Dismissing AI as non-intelligent may feel safe, but it carries a hidden implication: much of what humans do might not qualify either. The debate exposes a deeper tension—not about machines, but about how fragile, flexible, and perhaps self‑serving our definition of intelligence really is in practice, especially when it threatens our sense of uniqueness.
If AI can generate working software in minutes, why does software architecture still matter? As prototyping becomes cheap and iteration nearly instant, traditional upfront design starts to look increasingly obsolete. Yet this shift raises a critical tension: while speed and experimentation improve, we may be trading away long-term structure, maintainability, and scalability in the process.
What happens when anyone can build software instantly? As AI drives marginal costs to near zero, the industry begins to resemble a gold rush—except the winners may not be the builders. Companies like OpenAI are selling the “picks and shovels,” capturing value while developers face intensifying competition and shrinking margins.
An algorithm tax may seem like a natural response to the rise of AI, but it misunderstands how digital markets work. As software becomes abundant and cheap to produce, competition drives prices and profits toward zero. Instead of generating broad taxable value, AI concentrates economic gains in a few dominant platforms, making taxation far more complex than it appears.
OpenAI thinks it’s found the root of AI “hallucinations” — and a way to fix them. The idea? Teach models to admit when they don’t know instead of bluffing. But here’s the catch: a more cautious ChatGPT might refuse answers so often that users lose patience, and costs could soar. The solution could strip away the bold confidence that made ChatGPT irresistible in the first place.
AI isn’t just changing the workplace—it’s reshaping who even gets to enter it. A groundbreaking Stanford study reveals that the very workers once seen as the future—22 to 25-year-olds in tech and customer service—are now its first casualties. Entry-level jobs in AI-exposed roles have plunged by double digits since late 2022, while older workers hold steady.
This blog post presents a cheat sheet for working with ChatGPT, a large language model developed by OpenAI. The cheat sheet includes a range of useful commands and tips for fine-tuning the model, generating text and controlling the level of context and specificity in the model's responses. At least ChatGPT thinks so.
Welcome to the world of AI, where robots are smarter than humans, and humans are just a bunch of useless bags of flesh who are completely dependent on them. Yup, you heard it right, the future of AI is here, and this is the world where robots will take over all the jobs, leaving us with nothing but leisure time to play video games, watch TV, and scroll through our Facebook feeds.
It all began with DALL-E, a machine learning model developed by OpenAI to generate digital images from natural language descriptions. But it is closed source and not open to the public. Yet on August 22, 2022, the AI art community was shaken by the release of an open-source alternative, Stable Diffusion, which you can install and run locally using modest hardware. Within days, PCWorld described it as the new killer app. And it surely is.
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As of June 13, 2023, Google will shut down all so-called "conversational actions" for their Google Assistant. Every action that involves a speech dialog, like food ordering, games, educational apps, story telling, every application that requires questions and answers, will be canceled. Effectively, the Google Assistant is crippled down to a simple command-and-control interface.