AI coding: history, security, and the future of code creation
Do you remember mid-2022? The war in Ukraine had just begun, the world was slowly shaking off the COVID-19 pandemic, and remote work had become the new standard. This time, developers like me looked at the early versions of GitHub Copilot (and other tools) with a mixture of curiosity and skepticism. We treated AI coding as a novelty, an “autopilot” that might occasionally suggest a missing bracket…
None of us would have ever imagined what happens in the next 4 years…We would travel the path from simple autocomplete to autonomous agents capable of planning system architecture.
But this is NOT an article about yet another language model. This is the story of how AI coding changed the DNA of our work… and de facto created a new IT reality.
I have divided this evolution into 5 key eras. Understanding them is essential to know where we stand today and how to prepare your company for what is coming.
But before we go any step further let’s explain some basic terms.
The modern programmer’s glossary
- LLM (Large Language Model) – A neural network trained on massive text datasets to understand, generate, and reason with natural language. LLMs predict the next token in a sequence, enabling tasks such as conversation, coding, summarization, and analysis.
- GPT (Generative Pre-trained Transformer) – A specific class of LLM based on the Transformer architecture, pre-trained on large corpora and then fine-tuned for downstream tasks. GPT models generate coherent text by modeling long-range dependencies in language.
- NLP (Natural Language Processing) – A field of artificial intelligence focused on enabling computers to understand, analyze, and generate human language. NLP provides the theoretical and algorithmic foundation on which modern LLMs are built.
- Local Model – A language model that runs on a user’s own machine rather than in the cloud. Local models prioritize privacy, offline capability, and user control over data and execution.
- Multimodal – Refers to models that can process and generate multiple types of data—such as text, images, audio, and video—within a single unified system. Multimodal models enable richer interaction beyond text-only interfaces.
- Agent (in the AI context) – An AI system that can plan, make decisions, use tools, and execute actions autonomously over multiple steps to achieve a goal. Unlike traditional chat models, agents operate in feedback loops rather than single-turn responses.
- Prompt – The input instruction or context provided to an AI model that guides its behavior and output. Prompts can include questions, constraints, examples, or step-by-step instructions to shape the model’s response.
The 5 eras of AI coding: from autocomplete to agents
The history of AI coding is a fascinating journey that has accelerated significantly in recent years. What used to take years now happens in months. Here is how we moved from simple suggestions to autonomous systems:
Era 0: Foundations without conversation (until November 2022)
In this era, AI was a tool you “spoke to,” but didn’t converse with. Models like the early Codex or GitHub Copilot (June 2021) worked on the principle of completion. The programmer wrote the beginning of a function, and the AI guessed the rest. Models existed, but access to them was mainly through APIs.
Key feature: Lack of conversational context. AI was an “autopilot,” not a “partner.” Editors (IDEs) offered simple code completion mechanisms, based mainly on NLP.
Milestones:
Era 1: Chat becomes the interface (November 2022 – mid-2024)
The launch of ChatGPT (GPT-3.5) changed everything. For the first time, conversation with a model in natural language (NLP) became possible. We could ask: “Write me a Python script to scrap data,” and receive a ready-made result.
However, LLMs in this period were still unimodal (text in, text out) and largely reactive—they only answered what we asked, without a deeper understanding of the full context. They could explain, summarize, and generate content, but they did not take action. Their reasoning was shallow, linear, and limited to a single response. And the models themselves often “hallucinated” (made up facts). Despite this, people were amazed by the very fact that they could “talk” to a computer—and that it could generate code at all. This era built trust in LLMs as general cognitive tools, but not yet as autonomous systems.
Milestones:
Era 2: AI enters the editors (from June–July 2024)
This is the moment when AI coding became an integral part of developers’ daily work. Tools like Cursor meant we no longer had to copy code between the browser and the editor. We could tag files with the @ symbol and say: “Use this file as context and fix the bug in the controller.”
AI began to “see” our codebases holistically, modify files, and understand more complex tasks. From the role of a typical programmer—a “manual coder”—we transitioned to becoming reviewers. This was the moment of transition from chat assistants to agents. At that time, it still required a lot of human attention, but some began to predict that “a new future of coding is coming.”
Milestones:
Era 3: Models finally can see, hear, and speak (from May 2024)
In this era, multimodality became mainstream. Cloud-hosted models gained the ability to process text, images, audio, and sometimes video—all within a single, cohesive system. They also demonstrated significantly better reasoning capabilities, longer context windows, and real-time interaction possibilities. However, they remained centralized, closed, and cloud-dependent.
AI became more powerful, but not more independent. What seemed incredible just months earlier became possible. Since May 2024, one could send an image or audio to a cloud model and simply receive its description (for images) or transcription (for audio). This was another breakthrough for code generation: you just had to take a screenshot of the user interface you were building and add an instruction like “fix the margin for the red header.”
This drastically accelerated frontend work. Combined with the capabilities introduced in Era 2, completely new perspectives opened up.
