Calling AWS and Microsoft
We may need an AWS or perhaps a Microsoft to make sense of the surfeit of AI options. I say “perhaps a Microsoft,” because the market seems to need something akin to what Microsoft did for networking newbies: clear documentation, intuitive user interfaces, etc. AWS won big for the first decade of cloud computing by giving developers familiar primitives, i.e. the same LAMP building blocks they had in on-premises environments but with the flexibility of elasticity.
By contrast, read through the marketing description of Amazon SageMaker. AWS talks about “an integrated experience for analytics and AI with unified access to all your data” (sounds good) using “familiar AWS tools for model development, generative AI, data processing, and SQL analytics” (also good; don’t make developers learn new tools). But then AWS falls into the trap of insisting that developers want and need “purpose-built tools.” “Purpose-built” feels like a euphemism for “we’re going to offer you everything,” so much in fact that figuring out which model to use may start to seem like a coin toss rather than a clear decision.
Again, Microsoft won big in networking, operating systems, and developer tools by offering opinionated, easy-to-use options for mainstream IT administrators, developers, etc. These never appealed to the alpha geeks but guess what? The real money isn’t in appeasing the alpha geeks’ appetite for arcane options of infinite configurability. The real money is in providing easy options for people who may like technology but care even more about being able to get home in time for their kids’ games, bowling night, or whatever.
