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NIH Scraps Traditional Paylines, Upending How Grants Get Funded in 2026

February 21, 2026 · 4 min read

Claire Cummings

NIH Ditches Paylines, Redrawing the Map for Research Funding

Score-watching just became a relic at the National Institutes of Health. As of the January 2026 council round, NIH’s decades-old practice of using clear payline cutoffs to decide who gets funded is officially over. From now on, every R01 and research grant will be evaluated in a broader context—the score is still important, but so are factors like alignment with NIH priorities, applicant career stage, the geography of awards, and how much NIH funding an investigator already holds.

For thousands of scientists, institutions, and nonprofit leaders, this isn’t just a bureaucratic tweak. It’s a sea change that impacts how applicants plan submissions, interpret reviews, and advocate for their work at every stage of the grant process.

Why NIH Is Leaving Paylines Behind—and What’s Taking Their Place

Until now, most NIH Institutes and Centers set an annual or rolling "payline"—a percentile or priority score threshold below which grants were almost certain to be funded. This system offered clarity but little flexibility. Critics have long argued that hard cutoffs lock out high-potential projects when budgets get squeezed and create perverse incentives for risk-averse science.

The new, payline-free approach announced in late 2025 aims to give NIH leadership greater latitude. Instead of automatically deferring to scores, Institute and Center directors will weigh each proposal’s peer-review results alongside strategic priorities, investigator diversity, geographic distribution, and the applicant's existing NIH portfolio. NIH says this will better support high-impact, mission-driven science and allow for a more equitable funding landscape.

This mirrors recent moves at other agencies, including the National Science Foundation, which has also downplayed strict funding cutoffs in favor of holistic programmatic judgment.

Predictability Out, Uncertainty In: What Grant Seekers Face Now

For researchers, nonprofit staff, and small businesses counting on NIH support, the practical effect is an immediate surge in uncertainty:

Rethinking Your Application Strategy Under the New Rules

For those preparing spring and summer 2026 NIH submissions—or awaiting word on pending applications—adaptation is now essential. Here’s what this means in practical terms:

Where the Funding Landscape Goes From Here

With payline transparency gone, community vigilance is critical. Will this shift actually yield more innovative, diverse, and impactful science—or just move confusion behind closed doors? The next few funding cycles will tell.

Applicants should expect learning curve hiccups as Institutes refine internal processes and communicate shifting priorities. Early signals from NIH leadership suggest there will be more public reporting on factors guiding award decisions and a push to avoid bias and arbitrariness—but specifics are scant so far.

Some science organizations are already calling for greater clarity on how the new rubric works in practice. If you have a pending application or plan to submit soon, keep in close touch with program contacts and track each Institute’s funding news and recent council meeting minutes (example: December 2025 NICHD meeting).

It’s also an ideal time for new investigators and those from underrepresented backgrounds to engage with NIH offices focused on workforce development—these applicants may see new openings under the revised system.

As the dust settles, it’s crucial for applicants to move from a score-driven mindset toward one that treats NIH funding as a dynamic, evolving process—one that rewards strategic engagement as much as technical merit.

Staying agile and informed has never been more important, and platforms like Granted AI can help researchers and organizations keep track of these fast-changing grant policies as they chart a smart path forward.

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NIH Scraps Traditional Paylines, Upending How Grants Get Funded in 2026 | Granted AI