A common language for human intent, AI discovery, and silicon execution.
Efficient Code → Less Power → Lower Cost
Imagine Oscar Piastri and Max Verstappen settling their legacy by playing Mario Kart, all their genius untapped. That's how we're making AI write code today.
High-level languages add layers between code and hardware, introducing overhead like redundant type checks that slow execution and waste resources.
Tools like TensorFlow, PyTorch, and .NET are general-purpose and can't be perfectly optimized for specific AI models or hardware.
Interpreted or bytecode-based languages like Python and Java prevent full hardware-specific compilation and optimization.
Where performance is key, AI should be creating CPU or GPU-specific machine code that exploits every advanced feature a processor has to offer.
The race for AI supremacy has a cost that threatens to make victory unaffordable.
AI compute costs set to top $100B/year by 2026, with $5.2 trillion needed in new data centre infrastructure by 2030.
Sources: McKinsey, IO Fund
By 2027, AI data centres could use 0.5% of global electricity—as much as the Netherlands—costing $13+ billion a year in energy alone.
Source: Epoch AI
Data centres use billions of litres of water yearly for cooling, raising regulatory scrutiny in water-scarce regions.
Sources: Google & Microsoft Environmental Reports
The current path is unsustainable. The only way to win is to change the energy economics of AI itself.
Optimal efficiency breaks down the sustainability wall.
Bridging intent, discovery, and execution in a unified framework.
Strategic Goals & Tests
100,000+ Variations
Pure Machine Code
Humans no longer write inefficient, low-level code. Instead, they provide high-level strategic goals, creative direction and test criteria.
Agreum is a structured form of "vibe coding", it translates this human intent into a concrete, testable objective for the AI.
The Agreum engine empowers the AI to find the most efficient path to that objective. It intelligently discovers novel optimization strategies and recursively improves its own output.
Creating bespoke machine code perfectly tailored to the specific task and hardware.
The result is a hyper-efficient stream of machine code that optimally targets each specific processor's native language.
This unlocks the hardware's full potential, slashing the cost, energy, and water consumption.
AI-discovered optimization that explores what humans never could.
Code humans would never think—or have time—to write.
From writing complex code to defining high-level goals and tests.
Generating and testing thousands of machine code variants that meet developer-defined criteria.
By manually creating specialized, GPU-level optimizations they achieved massive efficiency gains that dramatically reduced training costs. Agreum automates what DeepSeek did, then takes it to the next level by enabling the AI to iterate.
Both perform the same function. Notice the difference in approach.
def find_divisible_matches(targets, large_array):
targets_arr = np.array(targets)
large_arr = np.array(large_array)
matches = []
for target in targets_arr:
if target != 0:
divisible = large_arr[large_arr % target == 0]
for num in divisible:
matches.append((target, num))
return matches; Load 4 targets into AVX-512 register
vmovdqu64 zmm0, [targets]
; Prefetch next cache line
prefetcht0 [array + 64]
loop_start:
; Load 8 array elements
vmovdqu64 zmm1, [array + rsi]
; Parallel modulo operations
vpdivq zmm2, zmm1, zmm0
vpmulq zmm3, zmm2, zmm0
vpsubq zmm4, zmm1, zmm3
; Check for zeros
vptestmq k1, zmm4, zmm4Both code examples filter a large array for 64-bit numbers divisible by multiple parameters—a common operation in Blockchain Validation and AI Reasoning (neural network weights or similarity scores).
Python processes sequentially with abstraction overhead. Machine code leverages AVX-512 vector instructions, cache prefetching, and parallel processing to handle multiple elements simultaneously.
Optimizing beyond the scope of human ability
Theoretical Space: 11.5 to 161 million variations
Practical exploration space: 50,000-500,000+ viable, distinct optimizations
Imagine AI systems that write optimal code for every chip. From data centers to edge devices. The era of human-constrained optimization is ending.