Date: January 20, 2026
Submitted by: Accelerate Science Now
Point of Contact: Joshua New, Director of Policy, SeedAI
Email: josh@seedai.org

2) What, if any, substantive comparative advantage (as compared to standard grants and other existing NSF programs) could the NSF Tech Labs program model provide in efforts to accelerate and advance U.S. competitiveness – either across various key technologies or within a specific technology focus area? 

Traditional NSF grants excel at supporting discrete research projects within individual laboratories or institutions. However, this model can make it difficult to sustain cross-functional execution, scale operational capabilities, or maintain shared resources beyond the initial research phase. Many critical translational challenges, from establishing interoperable standards to building shared testbeds, require sustained, coordinated effort that extends beyond typical grant cycles.

The Tech Labs program can address these limitations by:

Supporting integrated, execution-capable teams. Tech Labs should fund full-time, cross-disciplinary teams that include engineering, science, data, operations, and translation expertise. These teams can move quickly from concept to implementation, maintaining operational continuity across the entire translational pipeline.

Enabling pre-competitive public goods. Tech Labs are uniquely positioned to work on shared resources that benefit the entire ecosystem: validated datasets, reference implementations, interoperable standards, shared testbeds, and open protocols. These public goods are essential for U.S. competitiveness but are systematically underinvested by both traditional grants and private capital.

Providing operational flexibility. Milestone-based agreements allow teams to iterate and pivot based on evidence and user needs. This flexibility is particularly valuable for translational work, where requirements often emerge through deployment and engagement with end users.

4) What program design choices would contribute to the success of the Tech Labs mechanism? What program design choices would present potential barriers? 

NSF should design Tech Labs to maximize operational speed and translational impact while avoiding administrative burdens that would undermine the program's core advantages. Design features to consider include:

Explicit eligibility for execution-oriented, independent teams. To maximize speed and impact, NSF should explicitly allow applications from pre-formed, execution-capable teams with demonstrated ability to deliver multi-year translational outcomes. These teams may be housed in universities, nonprofit research organizations, or other eligible entities. Restricting eligibility too narrowly to traditional academic structures would limit participation by teams specifically designed for rapid translation and deployment.

Milestone-based agreements with operational flexibility. Tech Labs should preserve the flexibility of milestone-based mechanisms that enable rapid iteration, contracting, and hiring. This includes the ability to use Other Transaction (OT) authority or similar structures where appropriate. This flexibility represents a core comparative advantage of the program and should not be eroded by heavy administrative overhead or requirements that mirror traditional grant compliance.

Incentives for pre-competitive standards, shared infrastructure, and broad adoption. The program should explicitly reward outcomes that are broadly usable across the ecosystem: validated methods, interoperable standards, shared reference datasets and materials, and comprehensive documentation. These deliverables should be designed for maximum accessibility while still enabling downstream commercialization pathways where appropriate.

Built-in support for IP strategy, research security, and commercialization planning. Many high-potential teams may not have mature internal capacity for research security planning, intellectual property strategy, licensing negotiations, and partner contracting. NSF can significantly increase program success by providing templates, technical assistance, and early-phase support so teams can execute quickly and responsibly. This could include access to technology transfer professionals, model agreements, and research security guidance.

Prioritizing rapid, open dissemination of results. Tech Labs should be evaluated on the accessibility and usefulness of their outputs, not publication counts. NSF should encourage teams to release methods, datasets, negative results, and technical documentation through preprints, open repositories, and direct documentation as soon as quality and IP considerations allow. Traditional peer-reviewed publication can remain part of a dissemination strategy, but should not be treated as a prerequisite for demonstrating progress or impact. The goal is to accelerate adoption and enable others to build on Tech Labs work, which often requires faster and more practical forms of knowledge sharing than traditional publication timelines permit.

A. Which types of teams and organizations should be considered eligible to apply for the NSF Tech Labs program? What restrictions on team eligibility should be in place to maximize speed and ensure novel impact?

NSF should consider eligibility for teams and organizations with demonstrated ability to execute translational programs at speed. NSF should also avoid restrictions that inadvertently require applicants to conform to traditional academic structures or incentives. Eligibility should be based on execution capability, operational readiness, and measurable outcome plans.

B. Is the proposed timeline for Phase 0 (9 months), Phase 1 (24 months), and Phase 2 (24+ months) well-calibrated to support the program’s strategic objective of achieving high impact, accelerated outcomes? If not, what adjustments should be made and why?

The proposed timeline of Phase 0 (9 months), Phase 1 (24 months), and Phase 2 (24+ months) provides a directionally appropriate framework for most translational efforts. However, NSF should recognize that different technology domains and translational challenges may require different timelines.

NSF should consider allowing teams to propose phase-specific timelines justified by their technical approach and translational pathway, rather than imposing uniform durations across all teams. Additionally, extension mechanisms should be tied to milestone achievement and demonstrated progress rather than arbitrary time limits.

C. How should IP rights be structured to support maximum success and impact?

IP should be structured to maximize adoption and ecosystem growth while preserving incentives for commercialization where appropriate. A balanced approach should include:

Clear ownership and licensing frameworks from the outset. Requiring transparent IP and commercialization plans early in Phase 0 helps avoid disputes and ensures all parties understand their rights before significant joint development occurs. These agreements should address equitable sharing of IP developed collaboratively and include mechanisms for dispute resolution.

Tiered openness based on competitive positioning. Pre-competitive methods, standards, and reference implementations should favor non-exclusive licensing and broadly accessible terms to maximize ecosystem benefit. Exclusive arrangements should be permitted only when necessary to secure private follow-on investment, and should be time-limited, tied to performance milestones, and linked to demonstrable public benefit.

