# AI Costs Are Surging and the Cheap Model Fix Might Not Last — Transcript (2026-07-08)

https://aidailybrief.ai/e/2026-07-08 · Listen: https://pod.link/1680633614

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[00:00:00] today on the AI today on the AI Daily Brief, how does AI change If access to open weight models starts to get cut off Before that in the headlines. h-- all the new models you have access to right now and all the ones that are coming. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI All right, friends, quick announcements before we dive in. First of all, thank you to today's sponsors, KPMG, Blitzy, Airtable, and Retool. To get an ad-free version of the show, go to patreon.com/aidailybrief. And of course, if you wanna learn more about sponsoring the show, send us a note at sponsors@aidailybrief.ai. 

Welcome back to And my friends, if you thought that this was going to be a slow summer, think again We We are just absolutely drowning in model announcements or announcements of announcements in some cases today.

So let's get into everything that is here and everything that is coming



Now, the first one is not a surprise, as this was announced during the period that Fable was offline. But But the GPT [00:01:00] 5.6 family of models, including Sol, Terra, and Luna, OpenAI announced in the middle of the night for some reason, that they would be officially coming on Thursday



and in addition to that announcement of the announcement they they also unlocked early testers

to begin sharing their impressions. were going to go much deeper into this when the model actually comes out, but a lot of those first impressions are pretty positive Ali K. Miller calls the model an execution beast

" So much," she says, "that I think 5.6 is the absolute wrong name considering how big of a leap this felt to me."

Her conclusion: " "When When Sonnet 3.7 came out, I think we no longer tolerated bad writing. Having GPT 5.6 and Fable 5 out in the world, I think we will no longer tolerate bad execution or slow bug fixes or unhelpful customer support, or at least tolerate a whole lot less."



Magic Magic Path CEO Pietro Schirano wrote, " I can finally talk about 5.6. I've been testing it for months, and without exaggeration, it's the best model I've ever used. fast, smart, genuinely creative, and you guessed it, they they finally fixed front-end design. [00:02:00] I haven't needed to check the code I've written in two months

and AI entrepreneur Theo wrote It's a damn good model. Not quite as, quote-unquote, "smart" as Fable, but it's incredibly capable. Fixed all the problems I had with GPT 5.5. It's incredibly determined, will run for a day without even using a slash goal. It understands sub-agents incredibly well and is great at orchestrating.

It's super pleasant in use cases like OpenClaw and Hermes Agent. It knows iOS dev incredibly well. It has rough edges too, but far fewer than 5.5 did. For many things, GPT 5.6 Sol will become my obvious default



Now Now of course, the question that many will have is how does it compare to Fable?

And not everyone was convinced that five six beats it Not Schumer writes 5.6 5.6 Sol is an amazing model, but for almost every task I tested, Fable was quite a bit better and more agentic to boot. I.e., one Fable turn does the same things many 5.6 turns do Interestingly, however, according to Ethan Mollick That mode of interaction



where Fable goes off and does more things on its own

5.6 Sol sticks closer to the user might [00:03:00] be more intentional than it at first seems

Ethan wrote, " Sol is of similar ability but quite different feel than Fable. Fable wants to go off and do work on its own pace. Sol is faster but works with you in steps more Now for Ethan, this wasn't an either/or. He continued, " " I found myself switching between Fable and Sol depending on task.

Sol for back-and-forth tasks, especially when I had not yet figured out what I needed exactly. Fable for very long tasks where I could define what I wanted, and Sol Pro for really hard problems."

Still for some the biggest and most interesting hint from this commentary

was around just how long some folks said that they had been testing this. Remember, Pietro Schirano wrote, "I've been testing it for months." Chubby Kimonismus writes, " "Wait, he had Wait, he had already been testing 5.6 for months? That means 5.6 had already finished training when Mythos and Fable 5 had their reveal."

