Road to AI 01: How Computers Were Born
Like people, computing has a life story. This kickoff post explains where it started and maps the next 12 weekly episodes.
AI-assisted draft · Editorially reviewedThis blog content may use AI tools for drafting and structuring, and is published after editorial review by the Trensee Editorial Team.
Why this series exists
To understand AI today, model benchmarks are not enough.
We also need the long arc: why computers were built, what problems they solved, and how each layer evolved.
This series follows the life story of computing, one turning point per week.
Main point first: there was no single "birth date"
Computing was not born in one moment.
It emerged when theory, real-world demand, and hardware breakthroughs converged.
- Theory: defining what is computable
- Demand: wartime and industrial calculation pressure
- Implementation: electronic machines and stored-program architecture
Episode 1 takeaway: the birth moment
Computers did not begin as "fast calculators."
They began as systems that break complex work into repeatable procedures.
- 1930s-1940s: explosive demand for automated calculation
- Turing: formal limits of what can be computed
- von Neumann: stored-program architecture as the modern baseline
Modern AI still runs on this foundation.
No matter how strong the model is, execution is constrained by compute, memory, and I/O.
Timeline of the birth era
| Year | Event | Why it matters |
|---|---|---|
| 1936 | Turing formalizes a computation model | Theoretical basis for algorithmic execution |
| 1943-44 | Colossus used for codebreaking | Proof that large electronic computation is practical |
| 1945 | ENIAC announced | Symbolic start of general-purpose electronic computing |
| 1945 | Early von Neumann report draft | Direction for stored-program architecture |
| 1948-49 | Manchester Baby / EDSAC | Stored-program model becomes real and repeatable |
Turing and von Neumann in practical product terms
Turing: "What can be computed?"
Turing gave us a way to describe problems as procedures and state transitions.
That framing still appears in modern LLM pipelines.
- define input
- define rules
- manage intermediate state
- define stop conditions
von Neumann: "How should it run?"
The core of von Neumann architecture is storing program and data in memory together.
Modern AI inference systems are still extensions of this principle.
- fetch/decode/execute flow
- memory bandwidth as a bottleneck
- I/O design shaping end-to-end throughput
Why this still matters for AI teams now
The market talks about models, but production bottlenecks still sit in core computing fundamentals.
- Memory and bandwidth limits: longer context increases latency and cost
- I/O architecture: retrieval, caching, and streaming shape user experience
- Execution orchestration: agents/workflows are procedural systems at scale
So, better models help, but better execution architecture wins in production.
12-week publishing roadmap
| Week | Theme | Why it matters for AI today |
|---|---|---|
| 1 | Birth of computers | Root of modern AI infrastructure |
| 2 | Transistors and ICs | Cost/performance path to AI at scale |
| 3 | OS and software engineering | Core discipline behind reliable AI products |
| 4 | Internet expansion | Connectivity layer for distributed AI |
| 5 | Search engine era | Predecessor mindset for RAG systems |
| 6 | Mobile computing | How AI moved into everyday UX |
| 7 | Cloud computing | Standardized training/deployment model |
| 8 | Big data and recommender systems | Data strategy before foundation models |
| 9 | Deep learning revival | GPU + neural nets turning point |
| 10 | Transformers and LLMs | Core architecture of generative AI |
| 11 | Multimodal and agents | Shift toward workflow automation |
| 12 | AI-native era | How products and teams are being redesigned |
This week's action points (operator checklist)
- In planning, set latency and cost budgets alongside model quality targets.
- Draw a simple data flow (collect -> store -> retrieve -> respond) and mark bottlenecks.
- Split issues into "prompt-level fixes" vs "infrastructure-level fixes."
Summary
The birth of computing is not just history; it is the base layer of today's AI product decisions.
This first episode connects theory (Turing), architecture (von Neumann), and current operational bottlenecks.
Next episode
Episode 2 covers how transistors and integrated circuits reshaped the economics behind today's AI stack. ai-evolution-chronicle-01-birth-of-computing 2026-02-08 ai_road_f44bb122 evolution_to_f54bb2b5 chronicle_ai_f24badfc 01_01_f34baf8f birth_how_f04baad6 of_computers_f14bac69 computing_were_ee4ba7b0 ai_born_ef4ba943 evolution_road_fc4bbdba chronicle_to_fd4bbf4d
Execution Summary
| Item | Practical guideline |
|---|---|
| Core topic | Road to AI 01: How Computers Were Born |
| Best fit | Prioritize for AI Infrastructure workflows |
| Primary action | Profile GPU utilization and memory bottlenecks before scaling horizontally |
| Risk check | Confirm cold-start latency, failover behavior, and cost-per-request at target scale |
| Next step | Set auto-scaling thresholds and prepare a runbook for capacity spikes |
Frequently Asked Questions
After reading "Road to AI 01: How Computers Were Born", what is the single most important step to take?▾
Start with an input contract that requires objective, audience, source material, and output format for every request.
How does evolution-chronicle fit into an existing AI Infrastructure workflow?▾
Teams with repetitive workflows and high quality variance, such as AI Infrastructure, usually see faster gains.
What tools or frameworks complement evolution-chronicle best in practice?▾
Before rewriting prompts again, verify that context layering and post-generation validation loops are actually enforced.
Data Basis
- Method: Compiled by cross-checking public docs, official announcements, and article signals
- Validation rule: Prioritizes repeated signals across at least two sources over one-off claims
External References
Have a question about this post?
Ask anonymously in our Ask section.