Overview
Daniel Miessler proposes that 2026 could see AI breakthrough outcomes not from new models but from continuous algorithm loops - specifically a “Last Algorithm” that functions as a universal problem solver using iterative cycles of observation, planning, execution, and learning.
Key Arguments
- Current AI loops are thinking too small by focusing on narrow features and code rather than general problem-solving: Existing approaches like the Ralph loop grind on specific features rather than tackling universal problem-solving capabilities
- A universal problem solver algorithm could emerge through iterative loops that establish ideal states and systematically work toward them: The proposed approach uses an outer loop to define the ’euphoric surprise’ ideal state, then an inner loop with 7 phases (OBSERVE, THINK, PLAN, BUILD, EXECUTE, VERIFY, LEARN) to iteratively approach that ideal
- Breakthrough innovations often look like ‘ass’ initially but could slip through cracks when enough people try obvious-seeming approaches: Historical precedent shows transformative ideas initially appear flawed, and the author believes there’s a 50-75% chance this approach has merit despite seeming too simple
Implications
This matters because if successful, such an algorithm could represent the foundational breakthrough that enables artificial superintelligence-like outcomes - potentially the universal problem-solving capability that any advanced civilization would eventually discover, making it a pivotal moment in AI development rather than just another incremental model improvement.
Counterpoints
- Thousands of more qualified researchers aren’t actively pursuing this approach: The author acknowledges that many people with superior expertise and specialized education in AI aren’t working on this type of universal algorithm
- 99% of general problem solver attempts are ‘complete ass’: Historical track record shows that most attempts at creating universal problem-solving systems fail