If I had to choose one word to capture the spirit of AI after 2020, it would be scale.
Artificial intelligence has always been about teaching machines to mimic some aspect of human intelligence. But something changed in the last few years. Models like ChatGPT, Google Gemini, Anthropic’s Claude, and Midjourney are no longer small experiments or niche academic projects. They’re planetary in scale — so large that training them consumes measurable fractions of the world’s electricity, and researchers worry we might run out of high-quality public internet text to feed them.
This new age of AI is reshaping how applications are built. On one hand, AI models are more powerful than ever, capable of handling a dazzling variety of tasks. On the other hand, building them from scratch requires billions of dollars in compute, mountains of data, and elite talent that only a handful of companies can afford.
The solution has been “model as a service.” Instead of training your own massive AI model, you can call an API to access one that already exists. That’s what makes it possible for startups, hobbyists, educators, and enterprises alike to build powerful AI applications today.
This shift has given rise to a new discipline: AI engineering — the craft of building applications on top of foundation models. It’s one of the fastest-growing areas of software engineering, and in this blog post, we’re going to explore what it means, where it came from, and why it matters.
From Language Models to Large Language Models
To understand today’s AI boom, we need to rewind a bit.
Language models have been around since at least the 1950s. Early on, they were statistical systems that captured probabilities: given the phrase “My favorite color is __”, the model would know that “blue” is a more likely completion than “car.”
Claude Shannon — often called the father of information theory — helped pioneer this idea in his 1951 paper Prediction and Entropy of Printed English. Long before deep learning, this insight showed that language has structure, and that structure can be modeled mathematically.
For decades, progress was incremental. Then came self-supervision — a method that allowed models to train themselves by predicting missing words or the next word in a sequence, without requiring hand-labeled data. Suddenly, scaling became possible.
That’s how we went from small models to large language models (LLMs) like GPT-2 (1.5 billion parameters) and GPT-4 (over 100 billion). With scale came an explosion of capabilities: translation, summarization, coding, question answering, even creative writing.
Why Tokens Matter
At the heart of a language model is the concept of a token.
Tokens are the building blocks — they can be words, sub-words, or characters. GPT-4, for instance, breaks the sentence “I can’t wait to build AI applications” into nine tokens, splitting “can’t” into can and ’t.
Why not just use whole words? Because tokens strike the right balance:
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They capture meaning better than individual characters.
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They shrink the vocabulary size compared to full words, making models more efficient.
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They allow flexibility for new or made-up words, like splitting “chatgpting” into chatgpt + ing.
This token-based approach makes models efficient yet expressive — one of the quiet innovations that enable today’s LLMs.
The Leap to Foundation Models
LLMs were groundbreaking, but they were text-only. Humans, of course, process the world through multiple senses — vision, sound, even touch.
That’s where foundation models come in. A foundation model is a large, general-purpose model trained on vast datasets, often spanning multiple modalities. GPT-4V can “see” images, Gemini understands both text and visuals, and other models are expanding into video, 3D data, protein structures, and beyond.
These models are called “foundation” models because they serve as the base layer on which countless other applications can be built. Instead of training a bespoke model for each task — sentiment analysis, translation, object detection, etc. — you start with a foundation model and adapt it.
This adaptation can happen through:
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Prompt engineering (carefully wording your inputs).
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Retrieval-Augmented Generation (RAG) (connecting the model to external databases).
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Fine-tuning (training the model further on domain-specific data).
The result: it’s faster, cheaper, and more accessible than ever to build AI-powered applications.
The Rise of AI Engineering
So why talk about AI engineering now? After all, people have been building AI applications for years — recommendation systems, fraud detection, image recognition, and more.
The difference is that traditional machine learning (ML) often required custom model development. AI engineering, by contrast, is about leveraging pre-trained foundation models and adapting them to specific needs.
Three forces drive its rapid growth:
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General-purpose capabilities – Foundation models aren’t just better at old tasks; they can handle entirely new ones, from generating artwork to simulating human conversation.
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Massive investment – Venture capital and enterprise budgets are pouring into AI at unprecedented levels. Goldman Sachs estimates $200 billion in global AI investment by 2025.
