I always thought that Asimov's Laws of Robotics ("A robot may not injure a human being" etc) were an interesting prop for science fiction, but wildly disconnected from the way computing & robotics actually work.
Turns out he was just writing LLM prompts way ahead of his time.
Not only wildly disconnected, but purposefully created to show ambiguity of rules when interpreted by beings without empathy. All of Asimov's books that include the laws also include them being unintentionally broken through some edge-case.
More just that the rules are actually a summary of a very complex set of behaviours, and that those behaviours can interact with each other and unusual situations in unexpected ways.
It was weird to actually read I, Robot and discover that the entire book is a collection of short stories about those laws going wrong. Far as I know, Asimov never actually told a story where those laws were a good thing.
They aren't generally potrayed as bad, either, just as things which are not as simple as they first appear. Even in the story where the AIs basically run the economy and some humans figure out that they are surreptitiously suppressing opposition to this arrangement (with the hypothesized emergent zeroth law of not allowing humanity to come to harm), Asimov doesn't really seem to believe that this is entirely a bad thing.
Exactly! That was kind of the point IMO, that human morality was deeply complex and ‘the right thing’ couldn’t be expressed with some trite high level directives.
I use CNC machines and know how powerful stepper and servo motors are. You can ask yourself what will happen if your motor driver is controlled by an AI hallucination...
It's funny because Isaac Asimov would have come up with some convoluted logical puzzle to justify why the robot went on a murderous rampage - because in sci-fi robots and AI are all hyperrational and perfectly rational - when in real life you'd just have to explain that your dying grandmother's last wish was to kill all the humans, because a real AI is essentially a dementia-riddled child created from the Lovecraftian pool of chaos and madness that is the internet.
I recall that story of the guy who tried to use AI to recreate his dead friend as a microwave and it tried to kill him[0].
You couldn't sell a sci-fi story where AIs just randomly go insane sometimes and everyone just accepts it as a cost of doing business, and because "humans are worse," but that's essentially reality. At least not as anything but a dark satire that people would accuse of being a bit much.
If this makes it easier and faster to sort garbage, we could probably improve the efficiency of recycling 100x. I know there are some places that do that already, but there are so many menial tasks that could be done by robots to improve the world.
There are plenty of places [1] where garbage is sorted for free by poor people who scrape a living from recycling it.
Sorting garbage is a terrible job for humans, but it's a terrible one for robots too. Those fancy mechanical actuators etc are not going to stand up well to garbage that's regularly saturated with liquids, oil, grease, vomit, feces, dead animals, etc.
are you implying that society shouldn't aim to reduce human interaction with vomit, feces, and dead animals? Robotics in harsh environments isn't unheard of
I think they're pointing out you need to be cautious assuming a robot can be economically, sustainably deployed to do jobs in environments which are challenging for electro-mechanical systems.
An example: A friend worked on accurate built-in weighing machines for trucks, which could measure axle weight and load balance to meet compliance for bridges and other purposes. He found it almost impossible to make units which could withstand the torrents of chemical and biological wet materials which regularly leak into a truck. You would think "potting" electronics understands this problem but even that turns out to have severe limits. It's just hard to find materials which function well subjected to a range of chemicals. Stuff which is flexible is especially prone to risks here: the way you make things flex is to use softeners, which in turn make the material for other reasons have other properties like porosity, or being subject to attack by some combinations of acid and alkalai.
These units had NO MOVING PARTS because they were force tranducers. They still routinely failed in service.
It's interesting to think that it is more feasible (including economically) to expose humans to bleaches, acids, catalyst materials, electricity, abrasives, and sharp edges.
Why does it even need to be that type of robot, a conveyor that has items on it, but its a mesh, a camera looks, and if something can be sorted just use compressed air to move it to a collection area/bin. Put an electromagnet at the start of the conveyor that can move on a gantry to another bin.
Why's everything gotta have arms and graspers it's so inefficient.
Robots aren't climbing trees or chasing food. They don't need tails, either.
Why's everything gotta have arms and graspers it's so inefficient.
We have designed a lot of processes and workplaces around the assumption that the 'machine' working there will be be around 160-190 cm tall, with two arms with graspers on the end and equipped with stereo colour vision cameras. The closer you make your new machine match that spec the less changes you have to make to your current setup. It also makes it easier to partially swap in robots over time, rather than ripping everything out and building something completely new.
Having worked at a company close to this field, the real answer through is that both approaches are being done right now. People building new facilities from scratch are building entirely automated system where the 'robot' is the whole machine. People with existing facilities are more interested in finding ways to add robots to their current workflow with minimal changes.
I doubt anyone would use this kind of fancy machine to garbage handling until they become a commodity. I would bet that the first application would be to send those robots to trenches and foxholes...
The nuanced answer to this is they have a first mover advantage and make a great robot. The point of the thread is that new development is much cheaper for folks to figure it out. Recyclers are the most entrepreneurial people you will ever meet. we’ll figure out some good uses for this stuff when it gets cheaper.
