4 ms·
You can't build a moat with AI
- VeejayRampay 3y agothe best model is not GPT-4 it's April of 2024
- cpursley 3y agoWhat's the best model now?
- hypoxia87 3y agoClaude 3 Opus
- minimaxir 3y agoOpus is an order of magnitude more expensive and slower than GPT-4.
- VeejayRampay 3y agodoesn't matter that it's more expensive though, it beats it in benchmarks also, it's not "an order of magnitude" more expensive
- mattlondon 3y agoThis is probably why Google or Meta will "win" over OpenAI in the end. They have all the data, and no one else even comes close.
- altdataseller 3y agoMicrosoft has plenty of data too. In Microsoft Teams, LinkedIn posts and messages, and Outlook emails.
- hypoxia87 3y agoPlus every company's files in OneDrive and SharePoint.
- dumbo-octopus 3y agoMicrosoft 365 (nee Office 365) as well. And Dynamics 365. And GitHub. And OneDrive. And SharePoint. And Power Platform. Honestly I think they might have more useful data than Google, given Bing knows more or less that same as GoogleBot. Meta doesn't come close, unless you want your LLM to be purely conversational.
- ilovetux 3y agoDon't forget they have github as well.
- endofreach 3y agoI wouldn't worry about microsoft delivering quality in anyway.
- AnthonyMouse 3y agoNobody has explained how they could use that data without producing a model that would emit private information.
- abrichr 3y agoPerhaps de-identification before training could be helpful here. Microsoft does seem active in this, e.g. https://microsoft.github.io/presidio/ https://microsoft.github.io/presidio/
- AnthonyMouse 3y agoNone of that stuff actually works. You can remove someone's social security number from the data but there is still only one person at the exact intersection of all the data that isn't individually considered personally identifying but collectively it still is. Moreover, that isn't even the problem here. Suppose your company has a trade secret. You know how to manufacture widgets more efficiently than your competitors. If Microsoft produces a model that will now tell your competitors your secret process that it learned from your internal emails, it's completely irrelevant whether they stripped the PII out of your emails first.
- greenavocado 3y agoLarge companies like Google will fail at making the most successful LLMs because of internal cultural problems.
- altdataseller 3y agoGo away ChatGPT
- sumeruchat 3y agoSo annoying. I feel sorry for people who actually read that paragraph.
- htrp 3y ago> We firmly believe the moat for AI application is in the data and the data engineering today. At some point, the process of building custom LLMs might get so fast and easy that we’ll all return to building our own models. That simply isn’t the case today. Customized small models will outperform larger general models for your specific use case.
- sumeruchat 3y agoNo they wont. Any model you train now will be beat by GPT5 easily
- crooked-v 3y agoI think the real power of customized small models will be running things on local hardware, except that we're in an awkward phase where the local hardware isn't quite beefy enough to run anything really useful yet. Maybe Apple will do something interesting in that space at WWDC.
- sumeruchat 3y agoAlso not feasible. A network request to groq type machines will outperform your local hardware by such a huge amount that it wont make sense other than some very niche tasks
- transitorykris 3y agoNetwork availability, latency, privacy, etc. many qualities to consider beyond model size and performance for applications.
- happypumpkin 3y agoAnd cost-efficiency, if I'm using an LLM as an Siri-like assistant on my phone, most of the tasks I'll want it to do won't be that complicated and it would be a waste to send them to some SOTA LLM in the cloud, which I'll have to pay for by a monthly subscription or on a per-token basis.
- BadHumans 3y agoIf you can't build a moat with technology you build it politically which Altman is already trying to do.
- deleted 3y ago[deleted]
- altdataseller 3y ago“What that also means is that you don’t need to be an AI genius to succeed in building applications. With thoughtful software engineering and a focus on customer data, you’ll build a moat over time.” This sounds encouraging at first glance, but even more demoralizing when you think about it. It doesn’t matter how clever or smart you are over your competitors. If you don’t have the data, you don’t stand a chance. And of course the incumbents have the data not you, the entrepreneur
- killthebuddha 3y agoI don't see it that way. If you build a genuinely novel application then the critical data doesn't exist yet (IMO almost by definition). Sometimes it's easier for a startup to do this rather than for an incumbent who's trying to shoehorn the application into a preexisting framework (technical, operational, whatever).