Preservation of commercialization pathways. Industry partners must have sufficient freedom to further develop and commercialize resulting IP. Without this assurance, private sector engagement will be limited, undermining the program's translation objectives.

D. What degree of independence is optimal to ensure the flexibility, freedom, and speed required for the Tech Labs initiative? How should NSF define team independence? 

The program should define team independence based on a team’s operational capability rather than institutional affiliation. Institutional affiliation could of course be a limitation on team independence, but should not be treated as a constraint by default – institutions should be free to experiment with new models for research team governance that enable them to satisfy the requirements of the Tech Labs program. 

E. How should funding be allotted to each proposed Tech Labs? What factors – for example: team size, team expertise, infrastructure needs, growth trajectory – should NSF consider to determine appropriate funding amounts to support successful Tech Labs teams? 

NSF should determine appropriate funding levels based on:

Technical scope and complexity. What is the scale of the translational challenge? Does it require multi-site coordination or integration across multiple technology platforms?

Infrastructure and equipment needs. Does the team need to build, maintain, or access specialized facilities, instruments, or computational resources? Are there significant costs associated with testbeds, manufacturing access, or specialized equipment?

Team size and composition. What mix of scientific, engineering, operational, and administrative personnel is required to execute the translational program? How many full-time equivalents are needed across different phases?

Partnership and coordination costs. Does the team need to coordinate with multiple external partners, conduct multi-site deployments, or engage extensively with end-user communities? What resources are required for these coordination activities?

Deployment and maintenance requirements. What resources are needed for training, documentation, user support, and ongoing infrastructure maintenance beyond initial development?

Growth trajectory and scaling pathway. How quickly must the team scale up operations, and what resources are required at different stages of the translational pathway?

Rather than establishing fixed funding tiers, NSF should allow teams to propose budgets justified by their specific technical approach, subject to evaluation for reasonableness and alignment with expected milestone outcomes.

5) What opportunities do you see for synergy with research and development efforts that are or could be funded by industry or philanthropic organizations? What partnership structure would allow Tech Labs to leverage federal and private support for maximum benefit? 

Tech Labs could be a powerful catalyst for leveraging non-federal resources, particularly for work that sits between early research and commercial scale. Key synergy opportunities include:

Philanthropic co-funding for pre-competitive infrastructure and workforce programs. Philanthropic organizations can support shared facilities, reference resources, training pipelines, and community infrastructure that increase the national return on federal investment.

Industry cost-share and pilot deployments. Industry partners can provide in-kind support such as access to manufacturing lines, testbeds, datasets, instruments, or procurement commitments that accelerate validation and adoption.

Public-private testbeds and federated infrastructure networks. Tech Labs could coordinate shared testbeds across universities, national labs, and independent entities to accelerate progress across TRLs and reduce duplication.

Partnership structures that preserve public benefit while enabling commercialization. NSF should encourage standardized partnership agreements and clear governance to ensure that public investments produce broadly accessible outcomes while enabling downstream commercialization.

Embargoes can enable stronger public-private collaboration. Industry partners are more likely to provide in-kind support (testbeds, manufacturing access, datasets, pilots) when there is a clear, time-bounded release plan that protects sensitive information and preserves patentability. NSF should allow Tech Labs to use limited embargo periods with transparent timelines and default-to-release policies to maximize both adoption and public benefit.

A successful partnership model would allow Tech Labs to function as a trusted integrator across stakeholders—aligning technical milestones with real-world constraints and adoption needs.

Accelerate Science Now is a non-partisan coalition of leaders in industry, academia, civil society, and the research community, charged with igniting a new era of rapid scientific discovery and delivering the benefits to the American people. Accelerate Science Now members include: The Align Foundation, Amazon Web Services (AWS), Anthropic, Arizona State University, Arm, Astera, Bit Biome, Black Tech Street, Broad Institute, Caltech, Carnegie Mellon University, Center for Data Innovation, Cohere, Computing Research Association (CRA), Convergent Research, Digit Bio, Emerald Cloud Lab, Energy Sciences Coalition, Engineering Biology Research Consortium (EBRC), Federation of American Scientists (FAS), Foundation for American Innovation, FutureHouse, Ginkgo Bioworks, Good Science Project, Google DeepMind, Hewlett Packard Enterprise (HPE), Horizon Institute for Public Service, Inclusive Abundance, Information Technology Industry Council  (ITI), Institute for AI Policy and Strategy (IAPS), Institute of Electrical and Electronics Engineers (IEEE), Intel, Klyne, Lehigh University, Medra, Meridian, Meta, Microsoft, National Applied AI Consortium (NAAIC), New Mexico A.I. Labs, New Mexico Artificial Intelligence Consortium Academia (NMAIC-Academia), New Mexico State University, NobleReach Foundation, OpenMined, Potato, RenPhil, Rice University, Roadrunner Venture Studios, Roboflow, Samsung, SeedAI, Software Information Industry Association (SIIA), Syntensor, Systems & Technology Research (STR), Tetsuwan, Transfyr, UbiQD, University of California Berkeley, University of California Irvine, University of Albany, University of Florida, University of Tennessee-Knoxville, University of Wisconsin-Madison, VentureWell

Accelerate Science Now is led by SeedAI, a non-profit, nonpartisan organization working at the forefront of artificial intelligence policy and governance.

These recommendations do not necessarily represent or reflect the official positions of all coalition members. This document should be understood as a collaborative effort to advance shared objectives, while acknowledging the diversity of viewpoints within our coalition.