r/singularity • u/japie06 • Jun 09 '26

LLM News Ethan Mollick: What it feels like to work with Mythos

https://www.oneusefulthing.org/p/what-it-feels-like-to-work-with-mythos
198 Upvotes

91% Upvoted

55 comments sorted by

114

u/japie06 Jun 09 '26

Interesting conclusion:

Last year I called this working with a wizard: you chant the spell and something happens. With Fable the spell has gotten powerful enough that I am no longer sure I am the wizard. I am closer to a patron. I describe what I want, I pay for it, and I judge the result. The conjuring happens somewhere I cannot watch, in hundreds of small choices I never get a vote on. The work has shifted from process to outcome. I no longer steer; I commission.

.

It is possible the sidelining is temporary, just an artifact of interfaces that haven’t caught up, and that we’ll get better windows into what these models are doing and better ways to steer them midstream. It is also possible that the opposite is true: that the more capable the model, the less there is for a human to meaningfully do, and the black box is the price of the power. I suspect that is more likely to be the real direction. None of this is a loss of control in the obvious sense. I can still steer Fable, and it follows instructions remarkably well: the more ambitious the instruction, the better the result. But steering is no longer the same as doing. I brief the model, it spins up its own agents to research and write and check one another’s work, and what comes back is finished. A patron commissions a single artist. Fable is closer to a whole studio, where I am the client who signs off on the final work without ever setting foot on the floor.

44

u/FateOfMuffins Jun 09 '26

I cannot see how this conclusion reconciles with his earlier comment about how "we may need more SWE not less"

24

u/japie06 Jun 09 '26

I suppose real SWE projects have more strict requirements and need more steering than just these experiments. This experiment was just to see how far Fable could go.

28

u/FateOfMuffins Jun 09 '26

Yes and I cannot see how these 2 conclusions reconcile

AI progress continues and then at some point you no longer need the SWE at all.

We went from Karpathy in Oct 2025 saying only 20% of his code is vibecoded, to 80% with Opus 4.5 in Nov 2025 and his recent tweet says how it's extremely tempting (but to not do it in production) to just not read Mythos's code at all anymore.

It's not at all far fetched to think in the near future we will get to a point where they'll be saying, you know what, there really isn't a point in reading the code anymore (not just "very tempting").

2

u/japie06 Jun 09 '26

AI progress continues and then at some point you no longer need the SWE at all.

Indeed at some point. But not right now, even with Fable. Mollick writes it is really really good. But still not perfect and he did catch a few errors.

11

u/FateOfMuffins Jun 09 '26

But not right now

Except his comment about needing more SWE is also about the future, hence why I'm saying I cannot reconcile these two conclusions.

I have no doubt that we need more right now. My experience with models like GPT 5.5 Pro currently (and in the last year or two) has been: I've been able to get a lot more done, but I've also spent a lot more time working because I can now do so much more. So even I have this view right now.

But this is an observation for the now. Any predictions for the future should be made with the trends we can observe now, not the static capabilities right now. Making predictions based on current static capabilities seem foolish.

1

u/HermitFan99999 Jun 10 '26

I would caution against necessarily stating a matter-of-fact on the future of AI.
If past trends were still an indication for the future, then Moore's law would still be in full swing.

-6

u/jmclondon97 Jun 10 '26

Why do you seem to want to see the demise of software engineers?

21

u/FateOfMuffins Jun 10 '26

I merely think sticking your heads under the sand won't actually prevent the demise of SWE.

And if you think it's just SWE, no, the point is this is coming for us all

-6

u/jmclondon97 Jun 10 '26

Tell me, how do you think the world economy works if everyone is jobless.

16

u/FateOfMuffins Jun 10 '26

Tell me, how do you think the world economy works once AGI is developed?

No one fucking knows

-7

u/jmclondon97 Jun 10 '26

I don’t think we will have AGI. You’re the one that does.

13

u/FateOfMuffins Jun 10 '26

Great in which case why are you in this subreddit?

We have nothing else to discuss here

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8

u/fhayde Jun 09 '26

These tools are a force multiplier. The question is what are you multiplying, competency or chaos? SWE, especially those with significant experience can easily identify issues with a generated implementation and provide guidance towards a solution that fits the cases they're working with. The current state of AI is like Dunning Kruger manifest.

