Proliferation is the default of technology. General purpose technologies become waves when they diffuse widely. Without an epic and near-controlled global diffusion, it’s not a wave; it’s a historical curiosity.
Once diffusion starts, however, the process echoes throughout history, from agriculture’s spread throughout the Eurasian landmass to the slow scattering of water mils out from the Roman Empire across Europe.
Once a technology gets traction, once a wave starts building, the historical pattern we saw with cars is clear.
When Gutenberg invented the printing press around 1440, there was only a single example in Europe: his original in Mainz, Germany. But just fifty years later a thousand presses spread across the Continent. Books themselves, one of the most influential technologies in history, multiplied with explosive speed.
Or take electricity. The first electricity power stations debuted in London and New York in 1882, Milan and St. Petersburg in 1883, and Berlin in 1884. Unsurprisingly, consumer technologies exhibit a similar trend. Alexander Graham Bell introduced the telephone in 1876, By 1900, America had 600,000 telephones. Ten years later there were 5.8 million. Increasing quality joins decreasing prices in this picture. A primitive TV sold in $1000 in 1950 would cost just $8 in 2023. though, of course, TVs today are infinitely better and so cost more.
Proliferation is catalyzed by two forces: demand and the resulting cost decreases, each of which drives technology to become even better and cheaper. Of course, behind technological breakthroughs are people.
They labor at improving technology in workshops, labs, and garages, motivated by money, fame, and often knowledge itself. Technologists, innovators, and entrepreneurs get better by doing and crucially by copying.Copying is a critical driver of diffusion. Mimicry spurs competition, and technologies improve further.
Economies of scale kick in and reduce costs. Civilization’s appetite for userful and cheaper technologies is boundless. This will not change.
- Mustafa Suleyman, Founder of DeepMind
- Page 31, The Coming Wave.
Nine Additional Historical Examples of the Proliferation of Technology
Below are nine clear-cut cases where a single invention or platform tipped from “curiosity” to worldwide wave, echoing the diffusion dynamic described in the excerpt.
1. Medieval water-mills
- Roman engineers introduced the water-mill to Europe in the 1st century CE, but the real take-off waited for the early Middle Ages. By 1086 the Domesday Book already lists ≈5,600 mills in England alone, implying tens of thousands across Europe — a three-order-of-magnitude jump in a few centuries.
2. Steam engines in the Industrial Revolution
- Thomas Savery’s 1698 pump was “engine #1.” By 1800 Britain had ~2,300 working engines spread over mining, textiles and transport — enough to transform national energy use and industrial output. UCI Social Sciences
3. U.S. railroads
- Track length grew from 23 miles in 1830 to 254,000 miles by 1916 — more than a ten-thousand-fold expansion that rewired the American economy and settled the continent.
4. Household electrification
- Only ~10 % of U.S. homes were wired in 1910; by 1930 the figure had leapt to ≈70 %, thanks to falling generator costs and the network effects of appliance makers.
5. Penicillin
- Before WWII the world’s supply was measured in hundreds of millions of units; by late-1945 U.S. firms were turning out ≈650 billion units every month — a 1,600-fold jump in two years that made antibiotics a global standard of care.
6. Personal computers
- Annual shipments exploded from 48 000 units in 1977 to 125 million in 2001; installed base passed one billion machines in 2008, cementing the PC as a general-purpose platform.
7. The Internet
- Worldwide penetration doubled from 30 % of humanity in 2010 to 60 % in 2020—an extra 2½ billion users in a single decade.
8. Smartphones
- The iPhone launched in 2007; by 2024 there are ≈7.16 billion smartphone subscriptions, essentially one per adult on the planet, with further growth still forecast.
9. Solar photovoltaic modules
- As cumulative output doubled again and again, module prices crashed from $106 /W in 1976 to <$0.40 /W in 2019 (≈99 % decline). The plummeting cost turned solar from space-probe niche to the largest source of new generating capacity worldwide.
10. Generative-AI: ChatGPT
- OpenAI’s chatbot hit 100 million monthly users in just two months after launch — the fastest consumer-app adoption ever recorded, out-pacing even TikTok.
