but not ready.” Translation:
“Come back when the future has already happened.”

This is where Ioannidis’ conformity argument bites: if “fundable” effectively means “already half-proven,” then the system is less a bet on
discovery and more a reward for having already done a chunk of the workoften favoring established labs with resources to generate
that preliminary evidence.

Scarcity makes everyone more conservative (including nice people)

When paylines tighten, panels face painful tradeoffs. Even strong proposals get triaged or nudged down the ranking. In that environment,
“risk” becomes synonymous with “the thing we can’t afford to be wrong about.” And because reviewers are scientists, they can build a very
convincing argument for why their caution is actually wisdom. (Scientists are excellent at arguments. Have you ever been to a lab meeting?)

The Nicholson–Ioannidis Spark: Do Influential Scientists Go Unfunded?

The headline claim that started the fight

A high-profile critique associated with Ioannidis argued that systems like NIH peer review can “encourage conformity if not mediocrity”
and may miss some highly influential researchers. The argument gained attention because it didn’t just complain in general termsit tried
to use publication influence as a reality check: if someone’s work is demonstrably shaping a field, why aren’t they reliably funded?

Why that claim resonates with working scientists

The “conformity” critique rings familiar because many researchers can recall:

• Bold projects labeled “too ambitious” or “premature”
• Panels requesting more preliminary data that requires… funding
• Reviews that reward perfect storytelling over messy, real discovery
• Resubmissions where the new version is less exciting but more fundable

Over time, this can create a cultural adaptation: applicants learn the unwritten rules. You don’t kill innovationyou house-train it.
You make it wear a tie. You put it in a “modular budget.” You promise it won’t break anything.

Critics of the critique: influence isn’t the same as “should be NIH-funded”

Skeptics push back with several reasonable points:

• Selection and measurement issues: “Influential” is hard to define. Citations can reflect many thingsfield size, trends,
  even controversy.
• Alternative funding: Some top scientists are funded by foundations, philanthropy, industry collaborations, or HHMI-style
  models that operate outside NIH.
• Timing: Breakthrough papers may come from earlier funding, internal institutional support, or a temporary pivot that isn’t
  a stable long-term NIH project.
• Different missions: NIH Institutes have program priorities. A brilliant paper doesn’t automatically align with a particular
  Institute’s current portfolio needs.

In other words: yes, peer review can miss greatness, but “not NIH-funded right now” doesn’t always mean “unrecognized” or “undeserving.”
It may mean “funded differently” or “working at the edge of how NIH categories and mechanisms are built.”

Where “Mediocrity” Sneaks In (Even When Everyone Is Trying Hard)

Group decision-making loves the middle

Panels are made of experts with different tastes and risk tolerances. Group scoring tends to compress extremes. The proposal that everyone
agrees is “pretty strong” can beat the proposal that half the room thinks is “spectacular” and the other half thinks is “a fever dream.”

That’s not because reviewers are villains. It’s because consensus is easiest around the safe middle. In tight funding climates, the
safe middle can become the default.

Reputational signals can become shortcuts

NIH reviews are designed to focus on science, but humans are humans. Reviewers may (consciously or not) weigh signals like track record,
institution, network effects, or whether the story “feels like it belongs.” That can make it harder for outsiders, unconventional thinkers,
or early-career investigators to sell unusual ideaseven when those ideas are correct and valuable.

Proposal polish can outcompete scientific daring

A well-written, well-structured application can earn higher confidence. That’s fair to a point: clarity matters. But if the process over-rewards
grant-writing performance, applicants may optimize for reviewability rather than discovery. The result can be a system that
quietly trains scientists to present research like a perfectly rehearsed TED Talkwhen the underlying work is more like improv theater with
pipettes.

NIH Has Heard These ComplaintsAnd It Has Tried to Respond

High-risk, high-reward programs for the “weird but promising”

NIH has created pathways meant to support riskier, more creative workprograms that explicitly acknowledge that traditional peer review can
struggle with early-stage, high-uncertainty ideas. Some mechanisms reduce the demand for heavy preliminary data and emphasize potential impact.
These programs are competitive and limited in scale, but they are an institutional admission that: yes, the usual system has blind spots.

Simplifying peer review to reduce bias and focus the core questions

NIH has also worked on how peer review criteria are structured, including efforts that reorganize criteria to emphasize the importance of the
research, the rigor and feasibility of the approach, and whether the team and environment can deliver. The goal is to improve clarity and reduce
reputational biasbecause even subtle bias can translate into real differences when paylines are tight.

Early-stage investigator policies and portfolio balancing

NIH Institutes often use policies to support early-stage investigators and to maintain a balanced research portfolio. That’s important for
scientific “ecosystems,” where you need both steady incremental progress and occasional moonshots. A portfolio that funds only safe projects
becomes stale. A portfolio that funds only moonshots becomes chaos. NIH tries to hedgesometimes imperfectlyagainst both failure modes.

So… Is Ioannidis Right?