Some (most?) of these aren't really AI-based at all. For example. traditional optical sorters typically rely on the reflectivity of materials at one or a few laser wavelengths directed onto the material.
The mapping between sensor signals and material types is usually hardcoded from laboratory test results.
Sure. What they will call AI [an unspecified number of years in the future] will be compared similarly the the SOTA AI models.
For long time the term "artificial intelligence" has just gone out of favour, but I do remember the days where a good AI research lab had a bunch of symbolics lisp machines
I mean that we have visual recognition systems that do not use any kind of machine learning whatsoever and those are the majority of systems in use at industrial scale.
Laser interferometry and DCT image distance, primarily.
Haha I just came up with that off the hip (never heard of, seen, or even contemplated sorting garbage before) because the idea that this needs articulation and graspers is the height of "we're VC funded and don't care about anything except runway". Laughable.
Yeah, maybe someone with more industry knowledge can give a better picture, but I have a hard time seeing how these robots would fit into and improve existing processes [0]. Garbage is mechanically sorted most of the way already; then IR is used to identify different plastics and air blasts are used to separate them out at dozens per second.
The Gemini robot tech is cool as heck, don't get me wrong, but it doesn't seem particularly well suited to industrial automation.
Well, it's actually good to have that kind of marketing. First, because there are people that don't care anyway and keep mixing things. So, robots can be useful just the same.
And for the ones that actually follow the marketing, it's a good incentive to try to reduce the usage of one use plastics and packages in general. Recycling is the last of the 3 Rs for a reason.
Honestly I am so frustrated with the approach of "lets take a population of millions of people and ask them all to sort perfectly". 'Tis a silly thing. Some people won't care, some people will care but mistakes will happen, some people will care about money more and so will deliberately dump things in the wrong bucket.
Get everyone to dump their crap into one pile and actually invest in industrial processes to sort the crap out.
Huge con: this is a complex problem with possibly poisonous/explosive ramifications if it goes wrong.
Huge Pro: If we can solve this issue, that is a society changing capability, forevermore.
Or until armageddon/robot overlords/singularity/zombie plague at least.
Picking up after your dog. Putting the grocery cart away after unloading. Shoveling the sidewalk in front of your house. Waiting to the side of the subway doors. Not talking during movies.
We are asked to do hundreds of little things that mildly inconvenience us in order to maintain some social contract. Sure they could be made easier/nonexistent with better technology, but I:
1) don't see why asking people to do their part is silly
2) don't see why this particular problem would be more frustrating than e.g. the others I've mentioned. I feel like they are all similar on the "effort" scale.
Although I guess I'd admit that asking people to sort recycling properly is very different than relying on them to.
> Picking up after your dog. Putting the grocery cart away after unloading. Shoveling the sidewalk in front of your house. Waiting to the side of the subway doors. Not talking during movies.
If 10% of people don't put their cart away, then 90% of carts still get put away. If 10% put things into the recycling bin that shouldn't go there, then 100% of that batch of material becomes unsuitable for recycling process unless expensive remediation is done first.
If those things can be automated, we should not waste time doing them. It’s not like they are enjoyable anyway. Count the time wasted sorting stuff and multiply for millions and millions of households
I have no issue with the simple niceties of life. It's nice to do the nice things for those around and helps create a high trust society.
But I don't think this is a good system of caring for our environment. If we cared properly, rather than half-arsing it we'd have a proper industrial system with known outputs that we could improve upon. Instead we seem to have a "feel good you did your part, now forget about it" process. I guess it is shambling it's way to something more but it doesn't seem like it's in a rush - kinda the same way the world agrees on acting on climate change but no one is in a rush.
Most folks when they think of recycling, think of the blue bin they put out every week.
That’s about 25% by weight of all that gets recycled in the country.
Metals, industrial scrap, and other sources are 75% of what gets recycled in the US.
We are blue collar businesses, with high labor costs. Many are exploring robotics actively for repetitive tasks. We have some robots in our process, looking for more when the ROI makes sense.
It may not be 100x, but there will be value in robots in recycling.
Big corporations definitely care about recycling. Sustainability is a major issue for them, not for marketing and such, but because they're thinking 50 years down the line. If they can't keep making xPhones then, they'll need to find a new product or invade a country, and both of these things need to be planned decades in advance. If recycling is a gimmick, it's more to stakeholders than consumers.
Soda bottles can be washed and nearly directly used by 3d printers. Spiral cut the bottle, Thermoform it into a continuous cylinder (it folds it in and heats it to make it solid, then immediately fed to the printer or spooled.
If plastic recycling was actually being done and was profitable I don't think there'd be a Pacific garbage patch and pfas in my heart right now.
Plastic recycling, as commonly understood and promoted, is largely a myth. While technically possible, the reality of plastic recycling falls far short of public perception and industry claims.
# The Reality of Plastic Recycling:
- Low recycling rates: Only 9% of all plastic worldwide is actually recycled[1][2]. In the United States, the recycling rate for plastic waste is even lower, at just 5-6%[5].
