Every AI subscription is a ticking time bomb for the frontier provider; within a few years we will be running local models as good as today’s frontier models with almost no cost burden. The floor will fall out of the enterprise market for all the frontier companies.
> within a few years we will be running local models as good as today’s frontier models with almost no cost burden
Based on what? The RAM requirements alone are extraordinary.
No, running large models on shared, dedicated hosted hardware at full utilization is going to be vastly more cost-efficient for the foreseeable future.
I take it you haven’t actually run any of the current gen local models?
They all fit on fairly accessibility hardware, and their performance is at least on par with what I was paying for last year.
I have one of my agents running entirely from a local model running on a MBP and it has repeatedly shown it’s capable of non-trivial tasks.
Playing around with another, uncensored, local model on my 4090 desktop has me finally thinking about canceling my personal Anthropic subscription. Fully private, uncensored chat is a game changer.
For work it’s still all private models but largely because, at this stage, it’s worth paying a premium just to be sure you’re using the best and it saves the time of managing out own physical servers. But if we got news tomorrow that Anthropic and OpenAI were shutting down, a reasonable setup could be figured out pretty quickly.
Was using Gemma-4-A3b-26B for a while for chat (using llama.cpp for backend and Open Web UI for client features). I’ve been using Qwen-3.6-A3B for agents and am currently playing with one of HauHuaCS’s uncensored Qwen models for chat and really liking it.
I also have an agent using Kimi 2.6 as a backend (which is open, but not local) and for some coding tasks as well.
> Local modals are 6 months to 18 months behind frontier.
I wish this was true but it is not. And I am working on open source models so if anything, I would have a bias towards agreeing with you.
Frontier closed models (GPT/Claude) are gaining distance to everybody else. Even Google, once the king.
Your claim is a meme coming from benchmark results and sadly a lot of models are benchmaxxed. Llama 4, and most notably the Grok 3 drama with a lot of layoffs. And Chinese big tech... well they have some cultural issues.
"Qwen's base models live in a very exam-heavy basin - distinct from other base models like llama/gemma. Shown below are the embeddings from randomly sampled rollouts from ambiguous initial words like "The" and "A":"
But thank god at least we have DeepSeek. They keep releasing good models in spite of being so seriously resource constrained. Punching well above their weight. But they are not just 6 months behind, either.
I’ve worked, for a long time professionally, in the open model space for 3 years and up to 2 months ago I would have agreed with you. But it’s empirically not the case today. These models (combined with a good harness) have dramatically improved in both power and performance.
Gemma 4 was a major improvement is self-hostable local models and Qwen-3.6-A34B is a beast, and runs great on an MBP (and insanely well on a 4090).
The biggest lift is combining these models with a good agent harness (personally prefer Hermes agent). But I’ve found in practice they’re really not benchmaxxing. I’ve had these agents successfully hand a few non-trivial research projects that I wouldn’t have been able to accomplish as successfully even last year.
When you add in the open-but-not local models, Kimi, GLM, Minimax, you have a lot of very nice options. For personal use anything I don’t use local models for I give to my Kimi 2.6 powered agent.
For specific use cases, absolutely, a harness and other techniques help (this is literally what I'm working on). But GP was talking about general use.
Over-promising is a very stupid thing. Nobody will value the intermediate steps. Nobody will value all the effort because they will always compare us with frontier models made with billions and we will become a running joke. So please stop.
Over-promising is what the frontier companies are doing. I'm not pretending open weight models are gonna do your homework and pay your taxes and remember your wife's bday with a super personalized gift. I'm just saying that they seem pretty good for what they are. There's no promise being made here.
I said "open weight" rather than "local". I mean, local if you have $240k to drop on GPUs but you can run Kimi k2.6 on a B300 cluster for ~$50/hour too.
Yeah I mean the US has gotten tough on, like, foreign interference in elections and cyber security, but if you have the Chinese state behind you—which they absolutely do and as an observer, obviously, they have to—no company can stop them.
Case in point: North Korea, with far, far fewer resources.
Local models are ~18-24 months behind the frontier on approximate intelligence, and then like 36-48 months behind the frontier on inference speed for nice hardware.
I've got a 128GB strix halo staying warm at home, it has nothing on top models with big budget. It's good supplement to low end plans for offloading grunt work / initial triage
It is not getting easier to obtain hardware that can run models which are sufficiently useful to undercut frontier models, if anything the cost of such hardware has gone up by 25% or more just in the past 6 months.
