Funny you should say that. Google just announced today that they are giving all college students one year of free Gemini advanced. I wonder how much that will actually move the needle among the youth.
Take-home assignments are basically obsolete. Students who want to cheat, can do so easily. Of course, in the end, they cheat themselves, but that's not the point.
I'd like to burst into a post a number of the unbelievable akin mishandlings of academic tasks I was reported, but. I do have a number of prize-worthy anecdotes that compete with yours. Nonetheless. Let us fight farce with rigour.
Even when the tasks are not in-depth, but easier to assess, you still require a /reliable evaluator/. LLMs are not. Could they be at least employed as a virtual assistant, "parse and suggest, then I'll check"? If so, not randomly ("pick a bot"), but in full awareness of the specific instrument. That stage is not here.
ChatGPT seems to have a name recognition / first-mover advantage with college students now, but is there any reason to think that will stick when today's high school students are using Gemini on their Chromebooks?
I built a product that uses and LLM and I got curious about the quality of the output from different models. It took me a weekend to go from just using OpenAI's API to having Gemini, Claude, and DeepSeek all as options and a lot of that time was research on what model from each provider that I wanted to use.
For enterprise practically any SaaS gets used as one more thing to lock them into a platform they already have a relationship with (either AWS, GCP or Azure).
It's actually pretty dangerous for the industry to have this much vertical integration. Tech could end up like the car industry.
I'm aware of that. I'm an EM for a large tech company that sells multiple enterprise SaaS product.
You're right that the lock in happens because of relationships, but most big enterprise SaaS companies have relationships with multiple vendors. My company relationships with AWS, Azure, and GCP and we're currently using products from all of them in different products. Even on my specific product we're using all three.
When you've already got those relationships, the lock in is more about switching costs. The time it takes to switch, the knowledge needed to train people internally on the differences after the switch, and the actual cost of the new service vs the old one.
With AI models the time to switch from OpenAI to Gemini is negligible and there's little retraining needed. If the Google models (now or in the future) are comparable in price and do a better job than OpenAI models, I don't see where the lock in is coming from.
There isn’t much of a lock-in, and that’s part of the problem the industry is going to face. Everyone is spending gobs of money on training and if someone else creates a better one next week, the users can just swap it right in. We’re going to have another tech crash for AI companies, similar to what happened in 2001 for .coms. Some will be winners but they won’t all be.
It seems more and more like AI is less of a product and more of a feature. Most people aren't going to care or even know about the model or the company who made it, they're just going to use the AI features built into the products they already use.
That's going to be true until we reach AGI, when there will be a qualitative difference and we will lose our ability to discern which is better since they're too far ahead of us.
How will it lock in the enterprise if its market share of enterprise customers is half that of Azure (Azure also sells OpenAI inference, btw), and one third that of AWS?
The same reason why people enjoy BigQuery enough that their only use of GCP is BigQuery while they put their general compute spend on AWS.
In other words, I believe talking about cloud market share as a whole is misleading. One cloud could have one product that's so compelling that people use that one product even when they use other clouds for more commoditized products.
Came to say this. No respectable CTO would ever push a Google product to their superiors knowing Google will kill it in 1-3 years and they’ll look foolish for having pushed it.
That isn't what I'm seeing with my clientele (lots of startups and mature non-tech companies). Most are using Azure but very few have started to engage AI outside the periphery.