- Limited recyclability: Most types of single-use plastic cannot be recycled in the United States. Only plastic #1 and #2 bottles and jugs meet the minimum legal standard to be labeled recyclable[1].
- Downcycling: The majority of recycled plastic is of inferior quality, resulting in downcycling rather than true recycling[2].
- Economic challenges: Recycling plastic is often not economically viable compared to producing new plastic[4].
# Industry Deception:
The myth of plastic recycling has been perpetuated by the plastic and oil industries for decades:
- Misleading labeling: The Resin Identification Codes (RICs) on plastic products were created by the industry to give the impression of a vast and viable recycling system[3].
- Disinformation campaigns: The fossil fuel industry has benefited financially from promoting the idea that plastic could be recycled, despite knowing since 1974 that it was not economically viable for most plastics[3].
- Lack of commitment: In 1994, an Exxon chemical executive stated, "We are committed to the activities, but not committed to the results," regarding industry support for plastics recycling[5].
#Environmental and Health Impacts
- Pollution: Most plastic items labeled as recyclable often end up in landfills, incinerators, or polluting the environment[1].
- Health hazards: Plastic waste contamination affects soil, water, and air quality, potentially impacting human health[4].
Conclusion
The concept of widespread plastic recycling is largely a myth propagated by the plastic industry to distract from the real issues of plastic pollution and to avoid regulation. While some plastic can be recycled, the current system is far from effective or sustainable. To address the plastic crisis, focus needs to shift from recycling to reducing plastic production and consumption.
There's one shot that stood out to me, right at the end of the main video, where the robot puts a round belt on a pulley: https://youtu.be/4MvGnmmP3c0?si=f9dOIbgq58EUz-PW&t=163 . Of course there are probably many examples of this exact action in its training data, but it felt very intuitive in a way the shirt-folding and object-sorting tasks in these demos usually don't.
(Also there seems to be some kind of video auto-play/pause/scroll thing going on with the page? Whatever it is, it's broken.)
It felt extra fake - the cherry picked people lacking rudimentary mechanical skills, using the ~$50K set of Franka Emika arms vs their default 'budget' ALOHA 2 grippers, the sheer luck that helped the robots put the belt on instead of removing it from the pulley.
The trick was in that the belt was too tight for an average human to put on with brute force, and disabling the tensioner or using tricks would require better than average mechanical skills their specially chosen 'random humans' lacked.
Earlier in the video, where it was going to fold a "fox", I was expecting a fox, but a fox face. I know I should have high expectations at this point, but was disappointed from the result given the prompt.
I slowed it down to 1/4 speed to check -- the autonomous video is sped up 3x, but the human video seems to be 1x. I say that because generally no one moves that slowly for a physical task, not just in the "problem solving" aspect, but also in the "getting a belt to the gears" aspect. So, it appears that the robot did a better job than the human, but I believe the human only spent 1/3 of the time in the clip. After stretching the belt, it was probably put on easily, and likely the human still completed the task in 2/3 of the time of the robot.
Reference video (saw your clip is robot-only, but the robot vs human video is more telling):
I am not sure whether the videos are representative of real life performance or it is a marketing stunt but sure looks impressive. Reminds of the robot arm in Iron Man 1.
It's an impressive demo but perhaps you are misremembering Jarvis from Iron Man which is not only far faster but is effectively a full AGI system even at that point.
Sorry if this feels pedantic, perhaps it is. But it seems like an analogy that invites pedantry from fans of that movie.
The upshot of this is that anyone will be able to order a couple of robot arms from China and then set them up in a garage, programming them with just text, like we do with LLMs now.
Those tracks could be at the ceiling. Imagine a robot arm in a kitchen that is dangling from the ceiling. It could be helping when needed and disappear in a cupboard after that.
The cost of robotics is coming down, check out Unitree. A couple of robot arms would cost about the same as a minimum wageworker for 1 year right now. But of course they can go virtually 24/7 so likely 1/3rd the cost
It's quite telling that these discussions often end up at conclusion that we are becoming a developing (or 3rd world) country again, and not Star Trek society.
The problem with Google is that their ad business brings so much revenue that no other product makes sense. They will use whatever they learn with robots to raise their ad revenue, somehow.
Gcloud is a running business, and AI is a billable service in it. There's a strong incentive to branch out from 1 line of business, especially as AIe can replace regular Google search and the web browsing that shows Google ads.
Search is in real danger of mostly obsolescence. Ads aren't safe.
Google uses their insane ad revenue to subsidize the Xerox Parc / Bell Labs of the current generation. Waymo, DeepMind, Gemini Robotics. They're killing it and leading the entire market.
It's not just researchers. Engineers at Google get to spin up products and throw spaghetti at walls, too. Google has more money than God to throw around.
Google's ad dominance will probably never go away unless antitrust action by the FTC/DOJ/EU force a breakup. So they'll continue to lead as long as they can subsidize and outspend everyone else. Their investments compound and give an almost unassailable moat into deep tech problems.
Google might win AI and robotics and transportation and media and search 2.0. They'll own everything.