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Lower barriers to entry – With APIs and no-code tools, almost anyone can experiment with AI. You don’t need a PhD or a GPU cluster — you just need an idea.
That’s why AI engineering is exploding in popularity. GitHub projects like LangChain, AutoGPT, and Ollama gained millions of users in record time, outpacing even web development frameworks like React in star growth.
Where AI Is Already Making an Impact
The number of potential applications is dizzying. Let’s highlight some of the most significant categories:
1. Coding
AI coding assistants like GitHub Copilot have already crossed $100 million in annual revenue. They can autocomplete functions, generate tests, translate between programming languages, and even build websites from screenshots. Developers report productivity boosts of 25–50% for common tasks.
2. Creative Media
Tools like Midjourney, Runway, and Adobe Firefly are transforming image and video production. AI can generate headshots, ads, or entire movie scenes — not just as drafts, but as production-ready content. Marketing, design, and entertainment industries are being redefined.
3. Writing
From emails to novels, AI is everywhere. An MIT study found ChatGPT users finished writing tasks 40% faster with 18% higher quality. Enterprises use AI for reports, outreach emails, and SEO content. Students use it for essays; authors experiment with co-writing novels.
4. Education
Instead of banning AI, schools are learning to integrate it. Personalized tutoring, quiz generation, adaptive lesson plans, and AI-powered teaching assistants are just the beginning. Education may be one of AI’s most transformative domains.
5. Conversational Bots
ChatGPT popularized text-based bots, but voice and 3D bots are following. Enterprises deploy customer support agents, while gamers experiment with smart NPCs. Some people even turn to AI companions for emotional support — a controversial but rapidly growing trend.
6. Information Aggregation
From summarizing emails to distilling research papers, AI excels at taming information overload. Enterprises use it for meeting summaries, project management, and market research.
7. Data Organization
With billions of documents, images, and videos produced daily, AI is becoming essential for intelligent data management — extracting structured information from unstructured sources.
8. Workflow Automation
Ultimately, AI agents aim to automate end-to-end tasks: booking travel, filing expenses, or processing insurance claims. The dream is a world where AI handles the tedious stuff so humans can focus on creativity and strategy.
Should You Build an AI Application?
With all this potential, the temptation is to dive in immediately. But not every AI idea makes sense. Before building, ask:
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Why build this?
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Is it existential (competitors using AI could make you obsolete)?
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Is it opportunistic (boost profits, cut costs)?
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Or is it exploratory (experimenting so you’re not left behind)?
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What role will AI play?
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Critical or complementary?
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Reactive (responding to prompts) or proactive (offering insights unasked)?
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Dynamic (personalized, continuously updated) or static (one-size-fits-all)?
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What role will humans play?
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Can your product defend itself?
Setting Realistic Expectations
A common trap in AI development is mistaking a demo for a product.
It’s easy to build a flashy demo in a weekend using foundation models. But going from a demo to a reliable product can take months or even years. LinkedIn, for instance, hit 80% of their desired experience in one month — but needed four more months to polish the last 15%.
AI applications need:
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Clear success metrics (e.g., cost per request, customer satisfaction).
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Defined usefulness thresholds (how good is “good enough”?).
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Maintenance strategies (models, APIs, and costs change rapidly).
AI is a fast-moving train. Building on foundation models means committing to constant adaptation. Today’s best tool may be tomorrow’s outdated choice.
Final Thoughts: The AI Opportunity
We’re living through a rare technological moment — one where barriers are falling and possibilities are multiplying.
The internet transformed how we connect. Smartphones transformed how we live. AI is transforming how we think, create, and build.
Foundation models are the new “operating system” of innovation. They allow anyone — from solo entrepreneurs to global enterprises — to leverage intelligence at scale.
But success won’t come from blindly bolting AI onto everything. The winners will be those who understand the nuances: when to build, how to adapt, where to trust AI, and where to keep humans in the loop.
As with every major shift, there will be noise, hype, and failures. But there will also be breakthroughs — applications we can’t yet imagine that may reshape industries, education, creativity, and daily life.
If you’ve ever wanted to be at the frontier of technology, this is it. AI engineering is the frontier. And the best way to learn it is the simplest: start building.