And of course, the implication is that these are not the most state-of-the-art models that these labs have access to



now, while any new state-of-the-art model captures more attention than anything else It is increasingly the case that people are thinking [00:04:00] not just about raw model performance, but also model efficiency

and you can feel increasingly people getting excited not just about the frontier model releases



but models which offer something discrete and specific as part of an overall robust and complex model architecture. And that is potentially where SpaceX and Cursor's new model comes in

Yesterday Yesterday afternoon, The Information reported that the model release was imminent and could be coming as soon as Wednesday. The memo stated the release was pushed back from earlier this week to allow for efficiency tweaks



now now when it comes to this particular model, we have had a few breadcrumbs over recent months. Last month, for example, Cursor CEO Michael Truel announced that they had finished pre-training their first model from scratch using SpaceX AI infrastructure. he said the model had 

one point five trillion parameters and also hinted that the model would be intelligent beyond coding, suggesting that this could end up a more general purpose model as opposed to the Composer series, which has been very specifically designed for coding tasks Musk has also hinted at multiple large training runs taking place at Colossus And a little over a week ago said that Grok 4.5 had entered private [00:05:00] beta at SpaceX Tesla, and Tesla late last month



Grok 4.5, he said, is based on what he called their 1.5 trillion parameter V9 foundation models with cursor data added in post-training

At the end of June, Musk wrote, " "Early Early evals show performance close to perhaps exceeding Opus."

And that was reinforced when late last night, Musk confirmed the rumors and said that yes indeed, Grok 4.5 would be coming today. In fact, by the time that you are listening to this, it is highly likely that Grok 4.5 is out

Elon tweeted, " "Based Based on strong positive feedback from customers in our beta test program, SpaceX AI will make Grok 4.5 available to the public tomorrow. It is an open class model," he wrote, "but faster, more token efficient, and lower cost."

Now I want Now I want you to hold in mind that token efficiency and cost positioning, especially as we get to the main part of our episode in a few minutes

And by the way, you might have noticed that I keep referring to SpaceX as SpaceX AI

That's because that is the new official name of the company

The full integration of Elon's empire continues unabated Space XAI lives



now now one more small [00:06:00] note on SpaceX/SpaceX AI the post-IPO quiet period ended on Tuesday, meaning we have the first bank analyst ratings for the stock Everything that I've seen so far is pretty wildly bullish.

Morgan Stanley gave a $300 target, Bernstein at 239



And JP Morgan was also wildly bullish, expecting five thousand Starship launches, i.e. fourteen per day, by twenty thirty-one. Now Now keep in mind the IPO price was a hundred and thirty-five dollars and SpaceX AI stock is currently trading at a buck sixty Meaning that these price targets represent a significant increase.

Now, one model that you don't have to wait for, but you do get more time with, is Fable 5. Tuesday was expected to be the last day to use Fable as part of cloud subscriptions, with Anthropic switching their flagship model over to usage-based pricing after that. However, you now have until Sunday to make the most of your bundled Fable usage As they have extended access to Fable 5 on all paid plans through July 12th 

co-- that that is, of course, unless you've already maxed out your usage, which Andrew Curran thinks is all part of the plan, commenting, " With the number of people [00:07:00] distraught that they have used up all their Fable usage for the week already under the assumption that today was the last day, it's almost certain that Anthropic will announce a surprise reset.



this is how you feed a heroic aura. Set the stage and then save the day."

Now, Now, one thing that some folks have been asking me is whether the renewed Fable five

has lived up to not only the hype, but the experience we had a couple of weeks ago before it got turned off. And so far for me, it absolutely has, although I'll come back and talk in more detail about that at some episode in the near future



Certainly there are a lot of impressive things that people have done in this very short period that we've had it back

Amar Amar Reshi, for example, the product lead at Google AI Studio, showed off an iPad port of the 2003 game Command and Conquer Generals Zero Hour, claiming Fable had ported all the code across, rewriting it to run natively on the ARM64 with touchpad controls

Not to let the big labs all the fun, Business Insider reports that Perplexity has quietly cooked up a coding agent to take on Claude Code and Codex. The tool is named Teammate and has been deployed internally since May. An internal announcement viewed by Business Insider said that Teammate is designed to oversee [00:08:00] software projects from start to finish With the announcement saying, " It's built for long horizon engineering work, owning projects, investigating issues, and monitoring services."



We also have some Google rumors with some vague chatter about Gemini 4



And And even Meta has rejoined the party in the model game Specifically, Meta has launched a new image model that actually looks pretty impressive. The The model is called Muse Image, and it's the first image model released by Meta since restructuring the AI division to launch Superintelligence Labs.



The The model looks pretty close to state-of-the-art. It can handle photorealistic images as well as various stylized effects Now benchmarks are inherently a little tricky for image models, but on the image edit version of Arena AI

Image managed to rank in second place behind only GPT Image 2

CEO, Alexander Wang, gave us a look under the hood to see what Meta was doing with this model. The model is paired with Muse Spark, Meta's LLM, to apply reasoning to a prompt before producing an output. Now, Now, this approach was, of course, pioneered with NanoBanana and as we've seen, creates some fairly significant upgrades to the [00:09:00] capability set



Wang said that he was particularly impressed by three things Self-refinement, i.e., the model improves its own output within its chain of thought, which he said emerged during reinforcement learning, not by design. Second, multi-reference composition, i.e., many images blended into one coherent generation.