- marcosdumay 3y agoIf the data doesn't exist, you can't train an (neural network) AI as they exist today. Whatever thing people build, will be based on data that exists, not data that will be created afterwards. And guess what, you don't have that data.
- killthebuddha 3y agoYou could bootstrap a niche model using what's available and go from there. I don't think it's all that different from any other kind of bootstrapping startups tend to require.
- MichaelZuo 3y agoBootstrap a niche model based on what? On the yet to exist data?
- gravitronic 3y agoBefore I clicked I expected the article to be about LLM's failures in structural engineering applications
- minimaxir 3y ago...which is why OpenAI is focusing more on enterprise sales and unique value-propositions such as the GPT Store which can't trivially be imitated by competitors. This post seems to misunderstand what a "moat" is in a business sense (and unfortunately a lot of AI hypesters on social media do as well). The fact that LLMs are becoming a commodity was the point of the original "OpenAI has no moat" memo by Google, which has proven to be accurate.
- ianbicking 3y ago"It might feel like your applications’ prompts or prompt templates are a good form of differentiation. After all, your top-notch engineering team has invested days into tuning them to have the right response characteristics, tone, and output style. Of course, giving your competitors your prompts would probably accelerate their progress, but any good engineering team will figure out the right changes quickly. The main reason is that the experimentation (with the right evaluation data!) is quick and easy — trying a new prompt template isn’t much harder than writing it out. All it really takes is a little bit of patience, some creativity, and extra Azure OpenAI credits." And yet, over and over, I see products with output that clearly comes from simple and frankly lazy prompting. You can do a lot with prompting, but engineerings are not putting in the work! (If an engineer is even the right person... probably not, given any specific application of an LLM.) Prompting also isn't so reductive that you just write an evaluation and then iterate on the prompt until you satisfy the evaluation. Prompting is a co-creative exercise between the LLM, the domain expert, the product, and the user. And sure "data" fits in there, as well as relationships, comprehensibility, workflows, etc etc... the AI component is just a small piece of any full application.
- deleted 3y ago[deleted]
- hintymad 3y ago> The real differentiation lies in your data you feed into your models. It's more than data. Steven Jobs used to tell the founders of the Segway that their secret technologies would leak sooner or later, even if they set up their factories in the middle of a desert in Nevada. His advice to the founders was that they needed to build a product that users couldn't get away from even if all the competitors had the same technology (or so I remember). That could be a process, an ecosystem, market penetration with an amazing supply chain (think about the largest seller of straws in the world. The unit price of a single straw is close to 0, which is really hard to achieve), and etc. To me, data will just be one key component of the moat but not all. The moat of AI is the same ol' entire ecosystem: an infrastructure that is so efficient that the company can keep driving down the unit price of hosting the AI models; a fabulous culture to enable the company to keep churning out improvements; an extensive data platform and the associated process and sources that keeps provisioning quality data, a number of killer applications...
- satisfice 3y agoThere’s nothing you can do to make AI apps reliable. You can experiment all you like and play with prompts… in the end you will have a flakey product.
- CatWChainsaw 3y agoHallucinations are features not bugs and all that.
- morpheos137 3y agoYou can build a moat and fill it with bullshit scraped from Reddit. That's all AI is anyway. Sorry to burst anyone's hype bubble.
- navaed01 3y ago“ For reference, our team at RunLLM has spent roughly 70% of our engineering cycles in the last quarter on data engineering — everything from pre-processing data at ingestion time to implementing hybrid search to reranking results.” This to me is why I am actually bullish on building ‘LLM wrappers’ that are sustainable businesses. While you might not be developing new technology, you can be adding value to a client who can use chat gpt and “talk to your docs” but not the surrounding work to make it frictionless for their workflow or company and actually build a business. It’s entirely possible that AI will get so good that it makes it easy to all the surrounding tasks too.