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by gpt5
990 days ago
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One realization I had is that tech advantage might in fact become a disadvantage. Consider companies that have invested heavily in building a technological edge. Google Translate, for instance, faces challenges as a simple prompt can overshadow its billion-dollar product. Similarly, Grammarly's competitive edge may now rely more on its momentum and user interface than on its underlying tech. As ChatGPT introduces new capabilities, countless products see their technological edge vanish. To illustrate, the introduction of the image input feature means that, with a single prompt, it could serve as a top-tier school homework assistant, a photo-based calorie counter, and a plant identifier all at once. This dynamic raised into question the viability of ML research as a core business strategy. Take Midjourney, for example. They've made significant strides and achieved dominance with their advanced text-to-image generation technology. But if a product like DALL-E 3, or its successors, could render their entire offering redundant in a few short years, than it's a tricky path for a company to take. To me, this suggests that the actual "new strategy in the age of AI" is that tech companies need to transition from relying on their tech edge as their competitive advantages, to relying more on more stable moats. For example, the network effects rooted in two-sided marketplaces. It also hints that tech giants like Google, who above all relied on their tech advantage, could face existential challenges in the coming decade. A sort of a win-or-die situation. While companies like Amazon might be in a more stable ground for now. |
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Data is the new oil in the age of AI. The companies that do well will have products that siphon context enriched user behavior, build a strong brand with user loyalty, and effectively capitalize on the collected data to automate some expensive task. These data collection apps will be designed to break down and gamify tasks in such a way as to maximize the training value of the resulting data stream.
For example, imagine an IDE with an integrated stack overflow type service, where people could do collaborative coding or request help and get answers inside the application. That would give edit-by-edit updates, console output, problems with solutions and user solution preference. The company that owned that data would have a huge leg up on the competition in terms of creating AI software generation tools.