Verification-Centric Chains, Common Knowledge Machines, and Intents
Verification-centric blockchains outsource the computation/generation of transactions to off-chain agents ("solvers").
But a verification-centric chain is only as good as its verifier (the VM). You can judge a verification-centric chain's programming model by asking these questions:
1. What verification conditions can be expressed?
2. How easily can a solver understand the verification conditions, so they can treat those as a goal/problem to solve?
3. Are solvers afforded with some level of interoperability, or must we forsake trustless interop at the compute layer just because it lives off-chain (in contrast to compute-centric chains like Ethereum, where any smart contract can at least attempt to interact with any other)?
UTXO-based chains are the OG verification-centric chain. eUTXO / generalized utxo chains improve on the first of the three properties above: expressiveness.
In generalized UTXO chains, the kinds of verification conditions that can be expressed are comparable to the kinds of computations that can be expressed in a compute-centric chain.
But two challenges remain:
1. Semantic opacity: those verification conditions are not expressed in a format that a solver can easily understand: a developer has to manually implement a new solver that can understand and compute over the various verification programs it wishes to integrate with.
2. Reduced interoperability: Computation happens off-chain. So "integrating" with a protocol means generating transactions that fulfill that protocol's verification conditions within the context of a larger transaction. But that means that every protocol & app dev has to re-implement the business logic of every protocol & app with which they wish to integrate.
@khalani_network addresses these remaining challenges.
1. Semantic transparency: We create a new programming model that is semantically transparent. This means that the semantics of protocols & applications are discoverable and understandable by solvers. The most obvious way to do this is use a declarative model. The problem with a declarative model is that it is too rigid: it adds transparency at the expense of expressiveness, limiting the kinds of systems that it can support. Adding imperative features would address this, but it would do so at the expense of trustlessness and semantic transparency.
2. Intent-based interoperability: With a sufficiently expressive, semantically transparent model, intents become the most natural primitive for trustless coordination between off-chain agents because they can establish a shared & agreed upon set of expectations and understanding of how to interact! I.e., they can establish common knowledge, which is critical to facilitating coordination of any kind.
Khalani's VM, the Common Knowledge Machine, provides the means for solvers to coordinate around shared goals on-the-fly. Sort of like machine-to-machine MOU's, lol.
This has many consequences, but I'll describe just one for now (this is an X post, not a whitepaper, after all): the wild world of off-chain software becomes far less brittle, because APIs get replaced with ALIs (Application Logical Interfaces) and conformance to these "specs" is guaranteed.
PSA: I now consider *all* of DeFi unsafe.
Coding agents are superhuman at finding vulnerabilities, and smart contract security is too asymmetric: defenders need to fix every bug while attackers need just one exploit to steal funds.
1/ @TemporaLabs has integrated @Khalani_Network to power crosschain swap execution inside DR HIRO, their agentic DeFi investing copilot.
Every swap and rebalance DR HIRO initiates now settles atomically through Khalani, across chains.
I just encountered a project that is *the* fundamental economic layer for the global AI agent economy.
Amazing. Clearly an industry critical system they’re building.
*five minutes later*
I’ve now encountered 37 more projects that are also solely responsible for powering the global agentic AI economy.
Pls stop 😭
It is totally possible to live in a state without billionaires or AI/capitalism (same thing) as long as you’re willing to accept:
(A) poverty, scarcity, intense political struggle
(B) that this stasis will be temporary until competition reforms or destroys it
It’s not pretty.
Consciousness is not separate from the physical world — our “soul” is of the same nature as our body and any other phenomenon of the world | @carlorovelli in @NoemaMag https://t.co/2IM073nsyf https://t.co/b6nCA1JNz3
Atomic transactions are over. DeFi needs delayed settlement to survive. Without it, the risk/reward is hard.
So alongside its public release, Royco will introduce delayed settlement. Instead of every transaction happening instantly, it enters a delayed queue. Initially, that delay will be 24 hours.
On one hand, a t+1 settlement means the system will take longer to grow, and rates won't be as reactive. But on the other hand, the ability to keep depositors safe remains the top priority - and this is a strong lever to do so.
Delayed settlement joins other key security practices at Royco, including formal verification, traditional audits, active monitoring, and more. More on the 8 layers of security in the next tweet.
@pmarca From the stoics to Heidegger, much of the philosophical tradition would agree with this.
Which I find interesting because many non philosophers *seem* to regard philosophy as the antithesis of what you’re describing!
Important to note that there is a hard problem of consciousness. Reductive accounts don't straightforwardly work, which is why Smart's 1959 paper "Sensation and Brain Processes" (which defends the view Hinton has rediscovered) is not regarded as having settled the debate.
Computer scientists often seem incredibly confident one way or the other about computational functionalism. What they should say is that the arguments both for and against provide only inconclusive considerations and the right attitude is therefore one of great uncertainty.
The problem with this article is that while the conclusion — despite being categorical without enough warrant — is agreeable (a machine executing an LMM is not and cannot be conscious) and yet the reasons the author gives for the conclusion are not at all.
1. It’s true we don’t need a complete metaphysics of consciousness to assess the likelihood of AI consciousness
2. This article tries to assess AI consciousness using an approach that does actually require a metaphysics of consciousness
3. … all the while claiming that the approach sidesteps that same requirement
Google DeepMind researcher argues that LLMs can never be conscious, not in 10 years or 100 years.
"Expecting an algorithmic description to instantiate the quality it maps is like expecting the mathematical formula of gravity to physically exert weight."