Google has been looking for post-ad post-search revenue for almost a decade now. They certainly won't dominate forever and have several signals flashing red for a few years now.
Google has been looking for post-ad post-search revenue for almost a decade now
With a reasonable degree of success. In their last quarter (see https://abc.xyz/investor/earnings/) 25% of their revenue was non-ads, and that percentage has been consistently increasing.
YouTube has bigger revenues than Netflix. While the majority of that revenue is from ads, they get it by providing immense value in the form of near-unlimited entertainment.
My bet is on transparent, contextual ads. Assuming the product from all of this is having a robot in your house, when you're doing something like cooking, it will say things like "have you considered trying an oat milk base? Oatly is a great option. I can Doordash some for you if you'd like..."
Since profit controls everything in this society and we are in a regulatory capture government, there is only incentives to build murder robots, not disincentives.
The issues with all of these robotic demo videos is "repeatability" and "noise tolerance".
Can these spatial reasoning and end-effector tasks be reliably repeated or are we just looking at the robotic equivalent of "trick-shots" where the success percentile is in the single digits?
I'd say Okura and Vinci are the current leaders in multi-axis multi-arm end-effectors and they have nothing like this.
question for the robot experts: what is the limitation that makes the movements so slow? for example when it picks up the ball and puts it in the basket. why couldn't that movement be done much faster?
From university, I vaguely recall that, I had to implement a lot of feedback and correction calculations when working on industrial robotic arms. Usually too much speed causes overshooting(going the wrong trajectory or away from target). The feedback is constantly adjusted until the target is reached, hence a lot of expensive computation and readjustment from all the sensor feeds. Additionally, faster movement also has risk of damaging nearby objects when overshoot happens and also harms/degrades the joints faster. For a simpler example, think about the elevator, what would happen if it were to go up/down very fast, how would you tweak your PID controller to handle super fast movement to not throw your passengers when you need to correctly align and halt at the target floor….
Camera feed processing latency would be my guess. The system needs to make sense of a continuous video feed so moving slower reduces how much happens in between frames.
In this case it’s the model. There’s an insane amount of computation that should happen in milliseconds but given today’s hardware might run 10 times too slow. Mind you these models take in lots of sensor data and spit out trajectories in a tight feedback loop.
I'm not a robot expert, but I do know the answer is simply safety. Once it learns what to do, it can do it faster and faster, but when something goes very wrong, it will go very wrong.
> To further assess the societal implications of our work, we collaborate with experts in our Responsible Development and Innovation team and as well as our Responsibility and Safety Council, an internal review group committed to ensure we develop AI applications responsibly. We also consult with external specialists on particular challenges and opportunities presented by embodied AI in robotics applications.
Well, for now, at least.
I know who will be the first shown the door when the next round of layoffs comes: the guy saying "you can't make money that way."
I'm a firmware engineer that's been working in consumer electronics and I feel very bleak about my future
I feel so left behind. I have extremely limited robotics and computer vision experience. I have no ML experience. The only math I know has to do with basic signal processing.
When I see open roles at these companies I think the projects I'm going to work on in the future will be more and more irrelevant to society as a whole.
Anyway, this is amazing. Please delete/remove my post if it seems like this adds nothing to the conversation
Get concerned when you see a real product in the market that has a sustainable business model.
The man behind the curtain here has an army of engineers, unlimited cloud nodes and basically has harvested all the data currently available in the world.
It doesn't get any better than this right now.
What's next? They'll ping you later on Linked-in with this awesome idea that you need to make sure runs in a $1 USD microcontroller with a rechargeable battery that is supposed to last at least all day.
The actual scary stuff is the dilution of expertise, we contributed for a long time to share our knowledge for internet points (stack overflow, open source projects, etc), and it has been harvested by the AIs already, anyone that pays access to these services for tens of dollars a month can bootstrap really quickly and do what it might had needed years of expertise before.
It will dilute little by little our current service value, but you know what, it has always been like this forever, it is just faster.
In the meantime, learn to automate the automator, that's the way to get ahead.
Man, we shared our knowledge via books long before the internet. And a lot of those AI models train off of thousands of books as a base before they try to incorporate less accurate knowledge from the wild internet. The cat was out of the bag on that long ago.
I saw Musk saying a couple of days ago that we've "hit the limit of peak data" for training AI. My immediate reaction was no, surely you have not trained on every copyrighted textbook on every subject ever written. You hit the peak of easily accessible internet data that you could quickly steal to train your models.
But yes, in general, models won't get worse than they are now (or if they do, they won't stay that way.) At Google, search has been enshittified for business reasons, not technical ones.
> The actual scary stuff is the dilution of expertise, we contributed for a long time to share our knowledge for internet points (stack overflow, open source projects, etc), and it has been harvested by the AIs already, anyone that pays access to these services for tens of dollars a month can bootstrap really quickly and do what it might had needed years of expertise before.
What scares me more is the opposite of that: information scarcity leading to less accessible intelligence on newer topics.
