Everyday AI Podcast – An AI and ChatGPT Podcast - Ep 820: The Most Important AI Model You’ll Probably Never Use That Just Dropped

Starting point is 00:00:00 This is the Everyday AI Show, the Everyday Podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business, and everyday life. One of the more important AI models you've probably never heard of and likely won't use just got released. This may get slept on, but inkling is a huge release from Thinking Machines Lab. So if you haven't heard of Thinking Machines, they're led by Mira Maradi, the former CTO of OpenAI. So why is Inkling maybe the most important AI model you probably won't use? Because it's now the best open source model from an American company and Thinking Machines is betting on the future of AI, fine-tuning as a service. The model itself, though, it's multimodal, it's agentic, it's customizable, and it's available through enterprise distribution on day one.

Starting point is 00:01:10 Inklink doesn't need to beat every Chinese model or Claude Fable to be relevant. It only needs to peak the interest of a few cost-conscious enterprises to not only be extremely profitable, but to also help shift the conversation around enterprise AI. Frontier models are becoming so powerful, actually fine-tuned smaller practical models without much iteration or without even much expertise. So has a new category reemerged is fine-tuning back and what might thinking machines first major product mean for the broader AI competition? Well, here's the big picture. Inklink could change how enterprises buy AI. So this was just released Wednesday.

Starting point is 00:01:59 so hours ago. And it is not the smartest model, but it is strategically important. I think it gives companies a credible American open weight model alternative to Chinese models. And Inkling could be the best general purpose open model because it is multimodal, right? A lot of the open source, open weight Chinese models aren't. Many of them are text only. So Inkling, it is multimodal. It is agentic.

Starting point is 00:02:29 And as workflows in the real world lags so far behind model capabilities, I do think that there's a real market for bespoke middle of the pack AI. So on today's show, here's what you're going to learn. You're going to learn why an open model matters, even if you're never going to deploy it. You're going to understand how inkling reopens enterprise AI options beyond just Chinese open models. You're going to know why frontier models could make fine tuning practical beyond research. teams and you're going to know how model shopping is going to change budgets, vendors, and competitive leverage. All right, let's get to it. Welcome to Everyday AI. If you're new here, my name's Jordan Wilson. We do this every day. It's your daily

Starting point is 00:03:13 live stream podcast and free daily newsletter helping business leaders like you and me keep up with the nonstop AI updates because my gosh, can't take an hour off. I help you decide what's important. I tell you how to use all this information to grow your company and your career. So it starts here. with the unedited, unscripted, live stream podcast. But make sure you go to our website at your everyday AI.com. That's your cheat code. We're going to be not just recapping the highlights from today's show,

Starting point is 00:03:40 but go sign up for our free daily newsletter. We're going to be giving you everything else that you need to know that's happening in the world of AI today. Because, yeah, it's one of those things. You got to like go and work it out. It's a muscle. Use it every single day. And yeah, all the AI news will be in our newsletter.

Starting point is 00:03:57 All right. Let's get into it. Live Dream audience. to see you. Adam joining us from St. Louis, Jose from Santiago, Angie, joining from Montana, Amico, Tokyo. Brian, what's up, Brian, joining from Minnesota. So let's talk about maybe the most important AI model you'll probably never use. So there's a lot of different factors that have been compounding over the last, I would say, three months. And to put it in a very short summary.

Starting point is 00:04:33 Frontier models are probably, for the most part, too much for many enterprises, right? I'll say this, you know, probably the Fortune 500, they can squeeze as much juice and, you know, make it worth the cost. But I'd say for many companies, especially those probably in the Fortune 5 or like the Fortune 501 to the 5,000, right? So this isn't everyone. You know, I don't think this changes the equation for every single company, every single business out there because so many people are still going to want, you know, their chat GPT

Starting point is 00:05:09 enterprise, their, you know, Gemini, their Claude, their co-pilot. They want to make things easy and they're not necessarily, you know, looking at, okay, how do open models or fine-tuning models change our strategy. But for so many in our audience, it does. And this is a really big deal. So here's a little bit more about the model itself. So model, the model is from thinking machines, and it's called inkling. It is a 975 billion parameter model in its multimodal, so text, images, and audio.

