AI Venture Velocity Challenge

All teams must maintain a structured Experiment Log documenting their learning throughout the competition period.

After submitting their Venture Snapshot, all applicants will receive a link to the official Experiment Log submission form.

Each experiment entry must include:

• Hypothesis tested
• Why the assumption matters
• Experiment design
• AI used (if applicable)
• Evidence collected
• Decision made
• Impact on venture direction
• Entries are time-stamped and serve as the official record of learning velocity.

Advancement at each stage is determined by rubric scoring and documented Experiment Log evidence demonstrating continued progress on the most important assumptions using AI to accelerate learning and decision-making.

Submissions should clearly establish both the significance of the opportunity and the key assumptions that must be tested for the venture to succeed. This replaces a traditional written business plan. MVP is not required at the initial submission.

Please direct any inquiries with the subject line “AI Venture Velocity Challenge” to AIVVC@mays.tamu.edu.

• All individuals and teams must be students currently enrolled at an accredited U.S. university.
• Students from all majors are eligible to participate.
• Entries must leverage AI technologies as a core component of the venture.
• Individuals and teams must demonstrate ownership or full rights to any technology or intellectual property used.
• Ventures cannot have generated more than $5 million in gross revenue in any 12-month period prior to Jan. 1, 2026.

Problem Significance and Opportunity Quality (25%)

Problem Importance:

Does the venture address a meaningful, clearly defined problem with material business or societal relevance?

Customer Understanding:

Is there evidence that the problem is real and validated with target customers?

Market Opportunity:

Is the opportunity attractive in size and scope, with a clear target segment and value proposition?

Learning Velocity and Experimentation Rigor (30%)

Hypothesis Clarity:

Has the team identified the most important assumptions underlying the venture?

Experiment Design Quality:

Are experiments thoughtfully designed to test the highest-risk assumptions?

Evidence-Based Decisions:

Does the team use data and results to inform pivots, refinements, or strategic direction?

Speed of Iteration:

How quickly and systematically does the team test, learn, and iterate?

AI Integration & Leverage (20%)

AI as a Learning Accelerator:

How effectively are AI tools used to accelerate research, experimentation, prototyping, analysis, or discovery?

AI as a Solution Enabler (if applicable):

Does AI meaningfully enhance the product or service?

Intentionality and Appropriateness:

Is AI applied thoughtfully and strategically rather than superficially?

Evidence of Venture Progress (10%)

Progress Relative to Starting Point:

Has the venture made measurable progress during the competition window?

Uncertainty Reduction:

Have critical risks and assumptions been clarified or resolved?

Forward Momentum:

Is the venture demonstrating increasing clarity and direction over time?

Responsible Impact & Ethical Design (10%)

Societal Contribution:

Does the venture address important challenges and contribute positively to customers or communities?

Responsible AI Practices:

Does the team demonstrate awareness of ethical considerations such as privacy, bias mitigation, transparency, and governance?

Long-Term Impact Awareness:

Has the team considered potential unintended consequences and responsible scaling?

Adaptive Execution & Coachability (5%)

Responsiveness to Evidence:

Does the team adjust when data contradicts assumptions?

Decision Discipline:

Is decision-making clear and coherent?

Team Agility:

Does the team work cohesively and respond constructively to feedback?

Scoring Guidelines

Each criterion will be scored on a scale of 1 to 5:

1 – Poor: Element absent or inadequately addressed.
2 – Fair: Present but underdeveloped or lacking supporting evidence.
3 – Good: Adequately addressed with reasonable documentation.
4 – Very Good: Well-developed and evidence-based.
5 – Excellent: Exceptional, rigorously supported by documented experimentation and learning