TheCrypto-Seer

Don't Let Them Drink Your Milkshake: youtube.com/watch?v=a5d9BrLN… Since the advent of TCP/IP as a communications method, we've seen a torrential flood of increase in recorded entropy, at a rate that is not possible to be encompassed via human reckoning. Human generated messages alone have exponentially multiplied decade after decade, with electronic communication only serving as a means to facilitate what seems like an inevitable trend. The advent of LLMs, and their rampant adoption cycle, has been even faster in their race for users. teapaw.org/sama/status/1756… Synthetic data generation is only going to get faster from here, with machine to machine communication cycles taking up more and more of the bandwidth we have. There are many quandaries that arise from this, namely: + Can I trust who I am talking to is who they say they are? +How do I achieve privacy in a world that is continually scraped for content and training data? +Who benefits from this data I generate on a moment by moment basis, and how can I claim this back? +How does AI not just recursively degenerate into incestuous feedback flows, taking it further and further away from truths grounded in reality? +How do we understand AI, from its basic principles, before AGI/ASI/"The Singularity" becomes an inevitability? +How do we, proponents of the decentralized web, form vast distributed compute networks to rival centralized systems and the models that run on them? The list goes on and on here. Millions of years of data are now being generated in an instant, and, more than ever, we need essential context to frame and defang the torrential outpour of content that has come to dominate our lives. In this I believe we have to now broach the topic of Verifiable Credentials (or VCs for short.) VCs have the potential to act as the new base layer for the advent of computational data markets, trusted data markets, and reputation markets, codifying and contextualizing the means by which we act on and through the internet, and thereby in the algorithmic mechanisms (not exclusive to LLMs) we are participating in the generation of. If data is the new oil, then seizing the means of production (issuing VCs) allows anyone to participate in the Oil Rush of their own information. AIs are data hungry entities, constantly crying out for new inputs and computation to digest said inputs into their corpus. Additionally, the future of governance of said models are being fought out through massive movements in market valuations of startups, publicly traded companies, and even entities like $TAO (Bittensor) which deploy novel tokenomic methods to foster a more decentralized approach. Regardless of the form of these LLMs, LAMs, or any other myriad of forms of Generative AI (GenAI) that will emerge down the line, they are in desperate need of high quality data. This is where $CHEQ comes in. $CHEQ has built the primitives of credential issuance, revocation registries, and credential payment rails, in order for users to manage the complete lifecycle of verifiable data streams, erecting guardrails around the lifeblood of GenAI. Simply put, this means we can trust where data is coming from, and the creators of that data can be recognized and remunerated when it is used. $CHEQ has already established replicable, sustainable business models around reusable KYC/AML credentials, and can do the same thing for the Web3 GenAI data market in three key areas: 1. Verifiable Hardware; this enables decentralized distributed compute networks to better incentivize verified tiers of miners based on GPU and hardware specs. This is critical for incentivizing edge compute to join AI Web3 compute networks. Think how CHEQ can power and verify hardware for miners to be incentivized by networks like Bittensor, Akash, and Gensyn. 2. Verifiable Hardware Monitoring; VCs created to verify geo-location of mining rigs, uptime, performance metrics. Assuring four 9s levels of dependability is essential for going toe to toe with the likes of AWS. No less is acceptable. By enabling projects to build “clusters” of connected machines, we can minimize latency and maximize throughout for training ai models. This is critical for distributed computing networks in Web3 to crystalize around Web3 GenAI models. 3. Verifiable Datasets; The creation of reputation markets around quality attestations of datasets allows for the ambient creation and training of decentralized GenAI models. Each model can train on data that mitigates against hallucinations, copyright issues, and privacy leaks, all the while honing the quality of the output of responses to user queries. The need for these sorts of decentralized utilities has never been greater, and only $CHEQ is well positioned to fulfill these market needs. Speaking as someone who has been in $QNT since the beginning, I can testify that this utility is just as important as bringing CBDCs into the world. Infact, as seen with the release of EIDAS 2.0 (which $CHEQ is deeply embedded within), digital identity primitives are a prerequisite toward the adoption of CBDC. I have no doubt that some groundbreaking announcements, partnerships, and technical developments are coming down the pipe for this project, and it has cemented itself as a key holding of mine in the coming bullrun. Doubt this project at your peril; a public utility of this scale, mediated by such a team, will be a beast to behold.