Starting point is 00:05:43 What is interesting as well, previously thinking machines did demo, kind of a similar dual purpose or, you know, two-way street audio model as well. so that can kind of hear and listen as well as talk at the same time, like OpenAI's new GPT Live. So Inkling is led by former OpenAI CTO Miramarati, and its quick arrival raises, I think, the next big question, which is how good is it? Well, here's from the company themselves from their release. They say our model called Inkling is a mixture of experts transformer with 975 billion total parameters, 41 billion active. It supports a context window of up to one million to.

Starting point is 00:06:25 It was pre-trained on 45 trillion tokens of text, images, audio, and video. It is the first in family of models of different sizes. Alongside it, we are sharing a preview of inkling small, a lighter weight model with 12 billion active parameters, trained with a smaller recipe that achieves strong performance with even lower costs and latency. Inkling reasons natively over text images and audio and balances costs with performance, through efficient and controllable thinking effort. We trained it to be a broad, balanced foundation model, strong across many domains, flexible enough to adapt.

Starting point is 00:07:03 Inkling is not the strongest overall model available today, open or close. Instead, a combination of qualities make it a good open weight space for customization, multimodal capabilities, efficient thinking, and availability on Tinker for fine tuning. Inkling is just the start, our first release in a model family we will continue to build on. We want to make customization accessible for more use cases. So Inkling is available for fine tuning on Tinker today. Picking the right base model to fine tune is a qualitative judgment that combines measurable benchmarks with a unique feel of a model that comes from playing with it.

Starting point is 00:07:40 To enable the latter, we're adding the Inkling playground in the Tinker Council, a developer-facing interface for chatting with Inkling. To show what customization means in practice, we ask Inkling to fine-tune itself using Tinker, the model wrote its own fine-tuning job, ran it, and evaluated the result. So, long story, kind of short, right? If you want to go use tinkling, tinkling, right? I don't know why the combination of thinking machines lab and tinker, it just doesn't roll off the ton, right?

Starting point is 00:08:13 But if you want to go try it out, so they do have like a dev council where you can go quote, unquote, chat, which is interesting that they just didn't release a chat version. Anyways, this is, I think, a really big deal. And here's one of the reasons why. Yes, benchmarks. So podcast audience is showing the artificial analysis intelligence index. But this is shaded here by our closed proprietary in our open weights models. So let me zoom way out for our very non-technical audience or if you're very new to AI.

Starting point is 00:08:51 What's the difference? What's proprietary open source, right? proprietary are models that you can't really modify them to the core. You can add custom instructions to them and, you know, change their behavior that way, but you can't really change the foundations of these proprietary models, right? Those are the clods, the GPs, the Geminize, et cetera, right? Then you have these open source or open weight models. And these are ones that for the most, most part, China has been absolutely dominating on. And they've been doing it through distillation,

Starting point is 00:09:27 which is, you know, not exactly something the American labs are happy with. But that, you know, the Chinese labs are essentially, you know, stealing or borrowing, whatever you might call it, the work of the American labs and making their own versions of these models. And then they serve those, right? So you can, if you're a big company and if you have the server racks, you can use, these models and the open source open weight models if you have the infrastructure you can download them and run them 24-7 and not really pay any additional costs if you have that capacity so that is the the allure of these open source models or as consumer hardware becomes more capable being able to run some of these locally all right so some of these models not necessarily the ones i'm showing here

Starting point is 00:10:19 on screen. But, you know, some of these open source models, if you do have a very expensive, very beefy, you know, machine, you can run a slower version of them. So that's kind of the premise and the difference between proprietary and open source models. But here's why I think it's interesting. Because inkling, where it came at and where it landed on the artificial intelligence index, a 41. So right now, your leaders are Fable 5 with a 60 and GPT56 sold. with a 59. All right. So a 41, you know, seems like, okay, that's a pretty big drop off, middle of the pack, right?

