There are always new markets which get ignored. Apple, Facebook, Google, Amazon, Microsoft did not compete with HP, Dell, Nokia they essentially entered a new market. Most ML startups don't offer a new product but rather just enable features and acquisition is often the intended end goal.
We expect that DNNs will a fundamental thread to computing going forward. AFGAM has effectively locked up all of the talent and data in the broadest markets already. Not only that they are building and deploying the frameworks that new products will be built on in those other markets. So they don't need to create the product, they just need YOU to use Tensorflow or CUDA or whatever they come up with that runs on GoogleCloudGPU or Azure Compute etc...
What you're missing is that it's not about making a few million as a startup. It's that they are so far ahead in what is the most fundamental game changing, final human technology that I don't see a future where they are unseated.
Can you share your reasoning behind this opinion? Humans have invented a lot of tools and technological platforms over the years, what makes you think ML is the final frontier?
There is no AI competition against a company that has Tensor Processing Units among the absolutely incomprehensible data store that Google has amassed.... Especially if you are not some world class mathematician or computer scientist. The best you can hope for is to be acquired if you are in the ML/AI space as a stand-alone entity. In most cases they can probably safely ignore the bulk of AI startups.
Also, those TPU's are available for rent.
Nice that TPU's are available for rent, but to what end? If I run something truly innovative on their hardware (TPU), with their software platform(Tensor)...is that actually innovation, will they have the means to just copy it? How do I compete with that?
As always you want to be careful about dependencies, but that doesn't mean rewriting everything.
I use chrome and little snitch is always telling on it. It calls home so much that I have just given up in order to have a decent experience and gave it full connection rights. I don't know how much of the browser Chrome owns, but I guess it is not insignificant. That is a lot of data that I will never have access to... let alone for my family nor on at Google scale.
Netflix uses AWS and competes with Amazon Prime... I get it. I do think that no one knows the whole story.
Getting back to OP... it has to be damn tiring/frustrating to even contemplate competing in AI/ML space with these big mega corps. I simply would not, that ship has sailed.
By the time they copy you, you're already big. I don't really see the problem as long as the tech companies don't hoard the information and spread the knowledge - which they are doing with initiatives like OpenAI and many more.
The big question is, what happens when they invent AI that builds its own programs and exponentially accelerates? Who needs startups at all then?
They not only have custom silicon (TPU), they have the software too. Do you have extraordinary insight|foresight|genius or have you discovered/developed a major breakthrough in AI/ML? If not, good luck competing with these companies. It is a resource problem at this stage, as consolidation happened and there is no going back.
The way I see it, consumers (at least right now) are free not to buy into the hype and consume whatever it is that these companies produce. So if prices are too high or quality too low, a new company can enter in.
The real danger I see is the possibility that essentially all consumers will be forced to pay for this stuff through some regulatory capture, in a similar fashion to the state of the healthcare industry in the US.
You can download the whole Wikipedia data dump at under 100GB uncompressed (text only - with media is around a TB). The entire common crawl with 3bn pages is only around 250 TB. While the Wikipedia dataset is too large to fit in ram for most people and the common crawl is too big to fit on a single disk, you can process these in your own local cluster quite easily and relatively cheaply.
Honestly I think the real breakthroughs to be made will be algorithmic and I don't believe those are out of reach for "civilians" outside of the tech giants.
You are stuck with 25TBs of wikipedia for the rest of forever. And "academic datasets".
Because the important data is probably not inferable from web links or otherwise semi-passive Internet structures.
That said, I think the missing component is still a good mind theory.
We need a lot of data currently because algorithms don't generalize like people do.
I'm not convinced you need to be a tech giant to be able to make that breakthrough - I think it's a problem of approach, not a lack of data.
It's just not textual.
Last I checked they all work for...AFGAM (or are teaching)