Computing After Silicon: A History-Constrained Forecast of Computing Machine Evolution, 2026-2050

In the paper's words

A history-constrained forecast of computing machine evolution 2026-2050. Seven post-silicon candidates assessed; heterogeneous convergence (AI accelerators, quantum co-processors, processing-in-memory) is the central thesis; twelve dated falsifiable predictions registered for 2030-2040.

The abstract, verbatim, and its opening sentence is the claim. Every line below is quoted from the text.

Numbers it reports

1. Assumption Candidate Confidence Pillar A1: Accelerator software ecosystems mature within a decade AI-Heterogeneous 0.75 Empirical Base Rate (CUDA history) A2: Quantum error correction achieves logical qubits at scale by 2035 Quantum 0.35 Reference Class (classical ECC timeline) A3: Memory-bandwidth wall persists (no disruptive memory technology) PIM 0.70 Empirical Base Rate A4: Neuromorphic algorithms reach parity on real workloads Neuromorphic 0.30 Calibrated Subjective A5: Photonic logic integration overcomes device challenges Optical 0.20 Calibrated Subjective A6: Adiabatic control overhead falls 100× Reversible 0.15 Calibrated Subjective

   from 4.1 Enabling Assumptions

2. Candidate Allocation Rationale AI-Specialized Heterogeneous 30% Already dominant; focus on software/programmability Quantum Utility 25% Highest impact (9/10) but high uncertainty Processing-in-Memory 20% Lowest risk, engineering-driven near-term win Neuromorphic 10% Digital spiking path avoids materials risk Optical/Photonic 5% Interconnects first; logic is 2035+ Hedge (Unknown) 10% Anti-fragility floor — unlisted paradigm will emerge

   from 4.4 Research Effort Allocation

3. The Landauer limit is $10^{6}\times$ below current computing efficiency; even approaching it by 100× would transform mobile, edge, and datacenter computing.

   from Tier 2: Reversible Computing from Inception (1960s fork)

4. Quantum error correction at 5× current funding — the classical analogy suggests error correction, not qubit count, is the binding constraint.

   from Near-Term Fork Recommendations

5. AI-specialized architectures as the new processor baseline — every general-purpose CPU design from 2026 onward should assume >50% die area for domain-specific accelerators.

   from Near-Term Fork Recommendations

6. If CMOS scaling does not effectively end by 2035, or if AI accelerators do not exceed 50% of datacenter compute spending by 2030, the central thesis is disconfirmed.

   from 8. Conclusion

How it is done

No section of this paper is shaped like a method, so no method sentence is quoted.

What would falsify it

1. This paper synthesizes the history of computing paradigm shifts through a cross-domain lens spanning physics, information theory, economics, and biology, and produces a structured, falsifiable forecast of computing machine evolution through 2050.

   from Abstract

2. Third, what falsifiable predictions follow, and how would each be disconfirmed?

   from 1. Introduction

3. Section 6 registers the falsifiable predictions and practical applications.

   from 1. Introduction

Where the claim stops

1. If CMOS scaling does not effectively end by 2035, or if AI accelerators do not exceed 50% of datacenter compute spending by 2030, the central thesis is disconfirmed.

   from 8. Conclusion

2. We assess seven paradigm-shift candidates qualitatively, identifying AI-specialized heterogeneous computing as the highest-probability near-term trajectory and fault-tolerant quantum computing as the highest-impact but most uncertain candidate.

   from Abstract

3. Section 4 presents the forecast, including assumption audits and sensitivity analysis.

   from 1. Introduction

Where to read

1. 1. Introduction205 words · 1 min
2. 2. The Constraint-Saturation History of Computing324 words · 1 min
3. 3. The Post-Silicon Candidate Landscape341 words · 1 min
4. 4. The Forecast: Assumptions, Sensitivity, and Effort Allocation276 words · 1 min
5. 5. Counterfactual Backcasting256 words · 1 min
6. 6. Calibration Register: Twelve Dated, Falsifiable Predictions123 words · 1 min
7. 7. Practical Applications48 words · 1 min
8. 8. Conclusion102 words · 1 min
9. Declarations115 words · 1 min