Milestones:
Era 4: Reasoning moves to the local device (from January 2025)
Era 4 marks the return of local models—but with a key difference compared to earlier open-source models. These systems possess chat history, are conversational, and capable of maintaining long reasoning loops.
Privacy became a priority. Thanks to tools like Ollama and models like DeepSeek, companies started running advanced AI on their own servers. Local LLMs became easily accessible and emphasized privacy, data ownership, and offline operation. For the medical or financial industries, this was a true breakthrough. Because AI coding no longer meant sending sensitive code to external clouds. And although it still required high-end GPUs (such as the RTX 5080/5090 in PCs or RTX 6000 in dedicated servers), it gave us full control over data.
Despite this, most systems in this era still lacked management and long-term autonomy of action.
Milestones:
Era 5: Agents that plan and act (the present)
We are here and now. We are entering the era of agents. The biggest providers today are releasing CLI-based tools where LLMs can plan tasks, execute commands, observe results, and iteratively improve outcomes.
Tools like Claude Code or Codex signify the shift from AI as a responsive assistant to AI as an operator. Moreover, these tools will soon be able to connect with local models as well. Platform X (formerly Twitter) is full of “zero-shot” benchmarks. Reading them and watching the results (Claude / Codex and the “production” of output), it is hard not to be impressed. Additionally, Anthropic introduced the MCP protocol, which means the model can use any tool, such as Figma, Gmail, or Google Calendar.
It is no longer an assistant—it is a virtual employee.
Milestones:
Is AI coding safe?
With the development of AI coding, companies (perhaps yours too) are asking 3 key questions. Let’s address them.
💡 Who owns the code generated by AI?
Briefly: the person or organization for whom the code was created. AI is a tool, just like a brush for a painter or a word processor for a writer. AI has no legal personality, so it cannot be an “author” in the sense of copyright law. The owner of the economic rights is the entity for whom the code was created (i.e., the programmer or their employer).
💡 Is it safe?
That depends on the implementation. AI-assisted coding should be used exclusively in controlled environments. Even before the AI era, production data should never have been used locally. However, by maintaining data separation and using test environments, the risk is minimal.
💡 What about GDPR and privacy policies?
If you use cloud models (like OpenAI in the Enterprise version), your data is generally not used to train models (always check the terms of service!). The safest option remains local models. However, if you must use cloud models in a production environment, there are small local models, such as spaCy, which can act as an intermediate (anonymizing) layer before sending data to cloud models.
Does only the United States matter in this game?
Thanks to companies like OpenAI, Anthropic, and Meta (Facebook), the United States is currently the main player in the development of advanced language models and AI tools. Especially those available to the public. Leaders drive the greatest innovations in conversational, agentic, and multimodal models, and their technologies set the industry trends for 2025–2026.
However, the world is not standing still, and it doesn’t end with the USA. Global competition is growing in the area of local language models:
- China – Companies like Alibaba are developing their own series of models, including Qwen, which cover standard, multimodal, and code-dedicated models. They are gaining increasing capabilities and contextual memory windows.
- France – Represented by ambitious projects like Mistral, which strive to compete with large Western models through high efficiency and performance.
- Poland – Local initiatives are also appearing, e.g., Bielik. Although at this stage, their capabilities are still significantly smaller than the models of the largest providers.
Interestingly, boundaries are blurring. Thanks to protocols like MCP (Model Context Protocol), we can use American agent tools with local European models. In January 2026, Claude Code, an agentic coding tool from Anthropic, was integrated with the Ollama runtime, enabling the use of Claude Code with both proprietary local models and open models hosted by Ollama. This shows that technological rivalry is not just happening “across the pond”—it encompasses the entire global developer community.
In short: The United States still dominates in cloud, agentic, and multimodal LLMs, but competition from China, Europe, and local projects is growing, especially in the area of models running on local devices.
Summary: Don’t watch the revolution – be part of it
We have come a long way since 2022. AI coding is no longer just a novelty; it is a new market standard. It allows software to be built faster, cheaper, and often—surprisingly—more solidly, thanks to the automation of tedious tests and refactoring.
However, implementing these technologies in a company requires knowledge, strategy, and attention to data security. Buying a ChatGPT subscription is not enough to become an AI-driven company.
But you don’t have to go through these changes alone. At DevQube, we live and breathe technology. If you want to create modern software using the latest AI standards or need a consultation on how to safely implement these tools in your team – contact us.
👉 [Book a free consultation with a DevQube expert]
FAQ: Frequently asked questions about AI coding
💡 Will AI replace programmers?
No, but it will change their role. We are moving from “churning out code” to “solution architecture” and supervising AI agents. Programmers who master AI coding will replace those who ignore these tools.
💡 What is the best AI coding tool in 2026?
It depends on your needs. For rapid prototyping and building MVPs, Cursor or Claude Code is excellent. However, if absolute data privacy is a priority (e.g., in banking), the best choice would be a local model run via Ollama.
💡 Is code written by AI error-free?
No. Models can still make logical errors or create security loopholes. That is why the human role as a verifier (Code Reviewer) is more important than ever. AI is a powerful engine, but you must hold the steering wheel.