I’ve completely stopped posting on Reddit since the API changes, and I was extremely prolific before[1] because I genuinely love writing about random things that interest me. I know I’m not the only one: anecdotally, the overall quality of content on Reddit has nosedived since the change and while there doesn’t seem to be a drop in traffic or activity, data seems to indicate that the vast majority of activity these days is disposable meme content[2]. This seems to be because they’re attempting desperately to stick recommendation algorithms everywhere they can, which are heavily weighted toward disposable content since people view more of it. So even if there were just as many long discussion posts like before, they’re not getting surfaced nearly as often. And discussion quality when it does happen has noticeably dipped as well: the Severance subreddit has regularly gotten posts and comments where people question things that have already been fully explained in the series itself (not like subtext kind of things, like “a character looked at the camera and blatantly said that in the episode you’re talking about having just watched” things). Those would have been heavily downvoted years ago, now they’re the norm.
But if LLMs learn from the in-depth posting that used to be prominent across the Internet, and that kind of in-depth posting is no longer present, a new problem presents itself. If, let’s say, a new framework releases tomorrow and becomes the next big thing, where is ChatGPT going to learn how that framework works? Most new products and platforms seem to centralize their discussion on Discord, and that’s not being fed into any LLMs that I’m aware of. Reddit post quality has nosedived. Stack Overflow keeps trying to replace different parts of its Q&A system with weird variants of AI because “it’s what visitors expect to see these days.” So we’re left with whatever documentation is available on the open Internet, and a few mediocre-quality forum posts and Reddit threads.
An LLM might be able to pull together some meaning out of that data combined with the existing data it has. But what about the framework after that? And the language after that? There’s less and less information available each time.
“Model collapse” doesn’t seem to have panned out: as long as you have external human raters, you can use AI-generated information in training. (IIRC the original model collapse discussions were the result of AI attempting to rate AI generated content and then feed right back in; that obviously didn’t work since the rater models aren’t typically any better than the generator models.) But what if the “data wells” dry up eventually? They can kick the can down the road for a while with existing data (for example LLMs can relate the quirks of new languages to the quirks of existing languages, or text to image models can learn about characters from newer media by using what it already knows about how similar characters look as a baseline), but eventually quality will start to deteriorate without new high-quality data inputs.
What are they gonna do then when all the discussion boards where that data would originate are either gone or optimized into algorithmic metric farms like all the other social media sites?
[2]: I can’t find it now, but there was an analysis about six months ago that showed that since the change a significant majority of the most popular posts in a given month seem to originate from /r/MadeMeSmile. Prior to the API change, this was spread over an enormous number of subreddits (albeit with a significant presence by the “defaults” just due to comparative subscriber counts). While I think the subreddit distribution has gotten better since then, it’s still mostly passive meme posts that hit the site-wide top pages since the switchover, which is indicative of broader trends.
It definitely adds value to the conversation - we're all human, we're all unsure about the future and our place in it.
I'm just scared about a future where humans (say the next generation, kids 1-5 years of age right now) lack in-depth knowledge of almost everything and it's mostly AI writing low-level code, so there are no more "human experts."
We've already seen this happening where Gen Z mostly interacts with the world using phones and struggle with older operating systems/desktops, just like older generations. AI is going to make that 10x worse going forward.
In 10 years, no trace of our current practices will remain in a recognizable form. It'll take longer than most of us think -- imagine how nonplussed Winograd and the rest of the SHRDLU-era AI gurus would have been to see how long it took to pull off the dice-matching trick in the video -- but when it does happen, it'll happen faster than we think. We're not yet at the tipping point, but it's close.
That is only a negative if you believe that net intelligence is going down due to this level of increase of "easy of use" technology. I agree with you that it is bad for the avg person today, but for the smartest person, things are better.
Definitely believe we are reaching some sort of global maxima in terms of intelligence in our current structure of society
> I'm just scared about a future where humans (say the next generation, kids 1-5 years of age right now) lack in-depth knowledge of almost everything and it's mostly AI writing low-level code, so there are no more "human experts."
It isn't. I mean, that depends on what "low level code" means. We have compilers so, to an extent, it's something desirable. But if "low level code" means everything we understand as code today, it may not be great. Human languages aren't precise enough for the kind of work that needs to be done.
But let's say it's accomplished. What will end up happening is that AI (should it work to the extent it's been hyper) will replace all the 'fun' jobs and we'll be left with either no jobs (and no income), or the most menial physical labor imaginable.
I have a million and one things I want to accomplish but can't due to lack of time and energy as I work full time.
This includes reading, gardening, playing sports, learning to play the piano, etc. Of course some of that is consuming media, but I don't think people will just become couch potatoes.
Think of all the things "high society" accomplished in Victorian england because they had the time, energy and resources.
Why would it be? I can understand wanting us to be free of having to do the work, but having essentially no-one understand the systems they're using is something we haven't encountered once in the modern period. It feels more akin to the post-Roman Britons, who inherited the Empire's hydraulic infrastructure but not the know-how to fix it when things went wrong.