Why they all snowballed
Across very different eras, the same feedback loop appears: initial breakthrough → early adopters prove value → copy-cat entrants & competition drive scale → scale slashes unit cost and boosts quality → huge new demand pulls the technology into ubiquity. The historical record suggests that once diffusion reaches a critical mass, “proliferation is indeed the default.”
How Open Source Software Accelerates Proliferation of Technological Adoption
A live open-source “wave”: Linus Torvalds Writes Linux
- 1991 – one grad-student prototype.
- 2025 – Linux runs ≈ 96 % of the world’s top one-million web servers and ≥ 90 % of super-computers, and its kernel underpins every Android phone on the planet. That jump from hobby OS to the default internet substrate mirrors the pattern in your excerpt: free code → relentless copying → economies of scale → ubiquity.
WordPress — open-source diffusion on steroids
| Milestone | What happened | Why it mattered |
|---|---|---|
| May 2003 | Fork of the abandoned b2/cafelog blog engine ships under the GPL. | Zero licensing barriers; anyone can copy, hack, re-host. |
| July 2011 | WordPress already powers 50 million sites. | Confirms the wave is real; gives plug-in/theme creators a market. Weblog Tools Collection |
| May 2025 | 43.5 % of all websites (≈ 529 million) now run WordPress; it commands 61 % of the CMS market. WPZOOM | Past Gutenberg- press levels of diffusion; effectively the default publishing layer for the web. |
| Ecosystem scale | 70 k + plugins, 30 k + themes; countless SaaS hosts, agencies, and product companies. WPZOOMColorlib | Copy-and-compete loop drives quality up, cost down; every plug-in author is an incremental R&D lab. |
How WordPress rode (and fueled) the proliferation loop
- Demand pull – Small businesses, bloggers, newsrooms all needed cheap, flexible publishing. A single-click installer and a theme got them online in minutes instead of paying for bespoke CMS builds.
- GPL-powered mimicry – Because the core and every derivative must stay GPL, copying isn’t just legal — it’s expected. That norm produced tens of thousands of plugins and themes, each someone’s attempt to out-innovate the last.
- Economies of scale – As install base ballooned, hosting companies optimized specifically for WordPress, pushing running costs toward zero and performance up. Managed-WP hosting, page-builder plugins, and WooCommerce (itself GPL) stacked more value on top of the same free core.
- Feedback spiral – More users → more plugin revenue → more developers → better tooling → more users. The very thing Gutenberg’s printing press kicked off in the 15th century repeats here in software form.
Bottom line: WordPress is the open-source textbook case of technology’s default toward proliferation — from one fork in 2003 to nearly half the web by 2025 — driven by zero-cost replication, a rabid developer community, and the economic flywheel that open licensing unlocks.
What is the future of Open Source technology amidst the Coming Wave of AI?
1 Large-Language Models: the “source weights” wave
Open-weight releases have reached critical mass:
- Meta’s Llama series. Llama 3 (and, weeks ago, the 70 B-parameter “3.3” refresh) ships full checkpoint downloads via Hugging Face; requests are auto-approved within hours. GitHub
- Mistral’s Mixtral line. Apache-2 licensed, 8×22 B MoE and code-tuned variants deliver GPT-4-class performance while remaining completely royalty-free. Mistral AI Documentation
- Coming soon: even OpenAI has confirmed an “open-weight” model for mid-2025, a signal that open sourcing is no longer fringe. WIRED
Why it matters next:
- Hardware + quantization (gguf/llama.cpp) put 8–70 B models on consumer GPUs and phones; the cost of running frontier LLMs is collapsing.
- Specialisation loops (LoRA, QLoRA, DPO) let any lab or startup turn a general model into a niche expert for ≪ $10 k.
- Governance battles now focus on compute controls rather than banning open weights entirely. WIRED
Result: open LLMs are on track to become the Linux layer of AI — the default substrate every other product is built on.
