“ Embrace services. There are huge opportunities to meet the market where it stands. That may mean offering a full-stack translation service rather than translation software or running a taxi service rather than selling self-driving cars. Building hybrid businesses is harder than pure software, but this approach can provide deep insight into customer needs and yield fast-growing, market-defining companies. Services can also be a great tool to kickstart a company’s go-to-market engine – see this post for more on this – especially when selling complex and/or brand new technology. The key is pursue one strategy in a committed way, rather than supporting both software and services customers.”
Exactly wrong and contradicts most of the thesis of the article - that AI often fails to achieve acceptable models because of the individuality, finickiness, edge cases, and human involvement needed to process customer data sets.
The key to profitability is for AI to be a component in a proprietary software package, where the VENDOR studies, determines, and limits the data sets and PRESCRIBES this to the customer, choosing applications many customers agree upon. Edge cases and cat-guacamole situations are detected and ejected, and the AI forms a smaller, but critical efficiency enhancing component of a larger system.
The thesis of the article is that this is going to be called consultancy.
Single-focus disruptors bad. Generic consultancy good - with ML secret sauce, possibly helped by hired specialist human insight.
Companies that can make this work will kill it. Companies that can't will be killed.
It's going to be IBM, Oracle, SAP, etc all over again. Within 10 years there will be a dominant monopolistic player in the ML space. It will be selling corporate ML-as-a-service, doing all of that hard data wrangling and model building etc and setting it up for clients as a packaged service using its own economies of scale and "top sales talent" (it says here).
That's where the big big big big money will be. Not in individual specialist "We ML'd your pizza order/pet food/music choices/bicycle route to work" startups.
Amazon, Google, MS, and maybe the twitching remnants of IBM will be fighting it out in this space. But it's possible they'll get their lunch money stolen by a hungry startup, perhaps in collaboration with someone like McKinsey, or an investment bank, or a quant house with ambitions.
5-10 years after that customisable industrial-grade ML will start trickling down to the personal level. But it will probably have been superseded by primitive AGI by then, which makes prediction difficult - especially about that future.
The big consulting firms have been building in-house ML libraries for common business problems for 3+ years. They don't need to acquire the data startups because as the article points out, these models are commoditized pretty quickly (especially when you have access to the transactional data of many large multinational companies). There is no secret sauce to ML that makes you any more likely to succeed with it than Accenture -- and they have a much deeper pipeline than you do. ML is a mature capability at all of the enterprise-tier consultancies, and they bundle it with their $100M system deployments. The mid-market consultancies are working on it. There is very little money to squeeze out of this market.
We're also a long way off from AGI. Nobody really even has a roadmap to what an AGI would look like. Heck, DNN/ML techniques have been widely-known since the early 90s; they just became practical with access to cloud-scale hardware, so the current situation has been 25+ years in the making.
Exactly wrong and contradicts most of the thesis of the article - that AI often fails to achieve acceptable models because of the individuality, finickiness, edge cases, and human involvement needed to process customer data sets.
The key to profitability is for AI to be a component in a proprietary software package, where the VENDOR studies, determines, and limits the data sets and PRESCRIBES this to the customer, choosing applications many customers agree upon. Edge cases and cat-guacamole situations are detected and ejected, and the AI forms a smaller, but critical efficiency enhancing component of a larger system.