He’s right about the pressure toward conservatism

When resources are scarce and reviewers must justify decisions, systems tend to favor projects that look achievable and defensible.
That can create a bias toward incrementalism, and incrementalism can look like “mediocrity” if you were hoping for fireworks.

He may overstate the simplicity of “mavericks vs. teams”

Many major advances are built from years of cumulative workoften by teams, across labs, across methods. NIH is also tasked with keeping
a large and complex biomedical engine running, not just hunting for cinematic breakthroughs. Sometimes “safe” science is exactly what turns
a promising idea into something reproducible, translatable, and useful.

The most honest answer: the system is good at some things and bad at others

NIH peer review is relatively strong at identifying proposals that are rigorous, plausible, and well-matched to existing knowledge.
It is weaker at judging ideas that look wrong until they’re proven rightbecause, by definition, those ideas don’t fit what we already believe.
That’s the paradox: the more transformative an idea is, the less it resembles a “responsible” proposal on day one.

Practical Takeaways: How to Be Innovative Without Scaring the Panel

Make the risky part small and the payoff big

A common winning strategy is to isolate risk into a specific aim or phase, and show multiple ways the project can still produce valuable results.
Reviewers don’t hate innovationthey hate uncertainty without airbags.

Turn “wild idea” into “testable plan”

Innovation sells best when it’s paired with concrete methods, clear benchmarks, and contingency plans. If you can demonstrate that your team
will learn something important even if the bold hypothesis fails, you reduce the fear that the grant will become an expensive science-themed
regret.

Write for two audiences: experts and tired humans

Study sections include specialists and adjacent experts. Your application should satisfy technical scrutiny but also remain readable. A reviewer
who understands your logic is more likely to trust your risk. A reviewer who’s confused is more likely to “protect the score.”

Experiences Researchers Commonly Describe in the NIH Funding World (Approx. )

Researchers often describe the NIH process as a mix of rigor, ritual, and emotional cardio. One common experience is the “great idea / bad timing”
phenomenon. A team may have a genuinely novel approachsay, a new way to model disease mechanisms or a computational method that reshapes how data
are interpretedbut the summary statement comes back with a familiar refrain: “promising, but needs more preliminary data.” The science isn’t rejected
as wrong. It’s rejected as early. The sting is that “early” is often exactly when funding is most needed.

Another frequently described moment is the day scores appear in eRA Commons. Applicants refresh the page like it’s a concert ticket drop. If the score
is strong, there’s reliefbut also a new uncertainty: “Will this be within payline for this Institute this year?” If the score is borderline, the mood
turns into strategic arithmetic. Investigators start comparing percentile ranges, looking at Institute paylines, and trying to interpret “enthusiasm”
between the lines of reviewer language. A phrase like “high impact potential” can feel like a compliment until it’s paired with “concerns about feasibility,”
which is grant-speak for “we loved it, but we got scared.”

Resubmission experiences are practically a genre. Many investigators describe learning, over time, how to translate critique into a new draft that feels
simultaneously more cautious and more persuasive. They add pilot data, tighten aims, simplify the narrative, and write response letters that are equal parts
scientific argument and polite diplomacy. Some say the second version becomes “less exciting, more fundable,” like a movie trailer recut to please every focus
group at once. When it works, it’s a triumph. When it doesn’t, it can feel like you’ve done twice the work to be told you should have done the work first.

Scientists who serve on study sections often report a revealing shift in perspective. On the applicant side, a single critique can feel personal. On the panel
side, it looks different: reviewers are juggling many applications, limited time, conflict rules, and the pressure of making fair comparisons across diverse
topics. People arguesometimes passionatelyabout what “innovation” should mean and how much risk is acceptable. In tighter funding climates, panelists may
become more defensive of “sure bets,” not because they dislike creativity, but because they feel accountable for outcomes and frustrated by scarcity.

The most consistent experience researchers describe is that NIH peer review is not simply a judgment of scientific truthit’s a judgment of confidence.
The proposals that win are often those that make reviewers feel safe saying “yes.” That doesn’t automatically produce mediocrity. But it can produce a quiet
evolutionary pressure: applicants adapt. Over years, a community can learn to write grants that sound more alike, even when the underlying science is diverse.
That’s the heart of the Ioannidis concern: not that NIH reviewers want conformity, but that the system can unintentionally reward it.

Conclusion: Conformity, Mediocrity, or a System Under Stress?

Ioannidis’ critique lands because it names a tension at the core of public research funding: society wants breakthroughs, but the funding process must be
accountable, predictable, and defensible. Peer review is designed to identify strong science, yet it can struggle with ideas that are too new to look safe.

The best way to read the “conformity and mediocrity” argument is not as an accusation that NIH funds bad science. It’s a warning about incentives:
when funding is scarce and evaluation is consensus-based, novelty can be discountednot because it lacks value, but because it lacks certainty.
The challenge for NIH (and the scientific community) is to keep the rigor while making room for the kind of boldness that changes fields.