2 Open-source orchestration frameworks (LangChain → LangGraph)
LangChain stabilised with v0.2 (May 2024), splitting into small, composable packages and adding first-class tool-calling, tracing, and eval hooks. Langchain
LangGraph then extended LangChain with a graph-of-states model for multi-agent, fault-tolerant workflows (think Airflow, but for LLM calls). AWS is already teaching customers how to deploy LangGraph graphs on Bedrock. Amazon Web Services, Inc. Its GitHub repo has become the de-facto reference for “resilient agent” design patterns. GitHub
Trajectory:
| 2023 | 2024 | 2025-2026 (expected) |
|---|---|---|
| Prompt chains | Tool-calling, RAG pipelines | Full agent graphs with tracing, rollback, cost & carbon budgets built-in |
| Single-thread | Concurrency via LangGraph | Distributed micro-agent swarms across GPU clusters |
| Ad-hoc logging | LangSmith / AgentOps observability | SLO dashboards & policy enforcement layers GitHub |
3 Next layer up: open frameworks that create agents
With orchestration commoditised, the community is racing to ship “agent builders” that sit on top:
| Project | Built on | What it gives you |
|---|---|---|
| Flowise | LangChainJS | Drag-and-drop UI; ship a bespoke RAG or chat agent in minutes. GitHub |
| CrewAI | Python (independent) | Role-based “crew” pattern for collaborative multi-agent tasks. GitHub |
| Microsoft Autogen | Optional LangChain adapter | Programmatic framework + Autogen Studio no-code GUI; enterprises prototype multi-agent workflows, then export to code. GitHub |
| AgentOps | LangChain/LangGraph plug-ins | Monitoring, evals and cost tracking for anything you build. GitHub |
Where this is heading
- Domain-specific starter kits. Expect OSS templates for “legal research agent,” “WordPress marketing copilot,” etc., each bundling tools + guardrails.
- Marketplace economics. Plugins, data connectors, tool APIs will trade like WordPress themes did — a low-friction path to monetise niche know-how.
- Local-first & privacy. Quantised Llama-class models plus LangGraph on-device mean SMEs can run powerful workflows without sending data to the cloud.
- Safety layers baked-in. Guardrails.ai-style validation and the new “constitutional decoding” patches will ship as default nodes, not after-thoughts.
Stacking the AI-Agent universe the way the web was stacked
| Web-era layer (≈2000s) | Role | AI-era analogue | Role |
|---|---|---|---|
| Linux kernel | Free, modifiable operating substrate that every higher layer builds on. | Open-weight LLMs (Llama 3, Mixtral, Gemma-it, etc.) | The raw cognition layer: freely downloadable weights anyone can fine-tune or quantize. |
| Apache / PHP / MySQL (the LAMP middle tier) | Runtime, networking, and data plumbing that turns static bits into dynamic applications. | LangChain + LangGraph | Orchestration fabric that turns raw LLM calls into stateful, tool-using, multi-step agents and workflows. |
| WordPress | A user-facing UI/UX so non-coders can publish, extend with plugins, and run entire businesses on the web stack. | LlamaPress | The missing application-layer CMS for agents: a radically open-source builder that lets technical and non-technical users spin up custom software tools and AI agents on top of LangChain/LangGraph, just as easily as WordPress let them spin up websites. |
Bottom line:
If open-source LLMs are the Linux of the emerging AI platform and LangChain/LangGraph are its PHP/MySQL/Apache, then LlamaPress aims to be the WordPress—the approachable, plugin-friendly front door through which the next hundred million creators build tailor-made software and autonomous agents.
The big picture
History says that once a technology’s unit-cost crashes and copying is friction-free, proliferation is inevitable. Open-weight LLMs have crossed that threshold; open orchestration frameworks are doing the same for workflow logic; and a Cambrian explosion of agent-builder toolkits is lowering the bar from “developer” to “power user.”
By the late-2020s, spinning up a bespoke AI workforce could be as routine — and as openly sourced — as launching a WordPress site today. The democratization of AI is not a distant goal; it is the next wave, already appearing.
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