We’re very quickly moving in a direction where there will be a battle between AI platforms. Surely one of the key reasons OpenAI was in such a hurry to release ChatGPT recently was to gain as much global market share of users as possible. Taking any longer than a year to release ChatGPT would have threatened to take them out of contention for the earliest adopters in what could be an increasingly crowded field.
There was a rumor that the release of ChatGPT caused a “Code Red” alert at Google, but the search engine giant is readying a solid response of its own. Google will soon release BARD, a chatbot trained with human feedback and developed by its AI research lab, DeepMind. There’s little doubt that Google envisions BARD as a ChatGPT competitor, touting BARD’s ability to reduce the risk of unsafe and inappropriate answers by providing the source it is using.
Meanwhile, Amazon has told its employees to stop using ChatGPT and feeding it information. The reason? Amazon will be producing its version of a ChatGPT-like tool, so it doesn’t need to help provide information to a competing tool.
Of course, that’s to name a few. In truth, within two years, we can expect to see as many as ten different platforms competing against each other. The challenge will be that people don’t want to have to feed much information into a platform such as ChatGPT. It requires specific expertise to “train” these platforms, or you risk generating results that don’t align with your request.
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The beauty and the curse of ChatGPT and the next platforms to come is that the sky may be the limit in its capability, but the people able to direct it with great accuracy are limited. Take operating systems as an example of this dynamic. Ever wonder why there are such a limited number of operating systems? Because there are a minimal number of programmers writing code.
The New Profession Of Machine Learning Trainers
The ever-evolving AI platforms will create a new profession of people who understand how to provide highly specialized training to machine learning systems. Sam Altman, the CEO of OpenAI, has frequently spoken about companies worth $1+ trillion in revenue emerging from teams that can effectively harness the potential of AI and build new layers of development upon this early foundation. As soon as people start figuring out what it will take to build the next layer on top of AI platforms (and how many platforms can support that), they can pinpoint the type of money they need to commit to continued growth.
ChatGPT might have been the first platform out of the gate, but if we assume that there will be ten platforms or more, there’s no question that a battle for survival will occur based on which platform can attract enough investment. Is there room for many platforms to support certain verticals? Frankly, it’s hard to envision that outcome.
Altman himself has stated that the number of platforms will ultimately be limited. There may be as many as 15 to 20 “contenders”, but that number could quickly be narrowed to five platforms or less. How? In a word: Financing. A company needs billions of dollars to enter this competitive field and if it doesn’t obtain sound, consistent financial backing, there’s little point in pursuing entry.
Early in the race for platform superiority, the irony is that the winners may ultimately owe their victory not to a new platform but to their existing ecosystem. Microsoft, for example, can build ChatGPT into all of its products, making it near impossible for people to move away from that platform. Microsoft CEO Satya Nadella has stated as much. Many will wind up being on the platform without even realizing it. Since Microsoft already has such a robust ecosystem, people have already bought into it across the board. Add ChatGPT integration to the mix and Microsoft’s position just became stronger than ever.
Nadella has described multiple levels of expertise required with building platforms. One of those critical layers of expertise he highlights that Microsoft has developed is the ability to build an environment where one can train ChatGPT. In other words, it’s one thing to understand how to build a massive computing environment. Having the expertise to build systems that perform all the necessary training within that computing environment is another.
Where will such data expertise come from? Of these numerous platforms, which ones will gain legitimate support and funding? Will these coveted sources of financing be exclusive to one platform?
We can best answer these questions by citing a famous movie quote:
Fasten your seatbelts. It’s going to be a very bumpy ride.
A new breed of technical talent that understands how to train machine learning systems and work with such systems will be ideal for many companies that seek to pull ahead. Is that the kind of rare individual your competitor is already talking to? Perhaps that’s why you should Talk To Talman First.
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