I think hardware prices will come back down once we start seeing more efficiency improvements in models and hardware, and once more people and companies self-host models (which seems to be happening more and more these days). I think the massive infra/hardware expenditures of OpenAI and the like are going to end up unnecessary, leading to hardware price drops.
If companies decide to self-host, wouldn't that drive the demand and therefore prices up? Most companies currently do not have the needed infrastructure.
I think companies will self host (including on rented hardware) even if it's more expensive, and that, along with efficiency improvements, will drop demand for big AI. I think big AI is overspending on hardware/datacenters at the moment.
How do you know this? I'm not trying to attack your statement, I am genuinely curious how anyone knows anything about model performance outside of benchmarks that are already in the training set.
> Local modals are 6 months to 18 months behind frontier.
At what tps? You can run the new gemini flash or 5.3 codex spark at 1000+tps and run circles "open" models. You can't run anything useable locally without at the very least a blackwell 6000 if not two
Sure you can run qwen 3.6 at 20tps on a mac 128gb but let's not pretend this will get you anywhere
You can now buy 128 GB unified memory computers from AMD as commodity.
They’re still pricey, the world is still scaling up memory production, and a lot of code isn’t yet built for AMD, but we went from the Wright’s brothers first airplane to jet engines in 27 years.
I’m not sure “it’s only a few years away” but we are sure moving there fast.
Non-cynically: the frontier providers have a projection for demand.
Cynically: it’s become an executive-level gpu measuring contest. If you’re not making huge commitments on data centers, you can’t be a serious player.
Realistically: It’s a mix of the two. The recent Claude caps for agentic usage suggest that demand exceeded their immediate compute supply. That they can alleviate it with additional capacity from the existing and small-ish xAI facility suggests that either demand may not be rising quite as fast as anticipated, that they’re okay in the short term until more capacity comes online, or a mix of both.
Open questions:
1. At what price point does demand fall, and are the frontier providers overall profitable before that price point?
2. At what price/performance point do on-prem local models make more sense than cloud models?
How does that relate to my comment. I didn't say anything about the fungibility of either. Physical goods have wildly different logistical constraints compared to anything digital. This, and only this, I would argue, makes their production at home attractive to consumers. Tokens just don't have these properties.
>running large models on shared, dedicated hosted hardware at full utilization is going to be vastly more cost-efficient for the foreseeable future.
That is only true right now because hundreds of billions of dollars are being burned by these AI companies to try to win market share. If you paid what it actually cost, your comment would likely be very different.
No, it's economies of scale and I don't understand where anyone is coming from that thinks they'll be better off buying their own hardware, why would you get a better deal on MATMULs/watt than the cloud providers ?
Within 5-10 years you're going to see a box like one of those AMD Halo nodes running homes.
They'll be controlling lights and temperature, they'll be adding calendar reminders that show up on your phone and your fridge. Your phone and devices might sync pictures and videos there instead of the large cloud providers. They'll also be a media server, able to stream and multiplex whatever content you want through the home. They'll also be a VPN endpoint, likely your home router, maybe also a wifi access point.
I think this makes quite a bit of sense. I don't think they'll be ubiquitous, but they could be.
This distributes the power demand where local solar generation can supplement , gives the home user a lot of control, and claims overship of the user data from big tech.
Maybe I'm imagining things but this is what I think is coming.
It's the lmm/data heart of the home. A useful digital tool.
It's amazing to me. You say this like it isn't an absolute horror. We've really ramped up the malignant bloat of the software industry if it goes this way.
We'll have this massive machine to do "home automation", something that by all rights should be possible with less computing than is deployed in smartwatches today. Yuck...
Moving the LLM from SaaS to the home, reducing the power distribution problem, and giving people control back over their data - getting it away from Big Tech. The home controls should also be more responsive that most current modern home automation that mostly uses wireless and Bluetooth to a cloud service. These are all good things.
That's just one piece of the puzzle. If you're running the LLM there's no reason your family's mobile devices couldn't use said home LLM box to save battery life on their devices while maintaining control of their data, searches, photos, files, etc.
Another victim of Goldratt's Theory of Constraints. Some things are more important to optimize for than MATMULs per Watt. What that is I leave as an exercise to the student. May you realize what it is before it is too late.
Some individuals will choose some $10,000 hardware so they can keep freedom and privacy and that's well and good, my point is just that freedom and privacy is not what wins marketshare, and hence, IMHO, local LLMs are not going to catch up and surpass frontier models like some in this thread like to claim
We don't know the parameters but it probably takes at least a H100 and possibly several to run a SOTA model. Given the pricing (25+k per H100 + hardware to run it) and power (700W per H100 + hardware to run it), I don't see how anyone except for a largish company can afford to run this.