And third, multi-turn editing, iterating without losing coherence or starting over Wang also previewed the upcoming Muse video model, which he suggests will be competitive on prompt adherence, visual fidelity, and temporal consistency

Now, a lot of the coverage is focused on how this model is being introduced. Like their previous image model, this one will be available in the standalone Meta AI app. But it's also being dropped straight into Instagram and WhatsApp with a bunch of social features, like being able to generate an image according to what's trending

However, the feature that's causing controversy is the ability to tag someone else in a prompt and use their public photos to insert them into a generation

For most, this will just be harmless fun, but people are also worried about it being a one-click deepfake machine Users can of course opt out. but considering the current state of consumer AI sentiment, I would not be surprised to [00:10:00] see it cause some controversy in the coming weeks

Now, one interesting note from a very functional perspective is that Meta is planning an advertiser-specific version of Muse Image designed to allow brands to quickly generate product images.

There are a ton of AI startups out there who have been focused on exactly that type of use case, but given how deeply integrated advertisers are already into the Meta ecosystem, this could be one that drives business value from AI for Meta very, very quickly

Lastly today, we're also getting rumors out of China of MiniMax working on a very new LLM with two point seven trillion parameters, which is larger than any other Chinese AI model that is currently on the market According According to the information sources, the model could be released as soon as the third quarter and is known as M3 Pro internally As of now, MiniMax is planning to open source the model, but as we will see

Depending on how things proceed with the Chinese government, that might not be the way it plays out. For more For more on that, we will close the headlines and move over now to the main episode 

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Learn more at retool.com/aidaily. Welcome back to the AI Daily Brief. Today, we're Welcome back to the AI Daily Brief. Today, Today, we're doing something a little bit different



while our episode starts in a specific news report from Reuters The main substance of the episode is actually an exploration of the potential implications of how a particular type of action, which [00:14:00] I think is not at all implausible, would change the nature of the AI race and a lot of the key questions that we've been asking for the past several months



So the specific query that we're going to be exploring is how AI changes

And how the answers to all these challenges of token efficiency and token cost change if China were to decide to stop letting their company's premier models be open sourced overseas

Now, the reason we're having this conversation is a report from Reuters that Beijing is potentially exploring ways to block the overseas distribution of leading models. Reportedly, representatives from Alibaba, ByteDance, and Z.AI have attended meetings with Chinese authorities over the past month The The meetings were led by the Ministry of Commerce, itself an indication that this goes beyond the tech industry regulator and involves the much more powerful economic planning officials

Officials discussed placing limits on the distribution of the most advanced AI models being developed in China, including both open and proprietary models. [00:15:00] Sources said the measures under consideration include limits on who can invest in Chinese AI companies, all the way up to making the leaking of AI technology a criminal offense under stringent national security laws

Now, Now, no decisions have yet been made, but the reporting certainly suggests that China, just like the US, now views frontier AI technology as a national security asset, not just a consumer product

Some of the discussion centers around a Mytho-style approach where lesser models are able to be distributed, but the rollout of next generation models are under government control

And it is worth noting that officials at this point seem to be mostly thinking about this policy applying to future models, not trying to scrub already released model weights from the internet

Now, I think That most discussions of the AI China-US race, start from the position that the model of open source distribution that the Chinese companies have you so far is always going to be their strategy.



and if nothing else, this reporting calls that assumption into question

Now, some folks are fairly incredulous

CNBC's Deirdre Bosa wrote, " Anthropic shutting down access to Fable [00:16:00] and Mythos gave Chinese open source models a huge opening. unless this is about control and leverage over distribution, why would Beijing restrict access now?"