Couldn't a similar argument be made about using a calculator? Hopefully, the tools created based on these new technologies will enable future generations to achieve things that perhaps we haven't even considered before.
Hey, this is not new. For me these are akin to a web dev building an ERP system vs another creative coder building beautiful motion graphics using same tech.
While the ERP is boring as hell compared the the creative coding results, the latter is novelty and often has no intrinsic value.
Also, I see these videos and get deja vu of boston dynamics demos from years back. Not seeing anything new here except this is just early beta version of boston dynamic robots backed by different models.
Also amount of people around the demo set tells me, a lot of supervision and retakes happened. I often do not trust such demos(experience from seeing what goes behind the scene with cherry picked takes being published).
Anyways, my point is, just chill out. I remember how AWS/GCP/Heroku etc were eradicating IT admin but instead now we have dedicated DevOps and IAM specialist roles… and every day I see 7:1 ratio of job vacancy for DevOps:SWE.
No worries. You know the hard part: dealing with hardware. ML/AI/Comp Vis can be learned fairly quickly if you don't need to dig very deep into algorithms by yourself, but use some higher level libs like scikit-learn, pytorch, etc.
The math, well, basic signal processing means you know some algebra and differential calculus. Which is enough, unless, again, you don't want to prove theorems or invent new algos.
"Being left behind" is a floating point. If you think there's something you'd like to learn (or would be valuable to learn), just start digging in. Picking new things up usually takes far less time than you'd think, especially if you have existing experience in an even semi-related field.
Even for AI engineers, the future is not necessarily bright. These approaches are so powerful and so general that the world is probably not going to need that many of them.
I think where the real work will be is taking these models and creating real products out of them.
I think you are right, the value is in application. If you have a problem to solve with AI, you benefit from AI. If you don't, you don't.. like any software. The AI providers get $20/month, you can get anything out of AI. Users have a much higher upside than AI developers and providers.
I was at the SCaLE22x linux expo in Pasadena last week and there was a company Replit that has a tool where you type the website you like and it was pretty decent. They said someone came by and in a prompt alone created a GPDR compliant cookie popup. It's another tool, it wasn't perfect, but okay for some one-off sites. It will require skills to direct and know what you want, just like always. Embrace the power, know the limits. Let the new skills enabled by the new tools empower you, reject vendor lock-in to keep yourself free to ply your trade. Just like last time around.
Let me bring some perspective. In 1998, CMU was showing a self-driving car "driving on its own" [1]
27 years later, 99.99% of trips are driven by humans.
Real-world robotics takes multiple decades to pan out. These demos are just that: demos. What you are seeing will not remotely impact your life before the 2050's, if ever.
What you should be worried about, however, is you (not your job) becoming irrelevant if you don't learn to write firmware using state-of-the-art AI tooling.
At the minimum: learn to work with Cursor (or equivalent). Make sure you work at a company that uses state-of-the-art AI tooling.
If you want to go further: learn to code (e.g. in python). Take undergrad/grad level courses in math, statistics and fundamentals of deep learning.
It might be time to pick up some books and reading up on this stuff. What's nice is that you can directly ask ChatGPT/etc questions about all this tech, and the math behind it! It's never been an easier time to learn new things.
At least you're honest. I feel a lot of people (on HN esp) take pride in their programming ability and intelligence to the extent that they think there's no chance AI will take over their job.
LLMs were a breakthough out of like a multitude of breakthroughs in AI in the past decade. I think there only needs to be a couple more breakthroughs in the next decade for it to come full circle.
Whenever I here a naysayer open his mouth it's like he's insulting a baby. Look it can't talk! no way it can ever program!
Either way it's not just you. The people creating AI are also as a side effect creating the training data for AI to replace them. So no one is safe.
When I see pro-AI promoters giving weird analogies that do not fit, I get a feeling that they are either management class(aka MBA types) or are not serious professionals.
One key point we must first understand, coding is NOT software engineering or even programming! Writing code is the last bit, a minimal fraction of the job description(unless you are actually indie dev or working for consulting firms). The core tasks include actually untangling the numerous vague requirements, understanding the domain, figuring out best approaches, performing various tests and checks, validating ideas, figuring out a cost effective solution, preparing rough architecture, deciding on an actual set of tech, align a hoard of people that everyone is on the same page, then start coding.
My IDE can already read my mind by auto-suggestion since many years and patterns/frameworks exist to reduce the amount of code I need to write. The issue is that, with these AI model, I just need to abuse my fingers slightly less. The other core duties are not yet solved and remains same archaic procedural everywhere in any/all serious roles.
And speaking of consulting firms, they are also clever and often has several implementations of same stuff, which they can modify a bit and sell for big money.
So in the end, people who jump into the pit because they are afraid of the juju mask are the prime target of the juju mask, for the rest of us, life goes on with minor bumps when the MBA comes to the desk and asks if it is possible to layoff few people to jack up the stock price this quarter yet… while subscribing to that new agenting engineer product suite for double the fees of what the laid off people actually costed, because their best friend in the golf club said the price will eventually become reasonable but the benefits are immediate.
