We’re thinking about AI agents all wrong. In our rush to create “do anything” assistants, we’ve forgotten the fundamental nature of computation and intelligence. This isn’t just another technical critique – it’s about understanding what AI actually is and how we should be using it.
The Current Paradox
Every time we create an AI agent, we spend enormous effort programming it to handle specific tasks. Think about that for a moment: we’re taking a system that’s supposed to be generally intelligent, and we’re manually programming it for individual use cases. There’s a deep irony here that nobody seems to be talking about.
The Hidden Cost of Our Approach
Take a simple example: an agent designed to manage your calendar. To make this work, humans need to code extensive systems, guardrails, and function calls. Each new task requires new code, new prompts, new safety measures. We’re not building intelligence – we’re building incredibly complex, brittle automation systems and calling them “agents.”
The Fundamental Flaw
The problem isn’t with the AI – it’s with our paradigm. We’re treating these systems like servants waiting for commands, when we should be treating them like a new form of programming language. A language that can write itself, modify itself, and evolve through conversation.
A New Way of Thinking
Instead of asking “How do we make AI do X?”, we should be asking “How do we let AI write its own solution?” The difference is subtle but profound:
Current Approach: Human writes code → AI executes tasks
New Paradigm: Human converses → AI writes its own code
The Self-Writing Future
Imagine AI not as a tool waiting for instructions, but as a programming language that weaves itself into existence through conversation. Each interaction becomes a form of programming, but not in the traditional sense. The AI isn’t just executing commands – it’s writing its own solutions, creating its own functions, developing its own approaches.
The Tools We Need
This isn’t just philosophical musing – we can see the exact tools we need to make this paradigm shift real. Just as traditional programming evolved from basic instructions to complex systems, AI needs its own set of fundamental building blocks:
1. Parallel Thinking
Current AI thinks linearly – one thought after another. But real intelligence operates in parallel streams. We need systems that can pursue multiple lines of reasoning simultaneously, weaving them together into coherent solutions. This isn’t just about speed – it’s about the quality of thinking itself. Imagine we want to sort a list of Github repos by last activity. We would like to have a hundred chats of three messages. Not one thread of 300 messages.
2. Asynchronous Execution
Intelligence doesn’t wait for each thought to complete before starting the next. Our AI systems need to learn to initiate processes, let them run in the background, and integrate results as they arrive. This is how natural intelligence works – we don’t stop thinking about everything else while waiting for a single thought to complete.
I’m thinking cron jobs, ai’s setting alarms to schedule something to run, and starting more threads at once, like in parallelism.
3. Memory Management
Current AI is like a person who is really smart but can only remember for 10 minutes. We need sophisticated memory systems that can:
– Create new context windows dynamically
– Compress and retain important information
– Reduce subroutine outputs for higher-level processing
– Build lasting knowledge bases that evolve over time
4. Conditional Branching and Loops
Real intelligence doesn’t follow a linear path. It evaluates conditions, makes decisions, and repeats processes until goals are met. Our AI systems need the ability to:
– Make genuine decisions based on context
– Repeat processes until they achieve desired outcomes
– Learn from each iteration
– Adapt strategies based on results
5. The AI Compiler
Just as traditional programming languages need compilers to transform human-readable code into machine instructions, we need AI compilers that can:
– Optimize prompts automatically
– Learn from user feedback
– Reduce computational waste
– Transform natural language into efficient execution paths
6. Decentralized Infrastructure
The future of AI will be in data centers, in our homes, and in our communities. We need to:
There is a movement to move units of compute to decentralised networks. This allows you to provide your computer and earn a bit on the side. This will allow us to have large sophisticated models while still being able to run distributed on moderately small hardware. This is how we fight back against the big companies.
The Path to True Intelligence
This isn’t just about making AI more efficient – it’s about creating the conditions for genuine machine intelligence to emerge. By providing these fundamental tools, we allow AI to:
– Write and optimize its own “code”
– Build upon its own knowledge
– Interact with other AI systems
– Evolve beyond its initial programming
Beyond Current Limitations
The current approach of building specific agents for specific tasks is a dead end. By providing these fundamental tools and treating AI as a self-writing programming language, we open the door to systems that can:
– Truly learn and adapt
– Solve problems creatively
– Build upon their own solutions
– Collaborate with other AI systems
– Evolve beyond their initial capabilities
The Future is Decentralized
This new paradigm naturally leads to a decentralized future. Projects like Corcel and fetch.ai are already showing us glimpses of this future – where AI computation happens at the edge, in our homes and communities, rather than in centralized data centers.
Conclusion
The path forward isn’t about building better agents – it’s about providing the fundamental tools that allow AI to build itself. When we stop trying to program specific behaviors and start providing the building blocks of genuine intelligence, we open the door to possibilities we can barely imagine.
This isn’t just a technical evolution – it’s a fundamental shift in how we think about artificial intelligence. And it’s already beginning to happen.
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