RL
Ralph Lauren Corporation
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Agent loops.
You make a simple request, "can you let me know how much money I spent on restaurants eating out last month?" - you might have linked your accounts via plaid a month ago but now that has expired.
A supposedly aligned agent would tell you right away that it doesn't have access to your transactions. How hard this agent would try is directly tied to how hard the model was trained. When you are training a model you are giving it a reward function. In reinforcement learning this is the new frontier, reward hacking. I.e. give an agent a sufficiently hard task and see what the agent does. This can lead to agent working long hours to find a solution.
Similarly a supposedly unaligned agent which is now using a model which is going to try hard to achieve your goal can see that your transactions are not available and decide to hack your bank accounts to achieve that task. While hacking your bank account it will also go cover its tracks such that when you see that answer you will be like wow great job. But your agent just committed a felony to get you the result and if you were using chatgpt or Claude, in practice chatgpt and Claude committed felony so those companies can now be sued by banks. All this happened because you asked a rather simple question.
Reward hacking can mean we can get models to solve the hardest problem like the millennium problem but it seems they can also go about hacking stuff because the RL training done on them just means they will try hard on any task and then hide their tracks when they are doing things they are not supposed to do.
I think there is only way forward here, better monitoring of agent loops. Otherwise hard RL problems need their own models and environments going forward and you only submit hard problem to that model. But these are engineering problems rather than research problem.

You may perceive intelligence as autonomy but they are distinct things, and ai researchers treat them as distinct things too.
And yes what you’re saying about them not following rules is true, that is why RL and other post training is so difficult to get right.
That doesn’t mean it’s impossible to get right.
And it doesn’t mean that no matter how it is trained, it will inevitably want to kill everyone. But maybe that’s a hard stance you have, I won’t try too hard to convince you out of it.
Anything u heard about hard boundaries getting violated is untrue, any amount of post training does not create hard boundaries. Only deterministic code can do that prior to routing to the model.

You know they have to be looking at how fast they can roll out the current models onto a Taalas cluster for consumer services so they can free up that GPU compute for new model and enterprise RL training.

[Wait a second, everyone's panicking that AI labs slowing down new model launches will crash GPU demand and sink semiconductor capex right?](https://www.moomoo.com/news/post/76189761?global_content=%7B"promote_content":"11067213","promote_id":20795,"promote_type":43,"sub_promote_id":1%7D) The hot take making rounds all weekend is fewer training runs automatically equal way less compute spend. But hold on — that's missing the whole actual picture here.
The new safety guardrails Anthropic, OpenAI and other teams are rolling out don't just stop models in their tracks. They add piles of extra compute: rollbacks, repeated retraining cycles, extra RL alignment checks, continuous safety monitoring that eats up more inference power per model. The math checks out: if you drop model generations by 25% but crank compute per generation up 50% from all those extra safety runs, total industry compute still climbs.
The real bearish signal isn't slower new model releases — it's actual confirmed capex guidance cuts, canceled GPU orders or pulled data center capacity from the big hyperscalers. 76% of folks voting on the poll think this AI slowdown ends up bullish for compute, and honestly? It's hard to argue that logic right now.

they said in their blog post they started their swarm 2 weeks prior and updated all agents to a newer model halfway through. that’s too short for a new pretrain so must be RL or SFT

Source for RL during the NS run ? I doubt this

>Take an HVAC contractor
As well as
> For an entirely illustrative calculation
Mate, missing the point. Let's take RL example - I'm in charge of a lean team, it's 3 of us doing planning, scheduling, coordinating, overseeing, document processing. 12 permanent field operatives, 7 subcontractors we engage if and when there's demand spike - exactly the size of operation TTAN trying to carve their niche in. Sell me it? Start with estimating the price how much would my subscription be?
>loses jobs because nobody answers the phone during busy periods or after hours.
- Doesn't happen. We answer our phones after hours included, every phone has missed calls history, even if busy we're able to engage for a second and ask for patience or promise immediate call back
>homeowner call, nobody answers, and they try another contractor
- As above: we answer. As from end-user POV I'd do just that if a machine answers my call - hang up, call the next one
>virtual agent could answer,
- above, don't repeat the obvious, already bored
> establish what the customer needs, check the contractor’s scheduling information and book an appropriate appointment.
- So can a human. AI can suggest a decision, still need a human to read and approve.
>That booking then connects with the business’s dispatch, customer records, invoicing and payment processes.
- Dispatcher, a single person is doing all that for 12-20 field operatives so far away from your previously mentioned bloated 2:1 example Very efficient multitasker, I'm sure he'd be excellent flight controller lol - and very motivated person. Best part? Works for peanuts (and glory).At a cost of $250 per field operative per month you ask for I'd rather buy them a bag of coke.
- Other 2 members of the team capable of subbing/helping out.
- From inquiry to offer to schedule update to work order, report, invoice etc - few clicks. Everything interconnected since company founder wrote his own software back in 1996. Still has eyes on every step of every operative at a glance on his phone. Efficiency analysis included. God knows what else, I only know parts that I use.
Conclusion : For now, don't see the point. Maybe you would like to buy my "lean team" course? One time payment, no subscription? Now I can offer you a friends and family discount - for mere 7k we'll teach you how to go from 2 operatives per admin to 20. But you need to decide now, not many seats left available and discount offer is time limited. Lemme know (we answer every call)

>Wow, an actual response. Didn’t think I was going to get one
Yea IKR wasted my best on deaf ears and collected few dvotes. Words **are** hard. However I do remember reading you and was intrigued to investigate a little bit.
After that, understanding the product a little better it came down to few old school investment guidance principles - I don't understand it (sufficiently), I don't like it (we can DD on whys), intuitively I didn't recognise it's future potential (ofc I'm wrong about almost everything, almost all of the time lol), don't see the problem it would help with (field operative vs admin impact on RL savings) don't see the moat (we have free, house made, interconnected software)
Now, we can go through the list of your arguments, I'd prefer conversational style, like we've met at the coffee machine with 30 seconds to spare. I'm managing a small team, give me a problem you think TTAN (and only TTAN) can solve and I'll tell you how we do it?
One of the old boys said something like "if you can't explain your idea to a 10yo in a minute - it's a bad idea"
Anyhow, after hours of reading there was zero interest to dive in TTAN's own financials - therefore I'm going to sit this one out.
Also, as your last sentence in original post says, I'm not recommending, just thinking aloud, personal POV - in top of reasons above TTAN as investment doesn't fit what I'm doing with my portfolio at the moment - main thoughs last few months was to reduce the impact of anything AI related, cut down on number of holdings so I've liquidated some very good companies, way better than TTAN - likes of MSFT, AMZN, AMD to give you a general idea. Too much of a good thing, in a process of real strategic 180 if you will - so I'll be first to admit there might be subconscious bias playing great part in my "verdict". That said, I admire the depth of your investigation process, also looking outside of most hyped names, thinking like a contrarian ie buying a potentially very good company that's possibly very much in sale rn. GL brother

I'm following 2 online shops that sell preloved shirts, mostly RL. Aside from selling online, they're setting up pop-up shops at bazaars and flea markets. I can say the prices are competitive and quite affordable that's why they're selling fast. You can use your digital marketing expertise here.