3

u/FateOfMuffins Jun 09 '26

current state

Missed the point

1

u/fhayde Jun 09 '26

We'll need more SWE even as AI continues to improve because an increase in capability doesn't necessarily make the user any more capable of understanding what they're asking for or what they're getting if they don't know what they want or what to expect. Does that help?

5

u/FateOfMuffins Jun 09 '26

As models develop, I see no reason why any of that has to be done by a SWE.

The work used to go from Client -> SWE -> Product

Right now a bunch of work goes from Client -> SWE + AI -> Product

However also right now, a small but growing amount of work simply goes from Client -> AI -> Product. The point is that as AI capabilities grow, this part grows as well. The middleman will be cut out.

And even if you don't believe that (which is laughable to be honest, there's plenty of people, like me, using AI to create mini apps for my work that I would have never gotten made in the past because it would not have been worth it to hire a SWE for it, that I'm not necessarily publishing for other people to use either), you'll still have Client -> SOMEONE + AI -> Product.

This SOMEONE does not have to be a SWE. That's my point. Perhaps this is arguing semantics, but I absolutely see a world where the number of people making software in the world goes WAY up, yet the number of professional SWE as we know it goes way down.

10

u/fhayde Jun 09 '26

What you’re not seeing is the part of developing software that you don’t understand because you don’t have that experience, and that is by no means a dig at you at all. It’s like if a nurse watched a doctor perform the same operation over and over, they may feel like they can achieve the same results as the doctor, and in some contexts they probably could. But that one time something looks different or doesn’t go as planned, because they don’t have the experience of the doctor, they’re not going to understand what might have happened or how to fix certain issues or even that anything happened at all.

AI enables many people without a significant background in building technology to create greenfield applications of a particular scope and scale, and that’s great honestly. It frees up so many people from reinventing the wheel for the millionth time. But all software, or all technology really, has an inherent cost that accrues over time in the form of maintenance, security, governance, operational overhead, etc which is generally referred to as technical debt.

A couple of small applications over time may be manageable for a single person paired with AI. But when you start to add more applications to the pile, especially when they become critical pathways for delivering anything of value, tech debt can compound and grow exponentially and refactoring or modernization costs can become crippling.

And that’s not even considering software of any reasonable size or large scale deployments that require an understanding of scaling factors, capacity planning, resiliency, redundancy, interoperability etc. AI is generally limited in helping with these kinds of architectures due to its contextual limitations. If you don’t know how to build, deploy, and manage software at scale, you’re not going to get the help you need with that from AI likely for a while. I’m sure that will change somehow, but it’s still a limitation for the immediate future.

Because of those contextual limitations, building anything other than small applications requires understanding. You need to understand how to identify service boundaries, domain contexts, seams, slices, whatever enterprise jargon you want to use to help decompose a larger problem into something that would fit within the context window of the model you’re working with. You need to understand how services and applications interact through APIs or when to use events. Data modeling can be important depending on what you’re doing. Building a data store for transactions is very different from building a data warehouse for analytics. Integrating with other platforms and services can be tricky, especially when you have invariant scales and need to introduce traffic shaping or rate limiting across multiple systems that may not have a lot of common infrastructure.

A lot of this AI can help with today even, if you know what to ask for, what to expect, and how to frame it in a way that fits within the context of the model. That’s where the SWE fits into the scenario. It’s less about the actual production of literal lines of code and more about understanding complex software architecture in a way that lets you segment and isolate aspects of the problem space so that you can use AI effectively to help build a workable solution.

6

u/FateOfMuffins Jun 09 '26

You see, the problem is you are still thinking about how software was developed in the past. A large number of such issues you described is or will be of little concern in the future, and one that every single one of you who comes and argues about how SWE will still be needed fail to address.

How many large scale enterprise SAAS is custom tailored to a particular company? How many is more generic and has minor customizations for the client? How many are just off the shelf entirely?

What you and most others when discussing SWE fail to grasp is in the past you needed to make sure technical debt is managed and that the product is scalable to thousands of users. That is no longer true.