Now, a number of Chinese language accounts on Twitter



popped up to basically say that Reuters got it wrong



as evidence, they pointed to a sourced document that they were able to find



of a public dialogue in the Chinese courts about this set of issues

The process sounds a bit like the EU trialogues where in advance of big decisions being made, there's a broader discussion period



and and effectively the Chinese language X accounts that were reading those documents We're arguing that Reuters exaggerated its conclusions



It's important to note that this is not an official policy document, but rather a discussion featuring a Supreme People's Court IP judge alongside leading legal and AI scholars

They say that the 10 biggest themes that repeatedly emerged across the experts were, one, open source is no longer presumed to be pro-competition. Two, the real source of market power is the ecosystem, not the model Three, open source washing

Where companies open source enough to attract developers [00:17:00] while keeping the most valuable layers proprietary is a major concern. Four, open weights and OpenAI should be regulated differently. Five, traditional antitrust tools in China are seen as insufficient

Six, cross-border governance is increasingly recognized to be fundamentally different for AI

And finally

China wants to become a global rule maker for AI open source " Rather than simply adopting US or EU models," they wrote, China should develop its own legal framework, strengthen domestic open source infrastructure, and play a larger role in shaping international AI open source governance."

Now, I think that this is a good summary of the document that people went out and sourced. But But it seems to me that these accounts are almost willfully misreading the Reuters piece

Reuters is not just referring to the public dialogue in the court. they are explicitly focused

on meetings between Chinese authorities and companies including Alibaba, ByteDance, and Z.AI about these issues

Now, I suspect that what's happening here is that in conjunction and in the lead up to that public dialogue in the court, there have been a series of closed-door [00:18:00] meetings to take input from those companies

And I do think it's fair to caveat all of this as saying that it feels to be pretty clearly in an exploratory phase But as you can probably tell from the beginning of this episode, I don't think it's at all guaranteed that China doesn't decide to make a very different decision about their approach to open source than the norms are today



Ethan Mollick agrees, posting the article and writing, " This is a key reason I don't expect the flow frontier open weights models to continue indefinitely or even for very much longer." Sovereign AI strategies of all types are built on the assumption of continuous releases of open weight models that keep pace with the frontier, giving cost privacy control gains at the expense of only a little worse performance.



but that may no longer hold soon

Now, what I don't think is particularly useful is to try to get into the minds of the Chinese government

For the sake of this episode, let's assume that there are reasons, ones we might agree with or disagree with, why the Chinese government, who clearly likes having control over the distribution of technology in their country, would want to have more control over the distribution of the [00:19:00] technology that their country is making than releasing the open weights of models allows for

So So let's say that China does start restricting access. Well, what happens then? The entire theme that we've been exploring for the past several months is the growing realization that in a world of agentic workloads, AI costs look radically different than the SaaS-style budgets that people were planning for before

Now certainly those issues don't go away if China starts restricting access to open weight models. Because those issues don't have anything to do with China's open weight models, they have to do and OpenAI's cost of provisioning the frontier the shortages in compute, and all the infrastructure challenges that surround that

Now, so far, the first set of answers to the emerging token cost question have been fairly blunt force type of answers. they've been things like token spending caps, which we got another one from Tesla applied evenly across the entire company

or on the other hand, they're the simplified brute force approach of simply switching to a cheaper model

So what are the So what are the implications if that second option of switching to a cheaper Chinese model gets taken off the [00:20:00] table?

First of all



it obviously creates a lot more opportunity for emphasis on open weights and alternative model approaches from the US and Western labs

And already it's pretty clear that some companies are sensing this as an opportunity



NVIDIA has recently been putting more and more emphasis on its Nemotron model family. just announcing this week that it had reached a hundred million downloads.

About a month ago, the company introduced Nemo Tron 3 Ultra



really pushing its differentiation from the other Western models and even the other Chinese models around things like output speed



in this world of China blocking access to the frontier of open weights models

Google's emphasis around Gemma also gets a lot more interesting

In In fact, DeepMind's series of Gemma models are quietly pretty popular. A couple of weeks ago, at the end of June, Google announced that Gemma 4 had hit 200 million downloads in just its first two and a half months. For context, they said the total downloads across the entire Gemma family of models when Gemma 3 was launched was at 100 million

Gemma are what Google calls its lightweight state-of-the-art [00:21:00] open models and are clearly meant to serve a different part of the market than just the raw frontier



Now, we don't know how good Gemini 3.5 Pro or Gemini 4 are going to be. 

And it could be that once released, those models rocket Gemini right back, into the conversation alongside the fables and GPT 5.6s of the world.

