There\u2019s a common critique in the AI community: too often, AI solutions emerge
\nfrom asking, \u201cWhat can we do?\u201d rather than, \u201cWhat problem do we need to solve?\u201d
\nDevelopers frequently jump on the latest algorithm or innovative model and then
\nsearch for a suitable use case. This approach tends to produce superficial
\nfixes\u2014flashy AI wrappers that look impressive on paper but fail to address the
\ncomplex, real-world challenges faced by businesses and consumers.<\/p>\n
I firmly believe that innovation should follow a different path: start by
\nidentifying a genuine problem and then apply even a suboptimal solution to
\naddress it. Discovering the right problem is far more critical than finding the
\nperfect solution from the outset. If a solution truly tackles a real issue, it
\ncreates the foundation upon which more advanced and refined approaches will
\neventually build.<\/p>\n
Consider the example of the Wright brothers. Their early flying machines were
\nsimple, even rudimentary, yet they marked the beginning of aerodynamics and
\naviation. The modest success of these initial designs paved the way for decades
\nof innovation, transforming what was once a toy into the sophisticated aircraft
\nwe know today.<\/p>\n
The Pitfalls of a Technology-First Mindset<\/strong><\/p>\n Building AI solutions solely because a new technology is available might seem This focus on technology can also divert attention from the real needs of users An example of this can be seen in the current trend where large The tech world has seen AI wrappers fall short by reusing old ideas in new Problem-First AI Development is the way to go<\/strong><\/p>\n For AI to truly matter, focus on the problem first. Instead of adding AI to Conclusion<\/strong><\/p>\n Generative AI expands our possibilities, but we must focus on real needs. There\u2019s a common critique in the AI community: too often, AI solutions emerge from asking, \u201cWhat can we do?\u201d rather than, \u201cWhat problem do we…<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"advanced_seo_description":"","jetpack_seo_html_title":"","jetpack_seo_noindex":false,"footnotes":""},"categories":[11,12,6,13],"tags":[],"class_list":["post-348","post","type-post","status-publish","format-standard","hentry","category-ai","category-innovation","category-programming","category-startups"],"jetpack_sharing_enabled":true,"jetpack_featured_media_url":"","jetpack-related-posts":[{"id":323,"url":"https:\/\/codingismycraft.blog\/index.php\/2024\/10\/20\/beyond-the-ai-hype-charting-a-path-to-true-success\/","url_meta":{"origin":348,"position":0},"title":"Beyond the AI Hype: Charting a Path to True Success","author":"john","date":"October 20, 2024","format":false,"excerpt":"Artificial intelligence (AI) is undoubtedly here to stay. It has been a part of our technological landscape for decades, advancing from theoretical concepts to real-world applications. However, despite AI's persistence and gradual evolution, the current hype surrounding it is not entirely justified. We find ourselves in the midst of a\u2026","rel":"","context":"Similar post","block_context":{"text":"Similar post","link":""},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":374,"url":"https:\/\/codingismycraft.blog\/index.php\/2025\/05\/27\/the-hidden-cost-of-skipping-the-fundamentals-in-the-age-of-ai\/","url_meta":{"origin":348,"position":1},"title":"The Hidden Cost of Skipping the Fundamentals in the Age of AI","author":"john","date":"May 27, 2025","format":false,"excerpt":"The Problems of Shallow Learning One profound shift I\u2019ve observed recently is how AI has made it incredibly easy to start using new technologies without really learning them in depth or sometimes even without learning them at all. While this approach can feel like a productivity boost, I believe it\u2019s\u2026","rel":"","context":"Similar post","block_context":{"text":"Similar post","link":""},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":21,"url":"https:\/\/codingismycraft.blog\/index.php\/2023\/11\/11\/how-to-hunt-for-hidden-treasures-lessons-from-a-clever-locksmith\/","url_meta":{"origin":348,"position":2},"title":"How to Hunt for Hidden Treasures: Lessons from a Clever Locksmith","author":"john","date":"November 11, 2023","format":false,"excerpt":"Picture yourself as the best locksmith who can unlock any door in the world. You\u2019ve spent many years, hard work, and money to become an expert at unlocking doors. Now, you\u2019re looking to benefit from all the effort you\u2019ve put into it. 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In deed this is how science and technology are progressing and naturally the same principle applies to any human activity which can\u2026","rel":"","context":"In "Programming"","block_context":{"text":"Programming","link":"https:\/\/codingismycraft.blog\/index.php\/category\/programming\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":211,"url":"https:\/\/codingismycraft.blog\/index.php\/2020\/07\/01\/thinking-about-algorithmic-problems\/","url_meta":{"origin":348,"position":4},"title":"Thinking about Algorithmic problems","author":"john","date":"July 1, 2020","format":false,"excerpt":"In this posting we will discuss algorithmic thinking and try to propose a way of thinking that might become helpful when trying to solve a problem that does not have an obvious solution and some level of improvisation is required. 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\nappealing at first. However, when developers start with a pre-made solution or
\nan over-fitted algorithm, they risk missing the nuances of the actual problems.
\nWithout fully grasping the intricacies and constraints involved, any resulting
\nAI model might scratch only the surface and ultimately produce a product that
\ndoesn\u2019t truly solve anything.<\/p>\n
\nand stakeholders. When the emphasis is on showcasing cutting-edge algorithms or
\nleveraging popular frameworks, the solution might end up as little more than a
\nshiny AI wrapper\u2014a superficial interface simply built around existing
\ncomponents.<\/p>\n
\nlanguage model (LLM) solutions are built around NVIDIA\u2019s ecosystem. While
\nNVIDIA\u2019s hardware and software are undeniably advanced, merely wrapping AI
\nfunctionalities around these infrastructures does not necessarily foster true
\ninnovation. Instead, it reminds us of the dotcom boom, where companies
\nrushed to build an online presence without a clear business model, or the
\ncrypto hype, where flashy technology promised fortunes but often failed to
\nresolve any substantive issues.<\/p>\n
\ncovers. Chatbots often fail as robotic because they don\u2019t integrate well
\nwith other systems. AI marketing tools simply repackaged existing data without
\nadding much value. Similarly, hiring systems that were meant to improve resume
\nscreening ended up missing subtle human skills, leading to overlooked talent.
\nTheir reliance on fixed rules made them fragile, much like the tech that failed
\nduring the dotcom crash.<\/p>\n
\nflashy but shallow systems, start by understanding the challenge and defining
\nits key issues. This approach builds tools that really meet user needs. Engage
\nwith end users to learn their pain points and collect high-quality data for
\nrobust models\u2014much like the sustainable methods that helped companies recover
\nafter the dotcom crash. Finally, consider ethical and social impacts; this
\ncareful evaluation builds trust and ensures your AI evolves responsibly and
\nbenefits everyone.<\/p>\n
\nInstead of chasing flashy tech, we should tackle clear challenges, listen to
\nusers, use quality data, and care about ethics. As the dotcom and crypto booms
\nshowed, true success comes from solving real problems, not just seeking for a
\nproblem to apply new technologies and algorithms.<\/p>\n","protected":false},"excerpt":{"rendered":"