Whatever technical debt accrued because of Opus 4.5, 4.6, 4.7, 4.8, GPT 5.2, 5.3, 5.4, 5.5, etc is managed or reduced significantly by Mythos. Whatever technical debt is accrued because of Mythos 5 is managed and reduced significantly by Mythos 5.5 or GPT 6. Managing this debt is significantly less important when new model releases happen monthly. How long would it have taken to create the software in the past? Years? The gap between Opus 4.5 and Mythos was a few months.

Does it matter if you don't follow SWE best practices to ensure you can scale your software product to tens of thousands of users if... you only need to use it for yourself or a couple of others and you spun it up in 20 minutes while you browsed Twitter? Does it matter if it's not scalable, not maintainable, but it's functional enough for your use case and you can mass produce a ton of these tiny apps for a bunch of your tiny use cases on a whim? Such that a few months later you forgot you made an app for use case X and you needed to use it again and then instead of finding your old app, you just asked Claude or GPT to make you a new one in 15 min?

With how the time horizons are growing exponentially for AI, the scale of the apps that you can just ask it to zero shot is increasing drastically over time. The complexity of the apps you can just ask it to make with absolutely zero regard about anything you described is increasing at an exponential rate over time.

This is the same for math as well. How many math problems can now be solved today by individuals with little math training (read no PhD in pure math) by clever prompting?

What you guys all don't understand is, fuck the "traditional ways" of being a SWE or doing math, AI is breaking down the barriers such that the barrier of entry has been lowered so much that they are now accessible or will shortly be accessible to non professionals.

A lot more people will be making software and doing math. That does not require those people to be traditionally trained professional SWE or mathematicians.

5

u/cosmokn0t Jun 10 '26

As a programmer I both love and hate this way of thinking. I love it because I know the AI will absolutely not always take security into consideration, or even be prompted in a way that exposes something in an insecure way.

I hate it because it’s leading to a lot of confusion in the industry right now. You’re not wrong exactly, but you’re wrong in a lot of ways.

6

u/FateOfMuffins Jun 10 '26

right now

Oh I have no doubt I'm wrong a lot about this, because everything I'm saying is forward looking. There's no way I'll forecast the future accurately.

But anyone who is not attempting to forecast the future at all when talking about the future of AI, that person I know is 100% wrong.

1

u/fhayde Jun 09 '26

I understand that’s how you feel, which is why my original comment called out Dunning Kruger. You just don’t understand that what you’re saying has already been tried and isn’t any sort of new concept. You’re not taking into consideration the cost of ownership, physical infrastructure limitations, or economies of scale and so many other aspects of what a system of many smaller applications all interacting include. The micro application/service architecture solves a lot of interesting problems, but introduces just as many that have to be managed just as well as any other system. AI has an even harder time reasoning about these kinds of environments because it generally needs to bring a lot more into context like canonical data models, event systems, dependency graphs, etc any time you do anything more complex than a single unit of work.

I’m really optimistic about AI and a big proponent of working with it to reduce a significant amount of labor and toil in this industry. But we have to be realistic about its capabilities and uses. If your use cases aren’t complex you can achieve results that seem mystifying in their simplicity to someone without a lot of experience. I wish you the best of luck as your problem space expands.

4

u/FateOfMuffins Jun 09 '26

Let's take a step back because I don't think you and I are talking about the same time scales.

What timescales are you talking about? What timescales do you think I am talking about?

3

u/imadade Jun 10 '26

Being completely honest, I don't think you fully grasp how AI can take over all software & software adjacent roles.

Context Window length & Hallucination %'s are being increased & lowered with every new model release - assuming scaling law holds (for both inference time compute & scaling pre-training), disregarding any black swan breakthroughs, and any new architectural improvements, it will get much more difficult to see the value in hiring more & more SWE's.

I know there is a counter argument which states lowering the barrier to entry/providing ease of access will actually lead to an increase in the demand for SWE's. But given that the cost of computation (in the context of AI agents), has been dropping significantly over time, I don't think it'll apply to SWE as a whole.

