ChainGPT's robot mascot. IMAGE: CHAINGPT
A step forward that could influence how smart contracts are designed and verified.
Updated
November 27, 2025 3:26 PM
ChainGPT's robot mascot. IMAGE: CHAINGPT
A new collaboration between ChainGPT, an AI company specialising in blockchain development tools and Secret Network, a privacy-focused blockchain platform, is redefining how developers can safely build smart contracts with artificial intelligence. Together, they’ve achieved a major industry first: an AI model trained exclusively to write and audit Solidity code is now running inside a Trusted Execution Environment (TEE). For the blockchain ecosystem, this marks a turning point in how AI, privacy and on-chain development can work together.
For years, smart-contract developers have faced a trade-off. AI assistants could speed up coding and security reviews, but only if developers uploaded their most sensitive source code to external servers. That meant exposing intellectual property, confidential logic and even potential vulnerabilities. In an industry where trust is everything, this risk held many teams back from using AI at all.
ChainGPT’s Solidity-LLM aims to solve that problem. It is a specialised large language model trained on over 650,000 curated Solidity contracts, giving it a deep understanding of how real smart contracts are structured, optimised and secured. And now, by running inside SecretVM, the Confidential Virtual Machine that powers Secret Network’s encrypted compute layer, the model can assist developers without ever revealing their code to outside parties.
“Confidential computing is no longer an abstract concept,” said Luke Bowman, COO of the Secret Network Foundation. “We've shown that you can run a complex AI model, purpose-built for Solidity, inside a fully encrypted environment and that every inference can be verified on-chain. This is a real milestone for both privacy and decentralised infrastructure”.
SecretVM makes this workflow possible by using hardware-backed encryption to protect all data while computations take place. Developers don’t interact with the underlying hardware or cryptography. Instead, they simply work inside a private, sealed environment where their code stays invisible to everyone except them—even node operators. For the first time, developers can generate, test and analyse smart contracts with AI while keeping every detail confidential.
This shift opens new possibilities for the broader blockchain community. Developers gain a private coding partner that can streamline contract logic or catch vulnerabilities without risking leaks. Auditors can rely on AI-assisted analysis while keeping sensitive audit material protected. Enterprises working in finance, healthcare or governance finally have a path to adopt AI-driven blockchain automation without raising compliance concerns. Even decentralised organisations can run smart-contract agents that make decisions privately, without exposing internal logic on a public chain.
The system also supports secure model training and fine-tuning on encrypted datasets. This enables collaborative AI development without forcing anyone to share raw data—a meaningful step toward decentralised and privacy-preserving AI at scale.
By combining specialised AI with confidential computing, ChainGPT and Secret Network are shifting the trust model of on-chain development. Instead of relying on centralised cloud AI services, developers now have a verifiable, encrypted environment where they keep full control of their code, their data and their workflow. It’s a practical solution to one of blockchain’s biggest challenges: using powerful AI tools without sacrificing privacy.
As the technology evolves, the roadmap includes confidential model fine-tuning, multi-agent AI systems and cross-chain use cases. But the core advancement is already clear: developers now have a way to use AI for smart contract development that is fast, private and verifiable—without compromising the security standards that decentralised systems rely on.
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Bindwell is testing a simple idea: use AI to design smarter, more targeted pesticides built for today’s farming challenges.
Updated
November 28, 2025 4:18 PM
Researcher tending seedlings in a laboratory environment. PHOTO: FREEPIK
Bindwell, a San Francisco–based ag-tech startup using AI to design new pesticide molecules, has raised US$6 million in seed funding, co-led by General Catalyst and A Capital, with participation from SV Angel and Y Combinator founder Paul Graham. The round will help the company expand its lab in San Carlos, hire more technical talent and advance its first pesticide candidates toward validation.
Even as pesticide use has doubled over the last 30 years, farmers still lose up to 40% of global crops to pests and disease. The core issue is resistance: pests are adapting faster than the industry can update its tools. As a result, farmers often rely on larger amounts of the same outdated chemicals, even as they deliver diminishing returns.
Meanwhile, innovation in the agrochemical sector has slowed, leaving the industry struggling to keep up with rapidly evolving pests. This is the gap Bindwell is targeting. Instead of updating old chemicals, the company uses AI to find completely new compounds designed for today’s pests and farming conditions.
This vision is made even more striking by the people leading it. Bindwell was founded by 18-year-old Tyler Rose and 19-year-old Navvye Anand, who met at the Wolfram Summer Research Program in 2023. Both had deep ties to agriculture — Rose in China and Anand in India — witnessing up close how pest outbreaks and chemical dependence burdened farmers.
Filling the gap in today’s pesticide pipeline, Bindwell created an AI system that can design and evaluate new molecules long before they hit the lab. It starts with Foldwell, the company’s protein-structure model, which helps map the shapes of pest proteins so scientists know where a molecule should bind. Then comes PLAPT, which can scan through every known synthesized compound in just a few hours to see which ones might actually work. For biopesticides, they use APPT, a model tuned to spot protein-to-protein interactions and shown to outperform existing tools on industry benchmarks.
Bindwell isn’t selling AI tools. Instead, the company develops the molecules itself and licenses them to major agrochemical players. Owning the full discovery process lets the team bake in safety, selectivity and environmental considerations from day one. It also allows Bindwell to plug directly into the pipelines that produce commercial pesticides — just with a fundamentally different engine powering the science.
At present, the team is now testing its first AI-generated candidates in its San Carlos lab and is in early talks with established pesticide manufacturers about potential licensing deals. For Rose and Anand, the long-term vision is simple: create pest control that works without repeating the mistakes of the last half-century. As they put it, the goal is not to escalate chemical use but to design molecules that are more precise, less harmful and resilient against resistance from the start.