I was being pretty generous to the comment I was replying to. Needing 32+ H100s just strengthens my argument that people aren't going to run frontier models locally anytime soon.
> shared, dedicated hosted hardware at full utilization
I must say that the largest dedicated hosted hardware providers now, like Amazon or Google, to a large extent do not produce the software they are offering as a hosted solution (like Linux, Postgres, Redis, Python, Node, etc). Similarly I'm not sure if the producers of the frontier models are going to keep their lead as the service providers for the most widely used models. They would need to have quite a bit of an edge above open-weights models.
Also, models are given very sensitive data to process. For large organizations, the shared dedicated hardware may look like a few (dozens of) racks in a datacenter, rented by a particular company and not shared with any other tenants.
I strongly disagree. Humans are so insanely well incentivized here with trillions in market share to make localized AI good enough and that’s the only benchmark they need.
Are they? I don't believe there's that big of a market for local AI. Most people don't care that much, and you'll most likely lose the advertising revenue.
>I don't believe there's that big of a market for local AI. Most people don't care that much,
I agree that the market for local AI is basically limited to nerds at this point, but that's because nobody's really explained why local AI is a good thing and also because the vast majority of people need the $20 paid plan at most. How much time and money would it take to get something half as good as OpenAIs products running locally?
It will take another [human] generation before AI is well integrated into everyone's daily lives where people will expect a local model handling things for them. I don't think the killer app has arrived yet (OC is a hint of what is to come).
I agree that the vast majority of punters don't care about "local AI".
However, if you can deliver 90% of the value of AI for 90% less cost, that is a really big incentive. Companies will spring up to fill that kind of gap.
Nobody can undercut the big AI players right now because they are all over-funded by VC money. Once the frontier companies try to match cost to expense, suddenly they become very, very vulnerable.
What kind of codebase do you work on (number of lines?). How many tokens does your local context support?
Maybe your statement is true for smaller codebases and shorter conversations, but I’d be surprised if you actually achieve good results on millions of lines of code with a million token context.
Granted if your setup works well for your workload then that’s all you need.
I run Qwen3.6-35B-A3B on my 8GB VRAM GPU for 3 weeks now and its been blowing my mind how good it is (coded multiple tools that I use daily, setup CI/build scripts for several projects, meaningfully contributed to a large personal project, etc).
No one can deny that right now these new compact models are not as good as frontier models but for the first time we actually have competent local-first models. If I give you a local model that runs on your current hardware and performs at 75% of the ability of a frontier private paid model, would you still pay for frontier? More importantly, would you hand control of your processes and code to them knowing enshitifcation and price-hikes are always lurking nearby?
For businesses, I get it you want to compete. But personally, it's over. Even if I considered for a second paying OpenAI/Claude, not gonna happen now.
Or put another way, the frontier models are very quickly deprecating assets, because of the competition in the market.
They have to keep getting better to stay ahead of each other and open weight.
Which means it's the opposite of a timebomb, the article has it completely backwards, tokens at current level of reasoning will continue to get cheaper.
I'm not sure 'local' will be the end state, as hardware needs are high. But certainly competitive forces tend to push profit margins toward zero.
I've spent the last month bringing in a small demo of what the future could be like, running Qwen, Gemma, and Deepseek, behind LiteLLM so we can monitor token usage, and instead of some dumb ass "tokenmaxxing" we're actively trying to get the cost of inference both down, and in-house.
Boss is happy, very happy. We're rolling it out more widely now.
> within a few years we will be running local models as good as today’s frontier models
I seriously doubt it. Scaling is already strained (don't buy into the "exponential" hype). And, in any case, the competition will be against the frontier models that will exist in two years.
> I seriously doubt it. Scaling is already strained (don't buy into the "exponential" hype). And, in any case, the competition will be against the frontier models that will exist in two years.
The big question I'd be asking if I was investing in one of the big players is if those changes are "it can do 99% instead of 97% of the tasks a user will throw at it" (at which point going local and taking back cost control/ownership makes a lot of sense, especially for companies) OR "it will fully replace a human with better output"?
I already don't need Opus for a lot of my tasks and choose instead faster/cheaper ones.