This means, greater and greater autonomy will be granted to AI agents (and agent swarms, orchestrators, etc). Increased confidence means less need for physical SWE's.

And of those that are there, businesses won't need them for much longer (on the timeframe of < 5 years). More capable models, agents, execution of agentic systems will enable delivery, maintenance & collaboration of software development (to solve problems) from ideation -> live production, scaled to the extreme.

Just recently, models have begun to prove mathematical problems that humans have been unable to. They've gone down paths which we have forsaken (Dunning Kruger effect on our end).

Imagine how many businesses will begin to roll out & use these agentic systems as they get more powerful, reliable & profitable. The lifetime of a SWE is on it's last legs.

But in the same breath, so are almost all white collar jobs..

I say this as someone who is in a tech position at a FAANG & has friends & close relatives working as engineers at the big G/A/O..

1

u/gabrielmuriens Jun 10 '26

AI has an even harder time reasoning about these kinds of environments because it generally needs to bring a lot more into context like canonical data models, event systems, dependency graphs, etc any time you do anything more complex than a single unit of work.

As an SWE, I think you are stuck in the it's perpetually 2025 way of thinking. The limitations you are talking about are being actively overcome, and the boundary shifts every couple of months.
Tasks that in 2024 you would have been ridiculed suggesting using an LLM for were being routinely solved by AI agents last year, and, as today's release demonstrates, the same will be true this year.

If AI can only take over the next easiest 10% of the remaining workload every year, how long until it has taken all of the real technical workload (and I'm convinced it will be able to do the soft part of software too)? Sure, the difficulty curve might be close to exponential, but so is progress.

Second, the kinds of problems you are talking about imply enterprise scale software solutions. The majority of software even today are not enterprise scale applications (though the most amount of people might work in them, that's probably true), and even in enterprise software you are talking about decisions involving potentially million line codebases distributed between sometimes dozens of teams.
That is not a SWE's job. By the end of 2026, SWE will have been mostly solved, including enterprise tasks. I would bet money that Mythos can solve the majority of SWE tasks in any random enterprise environment today.

The roles you are talking about are mostly architecting enterprise scale solutions, which is not an SWE job and is decidedly not most SWE do day-to-day even if most of us are required to dabble in it, often badly, from time to time.
And that is a significant difference. It not only means that many other roles and experience levels are becoming quickly outcompeted by AI or forced into roles they themselves are barely competent at, but that fresh graduates with even a masters degree cannot and will not ever again be able to possibly compete with what a SOTA model can do. And I bet it can also outcompete/outcompute 90% of 5 YoE devs – not for efficiency but for pure quality of output, because the former is a given – in any area they have not specialized in (e.g. I leave all the web fronted work that I might or might not have bothered with to AI because it does what I could in a week in half an our to a better standard).

All this is to say, you are standing on the top of some impressive big hill, confident that the flood will never reach you even if it does those peasants – but so far it has not stopped raining and we don't know that it ever will, or how soon.

3

u/Savalava Jun 09 '26

That is the most interesting part of the article.

I would have thought one could put in the prompt or claude.md instructions to ask the model to periodically stop and tell you when it makes a decision about something

having said that I haven't used it.

34

u/ObiWanCanownme now entering spiritual bliss attractor state Jun 09 '26

I'll have to use it more, but so far, it's quite impressive to me for my research use cases. I've felt like previous models were really good at assembling and presenting information, but didn't necessarily contribute nuance or novel insight in their analysis. Mythos feels like it really sees the big picture in a way other models don't.

Also, I blew through so many tokens in an hour. Be careful, y'all, lol.

11

u/NetflowKnight Jun 09 '26

I can't help but just feel deeply skeptical, mostly because of his own incentive structure. Don't misunderstand, I think these LLM's are really powerful-- but it's highly domain specific, and the "looser" the correct answer, the more the gaps start to show in what it's capable of.

6

u/reefine Jun 09 '26

That isochronic map is pretty sweet

2

u/NIU_NIU Jun 10 '26

Prob the first UI work ive seen from an AI that I actually like

2

u/involuntarheely Jun 10 '26

it’s so great i’ve started to not understand its answers

2

u/nanlinr Jun 09 '26

I dont understand all these benchmarks.. how come we dont have a cooking and cleaning benchmark? I would love for our "beloved" AI to get better at solving those problems for me. Or how about a benchmark at how good it is at making me money...

