Peter Lae

I ve been thinking about how much design quality actually matters in the earliest stages of a product.

Some users don t seem to mind rough edges if the tool genuinely solves a problem. Others instantly bounce if the UI doesn t feel trustworthy.

Despite the hype, the frontier of AI might not be where most people are looking.

While Big Tech continues to throw billions at scaling transformer-based LLMs, a recent Delphi study shows that 76% of leading AGI researchers no longer believe models like ChatGPT will meaningfully contribute to superintelligence. The marginal gains are becoming too expensive

GPT-5 is estimated to cost up to $1B to train, yet the improvement is mostly superficial. What we re getting is better packaging, not better reasoning.

Meanwhile, a quiet shift is happening in the background. Research in alternative architectures is starting to show surprising results. Liquid Neural Networks, for example, are achieving task-specific performance far beyond what transformers deliver using a fraction of the resources. Neuromorphic chips are pushing 10x energy efficiency. And miniaturized, domain-specific models are proving far more practical at the edge than scaled-up giants.

Hey folks,

It's a big day for us  not only have we launched something that I've been this much excited about in years, we are also climbing the Product Hunt leaderboard to become the product of the day. Checkout Indy AI.