[HN Gopher] You can't build a moat with AI
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You can't build a moat with AI
Author : vsreekanti
Score : 28 points
Date : 2024-04-11 19:19 UTC (3 hours ago)
(HTM) web link (generatingconversation.substack.com)
(TXT) w3m dump (generatingconversation.substack.com)
| VeejayRampay wrote:
| the best model is not GPT-4 it's April of 2024
| cpursley wrote:
| What's the best model now?
| hypoxia87 wrote:
| Claude 3 Opus
| minimaxir wrote:
| Opus is an order of magnitude more expensive and slower
| than GPT-4.
| mattlondon wrote:
| This 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 wrote:
| Microsoft has plenty of data too. In Microsoft Teams, LinkedIn
| posts and messages, and Outlook emails.
| dumbo-octopus wrote:
| Microsoft 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.
| hypoxia87 wrote:
| Plus every company's files in OneDrive and SharePoint.
| ilovetux wrote:
| Don't forget they have github as well.
| endofreach wrote:
| I wouldn't worry about microsoft delivering quality in
| anyway.
| AnthonyMouse wrote:
| Nobody has explained how they could _use_ that data without
| producing a model that would emit private information.
| abrichr wrote:
| Perhaps de-identification before training could be helpful
| here.
|
| Microsoft does seem active in this, e.g.
| https://microsoft.github.io/presidio/
| greenavocado wrote:
| Large companies like Google will fail at making the most
| successful LLMs because of internal cultural problems.
| altdataseller wrote:
| Go away ChatGPT
| sumeruchat wrote:
| So annoying. I feel sorry for people who actually read that
| paragraph.
| htrp wrote:
| > 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 wrote:
| No they wont. Any model you train now will be beat by GPT5
| easily
| crooked-v wrote:
| I 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 wrote:
| Also 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 wrote:
| Network availability, latency, privacy, etc. many
| qualities to consider beyond model size and performance
| for applications.
| happypumpkin wrote:
| And 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.
| littlestymaar wrote:
| Except nobody but groq has such type of machines, and the
| economics of cloud AI is very hard to make it works in
| practice. Offloading the _capital_ cost (which is the
| hardest kind of cost to swallow for a company) to
| customers is very compelling business-wise.
| littlestymaar wrote:
| Not for most human language, or anything that requires
| business-specific context where what's publicly available
| lags behind the state of the art your business cares about.
|
| And of course, not if you care about token throughput more
| than fancy abilities. Or price for that matter.
|
| So for many if not most businesses needs GPT-4 isn't the best
| tool out there, and GPT-5 is the canonical example of a
| vaporware right now.
| VHRanger wrote:
| Past performance is not a predictor of future performance.
|
| While it's possible the gap between GPT5 and 4 is as big as
| between 4 and 3, it's unlikely. The gap between 2 and 3 was
| much larger as the one between 3 and 4 (and similarly between
| 1 and 2).
|
| Also, it's not clear that GPT5 will do this in an *economical
| way* once the spigot of investor money stops.
| jsemrau wrote:
| Tell more more about the data moat.
| marcosdumay wrote:
| I did expect that too, but it isn't happening reliably.
|
| But small customized models seem to perform close to as well as
| large general ones.
| altdataseller wrote:
| "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 wrote:
| I 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 wrote:
| If 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 wrote:
| You 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.
| gravitronic wrote:
| Before I clicked I expected the article to be about LLM's
| failures in structural engineering applications
| BadHumans wrote:
| If you can't build a moat with technology you build it
| politically which Altman is already trying to do.
| minimaxir wrote:
| ...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 wrote:
| "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.
| hintymad wrote:
| > 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...
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