Starting point is 00:10:56 True. But when you put into context that, you know, go back about seven-ish months, the leaders of the pack at that time, well, it was GPT 5.2 with a 42. So the numbers change, all right, because the benchmarks that go into this, the artificial analysis intelligence index, those benchmarks. get updated, but it's essentially, you know, about a dozen or so different benchmarks that are always updated and rotated, uh, that tell you how good is a model compared to, you know, the most important factors. And inkling, again, only being about seven months behind the frontier

Starting point is 00:11:34 is actually pretty impressive for the first release from a company that we didn't really know what they were working on, right? We really didn't start hearing from a thinking machine's lab for their first like year that they launched, right? So they, launched, I think it was quarter one or quarter two of 2025. We didn't really hear anything for them for a year. And then we heard they're working on, you know, Tinker. And then we heard that they were making their own model for fine tuning. And then we saw this, you know, this bi-directional voice model. But we didn't actually see the inkling model until, well, less than 24 hours ago. But if you put it like that, it is about seven months behind the frontier. But it is an American

Starting point is 00:12:14 model, which is actually important. And it's multimodal. Those two things alone, I think, have the potential to reshape what's possible. Because my thought is, right, that model right there, it's not going to, you know, if your team is AI native, if you have, you know, agents running, if you have workflow set, you're not going to be able to slip, like, let me just be honest, right? You can't just, you know, click copy and paste and put inkling in there.

Starting point is 00:12:41 But for those companies that are still finding their footing or larger enterprise companies that are looking to, you know, chunk off a big piece of their workflow. to something maybe more affordable. Inkling is actually not a bad option. All right. So the real product, though, is Tinker. It is, they are trying to turn fine-tuning into a service.

Starting point is 00:13:04 So Inkling, yes, it can be downloaded, but even those compressed versions need 600 gigabytes of memory. So, yeah, you're probably not running this or using this unless you are a large enterprise organization with your own, you know, GPU server infrastructure. So Tinker, though, manages the training so companies can customize the models without actually owning the GPUs. And the business model essentially turns that openness into what I think could be the first strategic reset. So let's talk about those potential strategic resets.

Starting point is 00:13:37 Reset number one, American open weights could reopen procurement. So many big enterprises haven't been able to touch some of these open source. Chinese models, well, because of the current, you know, China, U.S. relationship, right? Especially for those companies that do business with the government, right? And we've seen the U.S. government gets much more involved here recently between, you know, export controls. But also a huge thing here is that we've also seen reports in the past week that China may actually, which is, I don't know, funny or interesting, right, that China may shut down its models to other countries, right? So even though they are distilling from U.S. companies,

Starting point is 00:14:32 we've seen reports they may not let, you know, who knows how that will be set up, but they may not, quote unquote, allow overseas companies to use their open source models. So I don't know how open source that actually makes them and especially if they're just distilling them, from U.S. labs anyways, but that's beside the points. But I think for so many enterprises, they haven't been able to look at the open source category yet because of that reason, right?

Starting point is 00:14:58 If you're a company that has big government contracts, if you are using Chinese open source models, that's going to put you in a sticky situation. Or your RFP might be DOA, right? You may not even be considered if you are a company that has been using Chinese open source models. So that's a big, big unlock. All right. And I think that many companies have also just wanted to use open source

Starting point is 00:15:26 models, but the whole fact that these are Chinese models and we've seen reports on, you know, can you actually trust, right? Like what's kind of the, the messaging that may be coming out of this, that may not be in line with companies that have stronger, you know, American values or stronger Western values. I'm not going to get into that. But, you know, You know, there's obviously many different reasons, not just geopolitical reasons, that so many companies here in the U.S. haven't really been able to, you know, convince their board or convince, you know, anyone to go down the open source route just because it is all Chinese models.

