Reflections

ChatBotKit Reflections Topics for strategy

ChatBotKit is a conversational AI platform that helps you build, train, and deploy AI-powered chatbots and virtual assistants for your business.

Why I May Not Open Source Our ToolsI keep itching to open source the internal things we have built over the years. Every time I get close I reach the same conclusion. The upside is mostly gone, and handing over the source now means handing over the moat.
Cheaper Models, Bigger BillsThe common assumption is that falling model prices will lower AI spend. The opposite is true. Cheaper inference means more usage, and the only real limit is the floor where you spend too little to stay competitive.
Agentic SaaS Is Web 4.0Web 2.0 rose on AJAX and gave us rich apps we now take for granted. That era is over. Web 4.0 belongs to agents, where the thing worth owning is not a page but an agent that does the work.
The Output Is ConvergingMP3 once sounded worse than WAV, and you could hear the compression. Then the encoders got good enough that the gap vanished and the argument stopped. Model output is heading the same way - it is converging until you cannot tell what produced it, and the model becomes a commodity.
Agents Are for ExplorationIt is easy to look at AI as a faster way to run the jobs you already have, then feel let down when it will not follow a fixed recipe to the letter. That is the wrong frame. AI is built for exploration - point it at a fuzzy problem with no recipe and it earns its keep.
You Can Copy What You Can SeeThe belief that LLMs can copy any software instantly falls apart fast. You can clone what is visible, but the value lives below the surface, and that part is invisible, expensive, and never free to maintain.
The Bottleneck Is Somewhere ElseFrontier tokens are expensive enough that the ROI is genuinely unclear. Speeding up code production does nothing when the bottleneck is somewhere else.
Be Your Own CustomerWhen you build a product you know too much about it, and that knowledge blinds you to how it actually feels to use. Being your own customer is the cheapest way to get the cold, outside view back.
Why We Need Forward Deployed EngineersThe forward deployed engineer role is exploding at Google, OpenAI, and Anthropic because applying AI well is still hard. The job is mostly problem understanding, with code as a small piece at the end.
Thoughts on Disposable SoftwareDisposable AI-generated apps will fill the same slot spreadsheets occupy today. Complex systems will keep demanding serious resources, and the average person prompting Claude or Codex will not close that gap.
The AI Job Loss Story Is Mostly FearAI will change work, but many current job-loss decisions are driven less by working automation and more by executive fear that competitors may be using AI better.
The Bubble Is RealThe AI bubble is real and it is going to pop. What survives will look like what survived the dot-com crash - companies with real customers, real revenue, and real margins.
Open Source AI Is No Longer A SideshowProprietary frontier labs still lead, but open source AI - increasingly driven by Chinese labs - is taking real ground. Diversity is insurance, and the harness around the model is where real-world use cases get built.
AI Use Cases Are a MuscleThe value of AI depends on whether you can find a real use for it in your own work and life. It is easy but it takes practice.
The Imperceptible UpgradeEvery new model generation costs more and promises more intelligence. But if most users cannot tell the difference in their daily work, the upgrade is not a leap - it is a rounding error with a premium price tag.
The Rise of Agentic SaaSTraditional SaaS sells tools. Agentic SaaS sells agents that do the work for you. Everyone has domain knowledge worth productionizing and the timing to build an agent-powered business has never been better.
The Discipline of Not BuildingCoding agents made feature delivery almost free. But features were never the goal. Every feature you ship is a liability you maintain forever. The real discipline is knowing which ones to leave on paper.
The Rise of Micro Coding AssistantsPremium AI models are subsidized and unsustainable. Open source models are not perfect but they do not need to be. The future of autonomous coding is not one giant agent. It is many small ones, each doing one thing well.
Build What You Already DoYou do not need a startup idea. You already have domain knowledge, data, and people paying for your expertise. AI agents can turn that into a product. Stop looking for ideas and start packaging what you already know.
Spending More and Learning LessAI is supposed to cut costs. Instead companies will pay the same or more while skipping the part where they figure out what matters. Speed without learning is just burning money faster.
Automating the Wrong ThingAI efficiency is measured in output volume - lines of code, emails answered, tickets closed. But speed in the wrong direction is not progress. The real question is not what to automate but what to augment.
The Doorman FallacyThe instinct to replace people with machines is a pattern as old as the automatic door - and it keeps producing the same hollow outcome.
Most Work Is Not ContentThe dominant use case for AI is generating text - emails, reports, code, marketing copy. But most of what people actually do at work has nothing to do with producing content. Until we move past the content generation lens, we will keep underestimating what AI can do and failing to apply it where it matters.
There Is No Product in ProductivityWe have never been more productive and yet we have never shipped less that matters. AI made every idea feel worth pursuing. That is the trap.