![image.png](https://files.peakd.com/file/peakd-hive/fullcoverbetting/23u5ztHMAixMfoaUsABTkzWbFZLSWaL38pbvN1wUFBa2wYSHHVzSTNRwAhnLxRWYM4Xox.png)

I used to believe AI couldn't truly replace human work. My perspective was that it was a powerful tool, a sophisticated calculator even, but ultimately an assistant to human ingenuity. Based on the lessons learned over the last few days – in a rather unexpected journey into Hive data – I now believe AI is fundamentally changing what it means to be a developer. It's not just a tool; it's a co-developer, a discussion partner, and a force accelerating the 'citizen-developer' revolution.

My recent journey into AI began with a practical, if somewhat ambitious, goal: to build a Premier League prediction module using Gemini. I saw AI as a way to leverage vast amounts of data to find patterns and make informed guesses, a natural extension of my long-standing love for statistics. My roots are in tracking progress; back in the day, I had an incredibly extensive Excel sheet to monitor my Steem/Hive progress. I still keep an eye on dashboards like PeakD and fondly remember the https://hiveanalytics.usehive.com/ which has been discontinued due to a lack of support. Real pitty.  For me, understanding the game — not just playing it — has always been key. And while I do know how the game should be played, I still stink in it.

However, as is often the case, one thing led to another. My inherent fascination with Hive's underlying mechanics and payout structures quickly drew me away from football predictions. I found myself wanting to understand the 'unwritten rules' of success on Hive beyond the common wisdom. How do top posts gain traction? What voting patterns truly lead to significant rewards?

# My AI as a Development Team
This is where my interaction with AI took an unexpected turn. Instead of simply asking for data, I started asking how to get the data.

My initial approach was rudimentary: manually cutting and pasting data from various Hive sources. Frustrated by the inefficiency, I turned to AI with a clear goal: help me find the right Hive APIs and construct Python scripts to automate this data extraction. What transpired wasn't a series of commands and responses; it evolved into a genuine, iterative discussion that felt less like interacting with a chatbot and more like a sprint meeting with a development team.

"My questions to the AI often started to resemble a discussion with a development team, and the output wasn't just text, but immediately workable Python scripts." Immediately working is not true. We had to solve some issues. But still.

We discussed requirements, brainstormed solutions for tricky API limitations, and debugged frustrating errors like *InvalidParameters* from the beem library. The AI didn't just provide code; it helped me understand why certain approaches failed and how to implement more robust solutions, like direct-save pagination with start_entry_id for my blog history scraper. This wasn't just an analysis task; it was collaborative problem-solving and implementation.

This experience shifted my entire perspective. AI, at least in this context, wasn't just making analyses; it was actively participating in the development process, guided by my questions and requirements. The core analytical tasks, the "what should the system do?" and "what problem are we solving?" questions, remained firmly in my human domain. But the "how do we code it?" was increasingly delegated to my AI co-developer.

# The Rise of the Citizen-Developer
This leads me to a profound realization about the future of software development: AI will not necessarily replace all developers, but it will significantly reshape the developer's role and potentially reduce the overall number of developers needed for certain tasks in a general development team.

I believe AI will excel at:

- Automated Testing: Generating comprehensive test suites, a task often seen as tedious but critical, will become highly efficient.
- Boilerplate and Micro-Code Generation: Writing small, repetitive code blocks, common functions, or even entire basic modules will be instant.

My personal experience is a testament to this: I cannot write JavaScript or HTML myself. Yet, I am confident that, together with AI, I could set up a fully functional, aesthetically pleasing website in a matter of days without needing a single dedicated front-end developer. This is the essence of the 'citizen-developer' revolution: individuals with strong logical thinking and problem-solving skills, but perhaps lacking deep coding expertise, can now bring complex ideas to life.

# The Obligation of Trust: Responsibility, Review, and the Safety Switch
While embracing this massive leap in technology, we must acknowledge that blind faith is reckless. It is the responsibility of every individual—developer, analyst, or citizen-user—to learn how and where AI can truly help us, and perhaps more importantly, where it cannot.

We must always build in the necessary safety switches. We certainly don't want an AI automatically launching a rocket based solely on its own data analysis; human approval must always be the final gate. There are movies enough about this and hopefully not some real life stories which we will never hear.

For the developer, this means transparency is paramount:

- Verification: A genuine developer or analyst must still step through the code or the analysis to fully understand why the AI arrived at a conclusion.
- Explainability: Great progress has been made in this field. We're moving past the era where AI defined a photo of a dog as a cat without any rationale. Systems are becoming better at showing the specific lines of code or data points that led to a particular output.

We shouldn't trust blindfully what AI provides us, but there is nothing wrong with leveraging it. In fact, it can transform even the most mundane chores. For example, my garage has, after several years, become a chaotic mess where a car could no longer fit. I simply took a few photos, sent them to the AI with a request for an action plan, and the resulting, detailed organizational strategy was astonishing. This simple, non-technical use case perfectly illustrates how AI is ready to help us improve our lives in countless ways—provided we take the time to learn its capabilities and apply the necessary human judgment.

# The Irreplaceable Human Mind
Ultimately, AI will be capable of what we teach it. It can certainly uncover complex connections and patterns that would take humans years to identify. It can accelerate the implementation of ideas at an unprecedented rate.



However, AI will never replace the human brain's capacity for true curiosity, for defining a purpose, or for asking the initial profound questions that spark innovation. The quality of AI's output, whether it's an analysis or a Python script, will always be directly proportional to the quality of the discussion and the questions posed by its human partner.

The future isn't about AI replacing us; it's about AI empowering us to achieve more, faster, and with greater efficiency than ever before. It's about turning sceptics into co-developers, and that, for me, is an exciting prospect.

Cheers,
Peter

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