Apparently there's a quote attributed to Bill Gates: "people overestimate what they can do in one year and underestimate what they can do in 10 years."
People overestimate the changes that could happen within a couple years, and totally underestimate the changes that would happen in decades.
Perhaps it's a consequence of change having some kind of exponential behavior. The first couple years might not feel like anything in absolute terms, but give it 10 or 20 years and you'll see a huge change in retrospect.
IMHO, I don't think anyone needs to panic now, changes happen fast these days but I don't think things are going to drastically change in these ~2-3 years. But the world is probably going to look very different in 20 years, and in retrospect it will be attributed to seeds planted in these couple years.
In short I think both camps are right, except on different timescales.
I agree. But I think the change will come between 5 to 10 years.
Anecdotally the amount of hype and interest has been growing exponentially. This will push progress to a maximal pace. The next 10 years will be significantly faster then the last 10 years.
I agree with your first part but I think you are vastly underestimating how much writing code is a part of programming. I also in these discussions people, ironically, really overestimate how much “untangling requirements” is part of the day to day for the majority of programmers. There is obviously some of that but unless you are just talking about consultants that interact directly with customers, a lot of this is done at the product or project management. You’d be surprised how much programming is in programming.
Erm no, coding happens at genesis of the product. The improvement, adjustments, maintenance is 99% of the lifetime.
If you are a professional, please tell me about how much new code you write vs perform other stuff(meetings, alignment, feature planning, system design, benchmark, bug fix, release). For me, the ratio of coding:non-coding is around 10:90 on average week. Some weeks, only code I write are the suggestions on code reviews.
Your dismissal of AI as merely a glorified autocomplete tool correctly acknowledges its current limitations—but it reveals an alarming blindness to the aggressively upward trendline of technological progress. Yes, today’s AI primarily simplifies mechanical coding tasks; your assessment of its present role is accurate. However, your argument dangerously ignores the relentless momentum and historical pattern of breakthroughs clearly indicating what's on the horizon.
Consider the unmistakable trend: In the early 2010s, deep learning fundamentally transformed machine perception, image recognition, and natural language processing, setting new standards far surpassing earlier methods. By 2016, AlphaGo decisively defeated human champions, showcasing unprecedented strategic depth previously assumed beyond AI’s reach. Shortly after, AlphaFold solved the protein-folding problem, revolutionizing computational biology and drug discovery by rapidly predicting complex molecular structures. In parallel, generative adversarial networks (GANs) and diffusion models ushered in a new era of AI-driven image creation, enabling systems like DALL·E and Midjourney to generate strikingly detailed images, surreal artwork, and hyper-realistic visuals indistinguishable from human craftsmanship. AI’s ability to synthesize realistic voices and human-like speech has dramatically improved through innovations like WaveNet and advanced text-to-speech technologies, leading to widespread practical adoption in virtual assistants and accessibility tools.
Beyond imagery and voice, generative AI has also broken new ground in music composition, where models now produce compositions so sophisticated they are difficult to distinguish from professional human creations. Transformer-based models like GPT-3 and GPT-4 represent a seismic shift in language generation, enabling nuanced conversation, creative writing, complex reasoning, and contextual understanding previously believed impossible. ChatGPT further pushed conversational AI into mainstream utility, effortlessly handling complex user interactions, problem-solving tasks, and even creative brainstorming. Recent innovations in AI-driven video generation and editing—demonstrated by advancements like Runway’s Gen-2—indicate rapidly expanding possibilities for automated multimedia creation, streamlining production pipelines across industries.
Moreover, reinforcement learning breakthroughs have expanded significantly beyond gaming, improving complex logistics operations, real-time decision-making systems, and autonomous navigation in robotics and vehicles. The impressive capabilities demonstrated by autonomous driving systems from Tesla and Waymo further underscore AI’s advancing proficiency in real-world environments. Meanwhile, specialized large language models have emerged, demonstrating near-expert performance in fields such as law, medicine, and finance, streamlining tasks like legal research, medical diagnostics, and financial forecasting with unprecedented accuracy and efficiency.
These advances are not isolated phenomena—they represent continuous, accelerating progress. Today, AI assists with summarization, automated requirement analysis, preliminary architecture design, and domain-specific problem-solving. Each year brings measurable improvements, steadily eroding the barrier between supportive assistance and true cognitive engagement.
Your recognition of AI's limitations today is valid but dangerously incomplete. You fail to account for the rapid pace at which these limitations are being overcome. Each "core task" you've identified—domain understanding, requirement analysis, nuanced decision-making—is precisely within AI's increasingly sophisticated reach. The clear historical evidence signals a near-inevitable breakthrough into human-level reasoning within our professional lifetimes.
In disregarding this aggressively upward trendline, you're making the same grave error committed by those who previously underestimated transformative innovations like personal computing, the internet, and mobile technology. Recognizing current limitations without acknowledging clear indicators of impending revolution isn't merely shortsighted—it's strategically negligent.
