TPS Impact Model | EQTY Lab’s Verifiable Compute Sparks New Insights
Edited By
John Tsoi
A scenario-based TPS impact model is gaining traction as EQTY Lab proposes using Hedera as an auditing layer for its Verifiable Compute stack. This move could redefine transaction load, especially for AI applications, as enterprises are set to adopt this tech from 2026 onward.
Key Insights into Transaction Production
The model outlines how different elements generate transactions per second (TPS). Each unit of AI work—be it an inference, a batch job, or an agent action—contributes to transaction production:
Each job can yield 1 transaction.
Optional steps include identity checks and multi-step workflows.
Scenarios Projecting TPS Growth
Three adoption scenarios highlight potential TPS growth:
Scenario A: Early Enterprise Adoption (2026-2027)
Assumes 100 enterprises conducting 5,000 AI jobs daily each.
Projected transactions: 1,000,000 per day, translating to ~12 TPS.
Interpretation: Noticeable, yet not groundbreaking—fits current network norms.
Scenario B: Scaled Enterprise Adoption (2027-2029)
Expects between 500 to 2,000 enterprises with significantly more jobs.
Predicted transactions: 50 million per day at ~580 TPS.
Interpretation: A substantial load, positioning Hedera as an AI audit backbone.
Scenario C: Agent Economy and Machine Interaction (2030+)
Projects millions of autonomous agents interacting continuously.
Estimated transactions could hit 150 million daily, equating to ~1,740 TPS.
Upside scenario: If agents reach 20 million with 50 daily actions, TPS could skyrocket to ~17,000.
"This could be a game-changer for how auditing in AI is done" — Analysis from industry experts.
Drivers and Challenges
Key Drivers:
Frequency of verifiable events, regulatory intensity, and agent autonomy are crucial for TPS outcomes.
Scalability hinges on whether enterprises choose batch transactions or opt for granular logging.
Important Reality Check:
Even if integrated, enterprises might still limit TPS through transaction practices. More stringent regulations may encourage a shift to frequent, granular logging, which would boost transaction rates.
Implications for Hedera
Positioning as an Audit Layer:
Hedera could become the default audit layer for AI systems—not just another DeFi or retail crypto platform.
Potential Revenue Growth:
As TPS increases:
At 1,000 TPS: Projected revenue could reach ~$3 million annually.
At 10,000 TPS: Anticipated revenue may jump to ~$30 million.
Though the revenue growth from TPS isn't immediate, the strategic positioning signals strong future potential in the evolving crypto landscape.
Key Takeaways
🚀 Early Adoption: 10-50 TPS anticipated in 2026-2027.
📈 Enterprise Growth: Expect 200-1,000 TPS in scaling phases.
🔮 Long-Term Projection: 1,000-10,000+ TPS as agent economies develop.
Interestingly, while TPS metrics are essential, the underlying question remains: Will Hedera solidify its reputation as a trustworthy layer for AI verification?
As this situation evolves, it could bring significant changes to the way businesses interact and audit AI systems.
Forecasting the Path Ahead
As enterprises gradually embrace the TPS impact model, there’s a strong chance that we will see substantial growth in transaction volumes. Starting from early adoption phases in 2026-2027 with estimates of 10-50 TPS, the shift toward larger transaction operations seems inevitable as companies adopt AI technologies. Experts suggest that by 2029, TPS could reach between 200 and 1,000, fueled by both an increase in the number of enterprises and more complex AI functions. The future trajectory may even push the TPS to the upper echelons of 10,000 as agent economies thrive and transaction optimizations are adopted. This evolution hinges on how well regulatory environments adapt to spur greater use of granular logging for compliance and efficiency.
A Relational Echo from History
A reminder of how rapid technological adaptation reshaped industries can be found in how the 19th-century railroad expansion transformed American commerce. As railway lines connected previously isolated towns, businesses evolved to leverage the accessibility, paving the way for both economic boom and novel logistical challenges. Just as railroads turned transportation on its head, the TPS impact model could disrupt AI auditing, pushing businesses to reconsider their operational frameworks to improve efficiency and compliance. The parallels in these transformative moments underscore the potential for systemic change amidst new technological frontiers.