When it comes to Boundless, don't just think of it as a "tool for scaling blockchain"—its ambitions are much greater! In the future, it aims to become a global, decentralized "verifiable computing network"; simply put, it's a "truth machine" in the digital world: whether it’s cloud computing, AI reasoning, or data collaboration, as long as it is verified by it, it can ensure that there is "no fraud, no error." This is not just a minor issue, but an attempt to fundamentally change our understanding of "digital trust."
1. Not only serving blockchain, but also helping Web2 and AI "prove their innocence"
One of the most impressive aspects of Boundless is its ability to extend beyond the blockchain sphere, helping traditional internet (Web2) and AI solve the significant issue of 'credibility'—after all, whether it is cloud services or AI, they often resemble a 'black box', and you never know if the other party is secretly manipulating things.
1. Make cloud computing 'afraid to lie'.
Now everyone uses services like Amazon AWS and Google Cloud. After assigning computing tasks, they can only passively wait for results—if the cloud service provider cuts corners or makes mistakes, you cannot verify anything. But with Boundless, it is different: you can require the cloud service provider to include a ZK proof generated by Boundless (a type of cryptographic technology that can prove 'I did not deceive you' without disclosing details) when returning the result. With this proof, it is equivalent to placing a 'verification seal' on the computation result, leaving the cloud service provider no opportunity for malice or error.
For example, the company uses cloud services to calculate financial data. In the past, there was a fear of making mistakes that would affect the reports. Now, with the proof from Boundless, it can be directly confirmed that 'the data is calculated using the correct formula', eliminating the need for repeated manual verification and saving a lot of effort.
2. Give AI reasoning a 'safety net'
Now, when using AI like ChatGPT and Wenxin Yiyan, you can ask a question, and it gives an answer. However, you cannot confirm whether this answer genuinely comes from 'the model it claims'. What if the service provider uses a low-spec model to cut costs or fabricates an answer? You wouldn't know.
Boundless can solve this problem: you can require the AI service provider to provide proof that 'this answer is indeed calculated using the XX model, based on the input data you provided'. For example, doctors using AI for diagnosis can confirm that 'the AI diagnosis result is based on the correct medical model and patient data', rather than a guess. This is crucial for the application of AI in serious fields like healthcare and law.
2. Aspiring to be the 'Trust CPU' of the 'World Computer'
We often hear people refer to the 'World Computer', not implying that there is a supercomputer, but rather a computational network composed of countless nodes that can collaborate globally. However, the biggest problem with this network is 'lack of trust'—how do you know other nodes are not miscalculating?
What Boundless aims to do is to be the core of this 'World Computer'—not to provide computational power but to offer 'verifiable computational ability'. Regardless of which node executes a task on the network, it must go through Boundless verification to prove 'my computation is correct' before the result can be shared.
To put it simply, in the past, the nodes in the 'World Computer' were 'each working independently, without trust'. Boundless acts like an 'invigilator', overseeing each node's calculation process to ensure no one cheats, so that the final aggregated results are trustworthy. With this 'Trust CPU', the 'World Computer' can truly be realized; otherwise, it remains just a concept.
3. Combine with AI to create 'Privacy-Preserving Machine Learning'
What is the biggest fear in AI training nowadays? Data leakage. For instance, a few hospitals want to jointly train a cancer diagnosis model; each hospital has patient data, but no one dares to share their data—fearing violations of privacy regulations. As a result, this leads to 'data islands', and the model can never be trained properly.
Boundless's privacy protection capabilities can precisely solve this problem, giving rise to 'Privacy-Preserving Machine Learning': each hospital does not need to share raw data but can compute the 'gradients' (key data for training the model) on its own local data. Then, it uses Boundless to generate a proof that 'my gradient is calculated correctly, without fabrication'. Next, the encrypted gradient and proof are sent to the aggregator, which uses this data to update the model.
As a result, hospitals do not leak patient privacy and can collaboratively make the model more accurate—for example, previously a single hospital's cancer data was limited, yielding a model accuracy of 70%. After joint training, it can increase to 90%, which represents a life-saving advancement for patients.
4. The greater the ambition, the greater the challenge: it is not just a technical issue but also a philosophical one.
Boundless aims to become a 'Truth Machine', but this path will undoubtedly be difficult. In addition to technical challenges, there are also numerous 'longstanding issues' that need to be resolved.
1. Such an impressive system, who should govern it?
How to govern an infrastructure that can define global digital trust? Who decides the rules for upgrades? Is it the holders of Boundless tokens who vote? Or do the core developers decide? Or should we consider the opinions of the user community?
This is no small matter—if we need to update verification technology in the future, a wrong decision could impact countless services that rely on it globally. Moreover, it involves the 'decentralization philosophy': how much power should be given to the community, and how to prevent 'tyranny of the majority'? There are still no answers.
2. Could there be a new 'computational hegemony'?
If Boundless truly becomes the industry standard, and all computations must rely on it for verification, will the organization controlling its node resources become the new 'dominant power'? For instance, if a company controls 80% of the proof nodes, it can decide who passes verification and who does not, thus returning to the old issue of centralization.
How can we prevent this situation? Is it by limiting the number of nodes for a single organization? Or by using a more distributed node arrangement? This is also an 'eternal topic' that Boundless must face—after all, decentralization is not a one-time solution; it requires continuous vigilance against new centralization risks.
Finally: Boundless's journey is to transform 'trust' from 'human reliance' to 'technological reliance'.
The starting point of Boundless is quite simple: to help blockchain scale. However, its ultimate goal is to redefine 'trust'—previously, when we trusted something, it was either through people (e.g., trusting banks not to abscond with funds) or through systems (e.g., contracts, regulations), but these all have vulnerabilities.
What Boundless aims to do is turn 'trust' into a purely technical attribute: guaranteed by mathematical formulas and code. Regardless of who it is, as long as they comply with the rules, they can be trusted; as soon as they violate the rules, they will be verified. There is no need to gauge people's expressions or rely on institutional constraints.
This journey will undoubtedly be long and difficult, requiring technological breakthroughs, governance improvements, and persuading global organizations and users to accept this new trust model. But once it is achieved, the impact will be significant—whether in cross-border transactions, AI healthcare, or global data collaboration, there will no longer be worries about 'trustworthiness'.
Boundless has just begun, but it has already shown the possibility of a 'more trustworthy digital world'. As for whether it can truly become a 'Truth Machine', we can watch slowly. At least, the direction it is heading is a future that many people are looking forward to.