Starting point is 00:16:07 Are you still running in circles trying to figure out how to actually grow your business with AI? Maybe your company has been tinkering with large language models for a year or more, but can't really get traction to find ROI on Gen. AI. Hey, this is Jordan Wilson, host of this very. podcast. Companies like Adobe, Microsoft, and Nvidia have partnered with us because they trust our expertise in educating the masses around generative AI to get ahead. And some of the most innovative companies in the country hire us to help with their AI strategy and to train hundreds of their employees on how to use Gen AI. So whether you're looking for chat GPT training for

Starting point is 00:16:44 thousands or just need help building your front end AI strategy, you can partner with us too, just like some of the biggest companies in the world do. Go to your everyday AI.com slash partner to get in contact with our team or you can just click on the partner section of our website. We'll help you stop running in those AI circles and help get your team ahead and build a straight path to ROI on GenAI. So here's potential reset too. The model overhang makes this customization very timely. So I've talked about this a lot over the past like three months. But I think we're now at this point where there's a model overhang.

Starting point is 00:17:28 It's a little bit different than the capabilities gap that I talked about. There's the great anthropic study from, it seems like it was from so long ago, but it was only from a couple months ago. Their labor index report, you know, that essentially showed the capabilities of these models and then what companies were actually using them for, right? They anonymized, I think it was 400,000 agentic chats and they mapped it all. out. And essentially what they said is these models are so capable, but, you know, maybe, you know, enterprises are using, you know, on average, about 10 to 20 percent of the model capabilities.

Starting point is 00:18:04 So essentially, these models right now, the frontier models, are way more powerful than most than the average company actually needs or even has the actual capabilities to take advantage of. So that kind of adoption gap makes fine-tuning middle-of-the-pack models. for stable, repeated work, maybe an actual new and intriguing area of AI. You know, couple that with the fact that we have seen this whiplash, the token maxing to, you know, value maxing or token efficiency whiplash, where, you know, earlier in, you know, from December 2025 to, I would say March 26, you know, enterprise companies were like, yes, you know, we've been all in on AI.

Starting point is 00:18:50 So go use as many tokens as you can, right? And then it's like, wait, these token costs are getting higher and higher. And, you know, certain model providers aren't token efficient, right? The data says that is anthropic, right? So all of a sudden, these companies have these huge API bills. So now we've seen, you know, in the May, June, July, this whiplash of companies being like, wait, we have to start raining spend in. And part of it is, well, they're realizing that we don't need, you know, a Fable Five

Starting point is 00:19:22 tight model for, you know, a hundred employees rewriting their emails. And that's what I think these middle of the pack bespoke customized models could actually be a big thing. And why is that? Well, fine tuning could return because now we actually have frontier models that are smart enough to teach, right, which we haven't really had before. Actually have, you know, a couple exciting examples that I'm going to go over here. But, you know, this, the concept. to fine tuning, right? So this is the teacher student model. This is essentially, right? And we've also got reports. If you read our newsletter every single day, we've gotten now some sniffs of real like, okay, we're at this point of recursive self-improvement where, you know, models are actually

Starting point is 00:20:07 according to research, right? There's actually a very interesting paper from earlier this week that, yeah, the models are actually improving themselves, right? But that also lends itself to the big, big models. I think maybe GPD-5-6 sole is the first, one I've seen in mass that we've seen actual real examples from that can teach and train smaller, much, much, much, much smaller versions of themselves to be used for many different purposes, right? Because these frontier models can generate data evaluations, code, failure analysis. So I think it is not just, you know, tinker, this fine tuning as a service from thinking machines and their new model is the combination of that plus this new tier

Starting point is 00:20:55 of frontier models that make fine tuning a reality, right? Because it has to become common language, right? And I think we're going to see that. It's like I could right now, and I have an example that I think is a really awesome one, but I think I forgot to include the photo of it in my slide. But you can, if you have a decent enough machine, even a local one, right, like a Mac Studio. You can start building very, very small models yourself with hardly any idea of what the heck you're doing for very small purposes.