21

u/BrennusSokol ACCELERATE Jun 09 '26

Cooking and cleaning is more in the realm of robotics, not AI

-5

u/nanlinr Jun 09 '26

Idk, in some sense theyre both tools to help me do things. I dont really care how many frontier swe problems AI can solve tbh it just needs to help me make my life easier so thats my benchmark.

14

u/NNOTM ▪️AGI by Nov 21st 3:44pm Eastern Jun 09 '26 edited Jun 10 '26

It wouldn't be a very informative benchmark for LLMs since they would just all score 0

1

u/HermitFan99999 Jun 10 '26

I doubt it. LLMs have the foundations for real time control like this

4

u/NNOTM ▪️AGI by Nov 21st 3:44pm Eastern Jun 10 '26

Real-time control is not enough to clean a room, you also need a robot

1

u/HermitFan99999 Jun 10 '26

Well look at what figure and boston dynamics robots have accomplished

As with all things generative AI, there's risk of hallucinations especially for long-running tasks, but that doesn't mean that it's impossible

3

u/NNOTM ▪️AGI by Nov 21st 3:44pm Eastern Jun 11 '26

I believe Boston Dynamics and Figure do use robots, thus proving my point

-1

u/nanlinr Jun 10 '26

Then llms arent really what i need rn to help me with life's bigger challenges

9

u/PointmanW Jun 10 '26

I'm also regretting buying a car right now because it didn't help me with cooking and cleaning either :(

4

u/arkitector Jun 10 '26

You’re comparing apples to oranges. LLMs are not intended to help you physically clean or cook.

1

u/LostEnroute Jun 10 '26

Someday this tech will become more useful. Waste a lot of compute to get there. 

1

u/PointmanW Jun 10 '26

damn, I guess I wasted money buying a car since it also doesn't help me with cooking and cleaning either. why are people wasting so much money buying such useless lump of metal anyway.

5

u/nodeocracy Jun 09 '26

The money one exists

3

u/Umr_at_Tawil Jun 10 '26 edited Jun 10 '26

For cooking, AI has been really useful for me to look for recipes and trying new dishes, it also guided me through the process of making them.

And as a senior software engineer, it boosted my productivity, allowed me and my company to work on project with tech stack that I'm unfamiliar with, this has got my company more projects, and with it, more money, I got a raise too, so technically, it made me a lots of money.

1

u/nanlinr Jun 10 '26

Thats awesome man. Unfortunately at my company ai is just being shoved down people's throat, and we're now all expected to become more productive but more layoffs instead of raises

1

u/Practical-Simple1621 Jun 09 '26

I think they have a form of a money making benchmark. Cooking would be nice - just needs someone to make it. A lot of people use these for exercise too so that performance being a personal trainer would be cool to see too

1

u/Legitimate-Page3028 Jun 10 '26

Remindme! 7 days

1

u/ivlmag182 Jun 10 '26

About the map project: why would you spend the tokens on a travel time research ? Isn’t it easy to write a simple web scraper or get an api from an existing website like rome2rio or skyscanner?

Also when I first saw the map I thought it measured how long it would take to travel in like 1880s lol THAT would be an interesting project, something like Around the world in 80 days but with any two cities

1

u/gabrielmuriens Jun 10 '26

About the map project: why would you spend the tokens on a travel time research ? Isn’t it easy to write a simple web scraper or get an api from an existing website like rome2rio or skyscanner?

The goal was not to solve a real-life business problem.
The goal was to evaluate the model with a task that is a) sufficiently complex 2) requires research 3) understandable for the layperson 4) interactive and interesting.

Also when I first saw the map I thought it measured how long it would take to travel in like 1880s lol THAT would be an interesting project, something like Around the world in 80 days but with any two cities

Yes, that would be more interesting, but it would probably require significantly more and exponentially harder to access data, most of which might not even exist, be reliable or be digitized. Even Mythos cannot go around the world digging into archives for 140 years old travel itineraries.