But even if they don't Gemma represents this entirely different bet that at least at this stage, OpenAI and Anthropic aren't really making Does that become more interesting in this world where Beijing starts to restrict model access? One would think so

And And then there's Microsoft

Earlier this year



Microsoft released a series of new models that they had trained in-house Now, this announcement 

didn't get a ton of attention because I think the broad perception outside of Microsoft was just that this was them slowly working to catch up

with this set of models mostly just functioning as a way to prove that they still had some chops and especially over time should not be considered out of the game But I actually think But that misses a fairly important strategic change that Microsoft seems to be exploring

Alongside the models, [00:22:00] Microsoft introduced something that they called Microsoft Frontier Tuning

Microsoft AI CEO Mustafa Suleyman wrote, " It's time to move from renting intelligence to truly controlling your AI." Microsoft Frontier tuning lets you take our models and make them uniquely your own, turning them from capable generalists to complete custom partners

He then goes on to explain how the process works

And said that their early results have been really promising. He wrote, " "Within Within Microsoft, we use our reinforcement learning environments combined with our MAI models to climb towards the best agentic use cases for Excel. Our MAI-tuned model is on par with GPT 5.4 on public and private benchmarks, while being up to 10X more efficient."

In the overall model announcement post, he wrote, " When we tuned our models for McKinsey's tasks, MAI delivered the highest win rate, outperforming GPT 55 on quality while being 10X lower on cost."

now obviously the MAI models are not open But what Frontier Tuning represents is effectively a commercialized and productized version of what a lot of other folks are exploring with these post-training [00:23:00] approaches, but using Microsoft's models instead of these Chinese models 

that theoretically we might not always have access to



by by the way, this doesn't just seem theoretical. Bloomberg is reporting that Microsoft isactively considering using their MAI models for a number of different functions within their apps what's interesting is that previous reporting had suggested that they were going to use DeepSeek but now they're finding that their MAI models, when optimized for specific tasks like generating a chart from Excel data, are actually up to the task

in a way that would both save money as well as avoid any weird sovereignty issues with China



now now the announcement of Microsoft Frontier tuning happened on June 3rd

The Fable V banning happened a week and a half later on June 12th. I believe that if this Frontier Tuning announcement had come out after Fable V got taken offline, during that period where we were all waiting for what was next, it would've had a lot more buzz



and and by the way, Microsoft is far fromthe only company playing in this space. Back in October of last year, Thinking Machines Lab launched something called Tinker, which they labeled a flexible API for fine-tuning models

Just about a [00:24:00] week ago Thinking Machines' Mira Murati, shared a case study of Bridgewater using the Tinker API to fine-tune a model using what she called their unique financial knowledge

Thinking Machines co-founder John Schulman wrote, " People sometimes ask why fine-tune when general purpose models keep getting better? Bridgewater's work is a good reminder that with the right data, here expert judgments, you can beat prompting-only approaches by a lot."

Now in the paper, they showed that whereas models between GPT and Claude Opus 4.8 all had an average accuracy

Of between 74 and 78% for a cost of between $20 and about $90

Their model got up near 85%

at a cost of single-digit dollars

Google DeepMind's director of AGI economics, Alex Emas, wrote, " "The longer I've The longer I've spent with this paper, the bigger of a deal it seems. The economic implications are quite significant."



And we keep hearing about examples of exactly this sort of thing



Composers Cursor 2.5 is another example, built on a base of Moonshot's Kimi

and then modified and post-trained by them 

[00:25:00] to produce Opus and GPT level performance at a tiny fraction of the cost



Now in addition to China getting out of the open source game, creating new model opportunities for Western companies



these sort of changes also put a fine point

on an additional value proposition of model routers. now model routers are an increasingly popular approach where instead of just interacting with a single model, you can plug in a model router Which can theoretically figure out the right model for any particular task, leading to efficiencies and cost savings.



while especially in a period of increasing regulatory grayness, all of a sudden model routers could start to play an important governance role as well



selecting models not only on the basis of capability, but on the basis of risk



already there is so much evidence of things changing



Rauch recently discussed

the extent to which they've observed a shift from companies choosing a single AI lab to partner with, to actually building complex model architectures



Now, I think what's interesting about this

is that in many ways these trend lines are already set

The need for lower cost alternative models and better model [00:26:00] architectures to get the right tasks to those models is going to be there whether it's Chinese models plugging in or not

And And personally, I think that even the possibility or a growing recognition of the possibility

of China cutting off access to the frontier is going to create incredible market opportunities

for new model approaches from over here as well

Certainly Certainly the reality is if you are an AI buyer at an enterprise, your life is getting more, not less complicated

At the same time, I can promise you'll never be bored. These are trends we will continue to watch, but for now, that is gonna do it for today's AI Daily Brief. Appreciate you listening or watching as always, and until next time, peace 

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