The former is a company that's gonna be trying to sell mainframes against the PC. The latter is a company that is in potentially huge demand, assuming the replaced humans end up with other ways of getting money to still be able to buy stuff in the first place. ;)
Exactly the right argument. Local LLM doesn’t need to outrun the bear (outperform data centers) it only needs to outrun its friend (total cost of ownership).
> I seriously doubt it. Scaling is already strained (don't buy into the "exponential" hype). And, in any case, the competition will be against the frontier models that will exist in two years.
But even if scaling plateaus for the frontier models, maybe distillation will improve to the point where smaller more manageable models can reach the same plateau. That would be great for local.
Genuine question from a place of ignorance: what in the silicon pipeline makes it take 2-4years to produce chips with a new model on them? Curious what the process bottleneck is.
Without being an insider, I imagine that most global fab capacity is contracted out several years in advance.
You might be interested in the tiny tape out project, which guides you through the process of getting your own design etched on silicon. If you only need larger features and not the next gen single digit nanometer stuff, you may not be so supply constrained.
I think you could get it down to three months between weight changes, if you can encode it in metal layers only. The remaining limits are the fab lead time, and the cost of a metal respin (hundreds of thousands to millions of dollars depending on process).
I think that comment meant it's 2-4 years until local models are good enough that it's worthwhile to burn an ASIC of them. Not that it takes 2-4 years to make an ASIC chip.
Why not have a bunch of SRAM and various operations like "Q4 matmul" in silicon? Model weights and even architectures could still evolve on a platform like that.
Doesnt "a bunch of SRAM" top out at maybe a few gigs per chip (with zero area used for logic)? You'd need an order of magnitude more to fit even a fairly weak general purpose LLM model.
If the silicon costs $200-300 and the company throws it away every two years that’s cheaper than a subscription.
Also, how many companies will just buy an M6/M7 MacBook Pro with 32GB+ of RAM in a couple of years and get “free” AI along with the workstation they were going to buy anyway?
This will be interesting. I can see some world where it’s used with consumers, but for the most part I think it will be in the cloud and that would make most sense
> within a few years we will be running local models as good as today’s frontier
Unless there isn't some important breakthrough in hw production or in models architecture, it's quite the opposite: bigger, more expensive and more energy-intensive hw is needed today compared to 1 or 2 years ago.
I can run qwen3.6-27b on a four year-old Macbook Pro that dominates ChatGPT-4o (the frontier model from 2 years ago) and is competetitve against early ChatGPT-5 versions. We are also getting a lot smarter about using and deploying these local models. Your entire AI stack from two years ago would be absolutely crushed by a todays local LLM models and a high-end local inference system when combined with a good modern coding agent.
Today open weights frontier models cannot run locally, unless quantization is used. Deep seek v4 pro require almost 1 TB of RAM in INT4.
I hardly doubt there will be consumer grade HW to run it in 2 years either. And deep seek v4 pro is not even close to OAI or anthropic frontier models.
I run it on my 4 year old MBP and get 10 tok/s. With the RAM shortage buying anything new today is a nightmare but anyone with a reasonably modern Mac could run it at q6 probably. It is mostly a toy as 4o models weren’t really suitable for real work IMO but at least it won’t ever give me a refusal.
At 10toks, are you using it interactively or do you submit a prompt and come back to it later? I always thought it would make sense to just do conversations over email, asynchronously, the model can take all the time it needs and get back to me when it has an answer.
10 tok/s is around the borderline of interactive being good. I did the math and it is mostly bottlenecked by memory bandwidth, so in the future I can expect to run a similarly sized model on my 4090 once it gets retired from gaming service and get ~25 tok/s which will be very usable.
Per frontier token. You're not calculating the cost of a fixed quality asset here. Old hw running non-frontier models will be very valuable. In fact, we have two direct examples: older server gpus actually appreciating and the very obvious fact that not everyone always use MAX FULL EFFORT BEST MODEL no matter what.
The economics of local AI just doesn’t make sense. A model like Opus is - supposedly - something like 5T parameters, which is likely something like 3TB of GPU memory.
Local models never reach the % utilization that cloud providers have (80%+), and they’re always going to be much better than local models for this reason.
Capex, opex, quality, and volume are tricky things to balance. On balance, pc/mobile are cheaper to operate than equivalent cloud and on prem deployments.
It’s not unreasonable to suppose that in 2 years time an opus 5 quality model will be etched into silicon for high performance local inference. Then you just upgrade your model every 2-3 years by upgrading your hardware.