Starting point is 00:21:31 All right. So three of these more recent examples of this concept of, you know, well, in this case, GPD 56 soul being in the fine-tuned models. So OpenAI's Jason Luai said that GPD 56 Seoul actually helped post. most trained OpenAI's small model, GPD 56, Luna, completing the work estimated to require two researchers roughly two weeks. This is the one I forgot to put the screenshot in on my slides here, but Pietro Scherano, hopefully I got that name right. So he's a CEO of an AI company, but he said just a fun little project that he worked on. He used GPD 56 to build a small local model from his iMessage history so essentially i think he had it you know work overnight or

Starting point is 00:22:19 something like that and it literally read every single message in his mac history uh you know so on your mac you can access your i message if you're you know one of our green bubble friends you're like what does that mean right so on on my computer i have my text messages so he just let it go it read everything it understood everything ran some models some evaluations and all of a sudden he had a literal uh small language model that GBT56 made for him train on that. So this isn't like a custom GPT, right? It's like, oh, I'm using the big model to give it instructions. No, it is a separate model itself, right? Absolutely crazy. Then Nvidia used codex. They just put a blog post out about this, I think, on Tuesday. Invidia just used codex and GVD56 to post-train its Cosmos 3 nanomodel, reportedly improving

Starting point is 00:23:12 accuracy in it from 54% to 93% in one day using two prompts. All right. I'll make sure to reshare that in today's newsletter. I think we did put it in Tuesdays or yesterdays. But since I'm mentioning it on the show, I'll make sure to put it in there. But here's the reality. Two prompts. These are off the shelf creating or improving existing models with today's

Starting point is 00:23:38 frontier technology. So it's not just inkling, right? It is more of a reflection of the combination of these very powerful frontier models that can literally build, run the evaluations, run the testing, run the QA, you can literally build large small language models on their own and also train, you know, other variations of themselves. So one example, though, that we got from thinking machines. They did share recently one use case about Bridgewater, right? So using their tinker service, right? So this fine tuning as a service, bridgewater, customized Quinn, which is a Chinese open source model. So this is, you know, obviously before

Starting point is 00:24:17 inkling was available or at least before they were ready to use it and talk about it. They did come out with this customer use case of Bridgewater customizing the Chinese open source quen model through Thinking Machines Tinker platform for recurring financial judgment tasks. So it reportedly beat the best tested frontier model while costing 13.0.30 eight times less. So in this case, specialized judgment beat the frontier defaults, and that kind of explains

Starting point is 00:24:50 this potential model shopping shift. So let me just put it out here, y'all. Not wanting to be like, hey, I told you guys this a long time ago. Maybe I was just a little too early or a little too weird. But I think two years ago in my AI prediction series, right,

Starting point is 00:25:10 I talked about this very thing. And the rise of potentially seeing thousands of small language models created by the large language models. And although we're still maybe not there because, you know, there is this whole thing called, you know, compute is scarce, right? Where necessarily you can't have Anthropic and Open AI and Google and Microsoft and meta and GROC. They can't necessarily, you know, pull gigawatts of compute to create thousands of these bespoke middle tier and small language models. But that's still the reality. And I think that this here, the combination of a GPD 56 soul being able to create small language models and post train its own versions and the combination of thinking machines coming up with this as a service, I think there's finally my, I forgot if it was. late 2023 or late 2024 prediction could be coming true where I think we are eventually and very

Starting point is 00:26:15 soon going to see large language models create hundreds or thousands of versions of small language models because they're cheaper. And there's finally the appetite to care about it because nine months ago people didn't necessarily care because we were still, right, we were still on this, you know, this richy rich blank check AI, right? For $20 a month, you couldn't most 90% of employees, right? If you had a team or a business plan paying $20 to $50 a month per seat, most employees couldn't get through that. So companies didn't necessarily care about being efficient with their AI.

