From Millions and Months to $100 and a Weekend: How Chatbots Changed in 5 Years

Everything will be a chatbot by 2035

1. Chatbots Used to Be Painful

Just five years ago, building a chatbot required huge teams - backend engineers, NLP researchers, linguists, content editors, and data experts. Each chatbot consisted of specialized skills, added carefully without retraining the entire system from scratch.

Developers had to build advanced architectures with fuzzy search and powerful embeddings to handle unpredictable user queries and semantic variations. Each skill relied heavily on precomputed triggers, carefully balanced with business logic. Internally, chatbot modules tackled difficult NLP problems: extracting key data, querying databases or APIs, and clearly summarizing results. Large intent sets - like those in banking support - required extensive retrieval systems with constant adjustments.

Chatbots were complex combinations of conditional logic, advanced ML, and handcrafted examples - always needing quick iterations and careful maintenance.

As a result, chatbot development was a luxury mostly for large corporations. Even many banks couldn't afford to create a basic retrieval system answering the thousand most common customer-support questions.

I personally once spent an entire day just mapping out the logic of a single chatbot component - though admittedly a large one - within a broader dialogue system. Entire floors of engineers worked for months on tasks like this just to ship products.

2. Voice Assistants Made Things Even Harder

Adding voice capabilities raised complexity - and cost - to new levels. Text-to-Speech (TTS) required professional actors recording hours of flawless speech. Speech-to-Text (STT) was even harder, demanding large, specialized datasets covering diverse accents, noisy audio, and domain-specific vocabulary. Even premium off-the-shelf solutions often disappointed. Building your own STT or TTS would at least triple your costs.

3. Then the Game Changed Completely

Today, a single developer can build a functional chatbot in an evening, often after just reading one Medium tutorial. With three APIs - Speech-to-Text, a Large Language Model (LLM), and Text-to-Speech - you're good to go. Or skip coding altogether with no-code tools. Maintaining and scaling still require work, but quickly validating ideas is effortless.

The quality jump is impressive. You can now use Google ASR, Deepgram, or Whisper STT and get solid results. ElevenLabs provides natural-sounding speech synthesis at a very low cost. Modern LLMs handle dialogue naturally and reliably.

Companies previously published complex research papers explaining intent detection, fuzzy matching, and dialogue management. Now, three API calls and a small configuration file solve these problems.

Adding new chatbot features became easy thanks to built-in function-calling in modern LLMs. You simply describe new functions directly in your prompt - no complicated infrastructure needed. It works immediately.

4. The Trap: Building Everything In-House

Yet, surprisingly, some companies still insist on developing chatbot infrastructure themselves, usually because engineers want to experiment with shiny new technologies. I've personally seen this happen twice recently. Both times ended badly: months wasted, huge budgets burned, no product shipped, and layoffs - once with a major scandal involved.

Unless you're OpenAI, building chatbot infrastructure from scratch today is just poor management.

5. Premium APIs: Expensive or Smart?

Premium APIs and established LLM services seem expensive upfront, but they're cheaper in the long run. Proven solutions reduce maintenance, fixes, and support headaches. Cheaper local setups often become expensive rebuilds later.

6. The Market Explosion (and the New Challenge: Marketing)

Rapid market growth supports this shift. Companies not using chatbots risk losing ground to competitors. Yet marketing has become harder. From personal experience, attracting attention and converting leads is significantly tougher now compared to just a few years ago. Customers are skeptical, and expectations have risen sharply.

7. What's Next?

I'm not a believer in the "AI-2027" vision - that autonomous agents will build other agents, completely replacing humans. But continued simplification and, more importantly, reduced costs of AI-driven assistant interfaces will let us experiment faster and better.

Thus, I predict rapid growth of smaller, specialized AI tech companies solving specific use-cases efficiently and affordably.