I haven't been following anyone baking models into ASICs, is it not still necessary to pack just as many transistors onto a chip, whether it's an NPU or GPU, ASIC or not you still need to hold hundreds of gigabytes in memory, so how is it cheaper to bake it onto custom silicon than running it on commodity VRAM? (Asking because I don't know!)
Running local applications is less efficient than thin clients to the cloud generally, not just in LLMs. The trick is that you can get to the point where it's effective enough, and affordable enough, that the control and availability factors become dominant.
I just don't see how that's different from getting more value by giving all your employees the most stripped-down chromebook-type devices and running everything else in the cloud, than by giving them "proper" laptops with local apps.
It's a measure of a very thin sort of "value/$" that excludes a lot of other things that could be of value to a business, like control, predictability, and availability.
Thin clients have been going away for a long time. The trend has been to continue to push higher levels of compute into ever-smaller and ever-more-portable devices.
I don't know that this is true. The cloud companies are making money, and inferrence is kind of just "hosting an inferrence server and trying to keep it humming 24/7"
But in many cases self hosted or dedicated boxes are cheaper than cloud.
Eventually, we'll see. Frontier models still need some pretty serious hardware which will slowly come down in cost. Smaller models are becoming more capable, which will presumably continue to improve.
I think there's still a pretty big gap, though. Claude estimates Opus 4.6 and GLM-5 need about 1.5Ti VRAM. It puts gpt-5.5 around 3-6Ti of VRAM.
That's 8x Nvidia H200 @ ~$30k USD each. Still need some big efficiency improvements and big hardware cost reduction.
If that’s true, then it will be even cheaper to provide them as a subscription. Following your logic, every company would be running their own data centers instead of using cloud providers.
The economic question is whether the average company will have the time or talent to roll their own models instead of eating the cost increases. The firms in question are exactly the same that have already decimated their teams. Can they so quickly pivot to self-hosted models if their AI workloads suddenly cost them 10x more? I bet most will simply start shoveling themselves deeper.
Hard agree - the benefits of local/self-hosted models are not just hardware/cost (it might be more expensive at the moment), but what you get in exchange is unnerfed/unstupified models, full cost/usage transparency, optimized/specialized models, privacy/security, etc.
There's still going to be plenty of use-case and demand for frontier models running across hundreds or thousands of GPUs. It's just not going to be in the current shape - certainly not accessed by the general public for rote business tasks.
I think this is a good under-represented point. Again and again things that could only run on a mainframe get ported to the personal device level. However it looks like the campaign to eliminate the PC (by pre-buying all RAM) is the counter-stroke.
None of the models advanced enough to replace frontier will be able to run on your machine for any forseeable future or at a reasonable speed. 5tok/s is not acceptable.
To run deepseek v4 class model, you would need to spend $120k just in gpus.
People who are this certain of their predictions should be forced to put real money on them on Kalshi or Polymarket instead of drive-by blowharding on HN.
A lot of home computers are capable (with a large margin) to run a large amount of self-hosted services (eg: jellyfin, immich, minecraft, plex, karakeep, ... whatever people want to use).
And yet, less than 0.01% of the population (made up number, but I am more likely to be overestimating than underestimating) do so.
Running local models to do real work is likely to be another niche hobby.
I recently had my automatic reload double charge me $100. I tried reaching out to Anthropic, but my only option (of course) was a chat agent. After going through a conversation with it, I was told someone would reach out to help with the matter. Never happened. I eventually reached out to my credit-card company and did a dispute, which they just ruled in my favor.
Back in December the iOS app had a bug ( https://status.claude.com/incidents/6rrnsb1y0kbn) in which buying a subscription thru the Apple App Store would not register with the backend, so you’d be charged but not receive the plan entitlement.
I discovered this because I wanted to upgrade from free plan to the regular plan. I was charged, but remained in the free tier. Thinking it was a temporary bug, I tried buying the max plan. Same result.
I tried cancelling the plan and restarting but I when I went to buy the regular plan again, I was forever tagged as an “Apple” user and so could only manage the billing plan on the iOS app. I tried one more time, same result.
I tried interacting with the support bot and although it agreed that there was a bug and that it should be fixed and I should get a refund, my account never was able to get unstuck nor refunded. I lodged a refund request with Apple, which was relatively quickly refunded. The Bot never did escalate to a human as promised.
Even though the bug was ostensibly fixed, my account (personal email) remains in permanent limbo, unable to upgrade from Free to anything else (I tried again recently and same result - paid but stuck on free plan). I had to create a new gmail just to pay for Anthropic / Claude.