Starting point is 00:26:55 The fact, right, the concept of a small language model or a medium-sized model, fine-tuned for specific tasks didn't necessarily. necessarily matter because the big model could still do it. And everyone, essentially, we were playing with monopoly money until about four months ago. So the whole concept of small language models or fine-tuned models literally didn't matter because it was free money. Now, because of the whiplash, this is more important than ever. So as we wrap, here's what I want to talk about. Reset three, right? Model shopping is now big. And this is now, I think, part of the executive playbook. And we saw Microsoft, right? Microsoft is reportedly going to start moving away from OpenAI and

Starting point is 00:27:42 Anthropic models, not moving away, but they're going to start mixing in their own models as well as reportedly deep seek models. So even the biggest enterprise customers are starting to realize that for a majority of day-to-day tasks, you might not need the number one model in the world to do a big chunk of the work. And I think now it's more of this concept of maybe renting Frontier. intelligence for those, you know, for the ambiguous and risky and changing work. But I think the future, which I've talked about, is using the right model for the right purpose at the right time. And eventually that will become automatic, right?

Starting point is 00:28:22 I've actually built some things like that. I'm like, I wonder why no one else is doing this because it's not like, especially since GPD 56 came out. Like I built skills that essentially model routes, right? I can use Fable inside of Codex. I can use GPD 56 inside of Claude Desktop, right? If you have a little bit of skill and enough patience, you can do those things, right? And I've done the same things where not that I ever hit my usage, but just to practice, right, where I have, I've built in model routing within codex or, you know, chat GPT work.

Starting point is 00:28:55 So if you are a cost conscious, you know, company, this is the future of working with multiple models. and you're not just going to throw, you know, every single request at one big model. So here's the new AI playbook. You own the workflow, but you're probably just going to rent the frontier or just exclusively use the frontier for those specific purposes. So you have to score every workflow by the stakes, the volume, the privacy, and how much proprietary judgment actually differentiates it. So you have to start to default to more of this economical model, right? Like you have to start mapping out that. workflow and saying, okay, maybe we send the 20% to, you know, especially if you're using

Starting point is 00:29:38 if you have the API to that frontier model, right? But I mean, my gosh, talk about middle, middle and lower tier models. I mean, opening eyes, Tara and Luna, when it comes to a cost efficiency are legit off the charts, right? So if I'm advising companies, you know, I'm saying, like, you should be using these models for the most part, right? The, the, the, the, a model on like max setting is probably more than enough for almost 90% of the work that you would do. So it is kind of shifting away from this one model for all purposes to understanding, which I know is is confusing, because it is easy to hit that easy button. But we can't just hit that easy button every single time because that button is going to start to cost more and more money as it requires more and more power. Right. So you have to default to those sometimes more economic.

Starting point is 00:30:32 models, route by difficulty, and then fine-tuned, potentially, stable, repeated, measurable work. And you have to stop asking which model is the smartest and start asking which model is enough for each job. All right, that's a wrap. I hope this one was helpful, the pretty exciting release, maybe not just for the model itself, but more for what it represents. So I hope this was helpful. If so, please let me know about it. Go sign up for our free daily newsletter. line when you get that automated, you know, email, welcome email, but also if you could, do me a favor, go subscribe to the podcast on Spotify or Apple Podcasts. Appreciate you tuning in. We'll see you back tomorrow and every day for more Everyday AI. Thanks y'all.

Starting point is 00:31:20 And that's a wrap for today's edition of Everyday AI. Thanks for joining us. If you enjoyed this episode, please subscribe and leave us a rating. It helps keep us going. For a little more AI magic, Visit your everyday AI.com and sign up to our daily newsletter so you don't get left behind. Go break some barriers and we'll see you next time.

Everyday AI Podcast – An AI and ChatGPT Podcast - Ep 820: The Most Important AI Model You’ll Probably Never Use That Just Dropped

There aren't comments yet for this episode. Click on any sentence in the transcript to leave a comment.