There was also a bug where you could cancel the subscription via the iOS app store and if you never opened the iOS claude app again, you'd keep the subscription forever and could use claude via the web, without paying.
Also when they added extra credits to everyone as an apology I was able to click the claim button multiple times and I got up to $400 in credits. Eventually a day later this dropped to $200 and then a few days later, $100 where it sits today.
I once had PayPal refuse to give me my money back (for a delivery) for months even though the postal service status clearly stated: "Address unknown, returning to sender."
I should have denied the PayPal charge on my bank account, that always gets a real human to look into it. Lesson learned.
I got given a gift card with around 6 months credit on it. I used up 1 or 2, and last week suddenly the credit disappeared. I reached out through their chat bot, raised a ticket and have been emailing them daily. Nothing. Absolutely not a word. Unfortunately I dont have the option for a charge back.
That's the thing, right? I would not be surprised if they have an agent that bans accounts that do chargebacks on them even when they're wrong. So you either accept it if you have to use it for work or you risk and deal with the possible consequences.
No bigsies just got a little trippy hallucination while vibing in the billing code bro. The spiritual support guru was walking the lonely wastelands and couldn't get back to you on this plane. Just wasn't meant to be
I've been using Cursor / Claude Code to open my Vault folder, or a sub-folder in the vault; since Obsidian is stored in .MD files you can chat with your LLM about whatever info is in there. I used this to review and prepare for interviews, and it was extremely effective in helping me land my new job.
Serous question - why do people stick with Clause Code over Cursor? With Cursors base subscription I have access to pretty much all the Frontier models and can pick and choose. Anthropic models haven’t been my go-to in months, Gemini and Codex produce much better results for me.
Cursor performs notably worse for me on my medium-sized codebase (~500kloc), possibly because they try to aggressively conserve context. This is especially true for debugging, Claude Code will read dozens of files and do a surprisingly good job of finding complex bugs, while Cursor seems to just respond with the first hypothesis it comes up with.
That said, Cursor Composer is a lot faster and really nice for some tasks that don't require lots of context.
My answer is that I tested both, and Claude Code (~8 months ago) was so obviously better than Cursor that I continue to happily pay Anthropic $200/month. Based on anecdotes I happen to catch, I don't believe Cursor's caught up.
The value isn't just the models. Claude Code is notably better than (for example) OpenCode, even when using the same models. The plug-in system is also excellent, allowing me to build things like https://charleswiltgen.github.io/Axiom/ that everyone can benefit from.
Because I tried all the Cs - Copilot, Cursor, Codex, and Claude - and Claude consistently have better results. Codex was faster, Copilot had better integration, Cursor sometimes seemed smarter, but Claude was the best most reliable consistent experience overall, so Claude is what I stuck with - and so did the rest of our eng department.
Because when it's good, it's really good - Cursor doesn't work as well for me and also I prefer the TUI experience. If anything, the real alternative is OpenCode.
Part of the sauce is not in the model, but in the agent itself. And for that matter, I think AMP an incredibly better agent that Claude Code. But then, Claude heavily subsidized subscription prices are hard to beat.
Follow the heads. Counting clockwise from the "A", the fifth boy has his head right on top of the line. When you shift to position B, the bit of the head on the outside becomes part of a hand.
I’ve owned two model Ys over the past 5 years or so. Zero maintenance issues. I also had a 2020 Model 3 that I recently sold and it had 1 issue with the small secondary battery after 5 years. Tesla charged me ~$140 to come to my house and replace it.
>How Deckerd can afford to live in one post economic meltdown is a bit unclear.
He's part of a precarious minority of semi-technical functionaries, armed bureaucrats afforded generous promotions and great inner leeway amidst the post-meltdown order of things, in return for their unquestioning allegiance to the same
Personally I prefer the PKD book. It was more nuanced. But the aesthetic of the first film was just wondeful. If somebody had sold cold cathode flouro umbrellas when the movie came out they would have cleaned up.
After Deckard did an exemplary job, everyone liked it so much that they they replaced his entire cadre with simulacra.
>Personally I prefer the PKD book. It was more nuanced.
Oh absolutely! Just recently bought a fake animal and pondered it. Love PKD for selling various angles on the same trip for decades; wonder if his OG exegesis can be read anywhere...
I have a copy. Send me an email and I’ll upload it somewhere for you. It’s not a great read, but it’s interesting in places. You can use rob.crimedoer at gmail.
In the "Deckard is a replicant" version that Scott has defended for years, I assume he's simply living in someone else's place (unaware that it's not his own).