You might feel like this, but end to end learning in robotics has basically killed almost all of the high level trajectory planning, leaving just the real time control up to you.
I disagree, you are in a prime position to learn those technologies and have a greater breadth of opportunity. There is so much noise online, it’s all bullshit. Keep going!
So has the labels like "Autonomous 1x" actually been a thing that Google has used before, or is it actually meant to be an "inside joke" jab at Tesla's previous videos that had small labels indicating the video was sped up and/or being human controlled?
Parts of the video are sped up. These are labeled "Autonomous 5x", etc. In some cases, it's not obvious, so it's useful to have the label.
And many popular robotics demos are either controlled by humans or scripted, so it's useful to have the "Autonomous" label as well to clear up confusion. For example, I know a lot of people who thought the recent Unitree G1 demos were autonomous.
Videos like these are so often sped up or Teleop, I don't think it's really a jab at anyone specifically, just making it clear this video is showing an Autonomous agent without any speedup.
I'd like to see more about what the Gemini system actually tells the robot. Eventually, it comes down to motor commands. It's not clear how they get there.
I just want a device that works as a real-time bidirectional translator by collecting audio-visual input. It’d be great if I didn’t have to waste time learning German or other languages while living in those places.
Being able to order food and handle bureaucracy in these languages while speaking only English would be amazing. This seems like a simpler problem than tackling robots in 3D space, yet it’s still unsolved.
When they're competent enough to cook meals, that's the point of no return for the job market.
These models are nowhere near that for now, but I'll be watching to see if the big investments into synthetic data generation over the next few years get them closer.
They can be competent enough to cook meals in a controlled environment: (environment built for machines and specific dishes), without ever being able to replicate the same in a human restaurant.
There are a few companies that do robotic cooking, and it has its challenges due to the above reason. I am not aware of the cost problem though.
How cool would it be if this replaced someone at Subway though
Robotics has been trying the same ideas for the last who knows how many years. They still believe it will work now, somehow.
Perhaps it goes beyond the brightest minds at Google that people can grasp things with their eyes closed. That we don't need to see to grasp. But designing good robots with tactile sensors is too much for our top researchers.
This is lack of impulse response data, usually broken by motor control paradigms. I reread Cybernetic by Norbert Weiner recently and this is one of the fundamental insights he had. Once we go from Position/Velocity/Torque to encoder ticks, resolver ADCs, and PWM we will have proprioception as you expect. This also requires several orders of magnitude cycle time improvement and variable rate controllers.
I think this is correct, to an extent. But consider handling an egg while your arm is numb. It would be difficult.
But perhaps a great benefit of tactile input is its simplicity. Instead of processing thousands of pixels, which are passive to interference from changing light conditions, one only has to process perhaps a few dozen tactile inputs.
Ex-Googler so maybe I'm just spoiled by access to non-public information?
But I'm fairly sure there's plenty of public material of Google robots gripping.
Is it a play on words?
Like, "we don't need to see to grasp", but obviously that isn't what you meant. We just don't need to if we saw it previously, and it hadn't moved.
EDIT: It does look like the video demonstrates this, including why you can't forgo vision (changing conditions, see 1m02s https://youtu.be/4MvGnmmP3c0?t=62)
I think the point GP is raising is that most of the robotic development in the past several decades has been on Motion Control and Perception through Visual Servoing.
Those are realistically the 'natural' developments in the domain knowledge of Robotics/Computer Science.
However, what GP (I think) is raising is the blind spot that robotics currently has on proprioception and tactile sensing at the end-effector as well as a along the kinematic chain.
As in you can accomplish this with just kinematic position and force feedback and Visual servoing. But if you think of any dexterous primate they will handle an object and perceive texture, compliance, brittleness etc in a much richer way then any state-of-the art robotic end-effector.
Unless you devote significant research to creating miniaturized sensors that give a robot an approximation of the information rich sources in human skin, connective tissue, muscle, joints (tactil sensors, tensile sensor, vibration sensors, Force sensors) that blind spot remains.
Ah, that's a really good point, thank you - makes me think of how little progress there's been in that domain, whether robots perceiving or tricking our perception.
For the inverse of the robot problem: younger me, spoiled by youth and thinking multitouch was the beginning of a drumbeat of steady revolution, distinctly thought we were a year or two out from having haptics that could "fake" the sensation of feeling a material.
I swear there was stuff to back this up...but I was probably just on a diet of unquestioning, and projecting, Apple blogs when the taptic engine was released, and they probably shared one-off research videos.
I'm convinced the best haptics that I use every day are the "clicks" on the Macbook trackpad. You can only tell they're not real because they don't work when it's beachballing.
Can you imagine what Google hcould have achieved had they not let “Astro Teller” burn it all up? If all the money that’s gone into X had instead gone to space tech could they have a 2nd place to Space X?
Gathering the necessary training data for embodied models is going to be a real doozy. Hard to scale, unless you figure out how to make a perfect simulator or possibly collect data in a decentralized manner...?
1) tweak something like that to increase the likelihood of certain situations (abundance of a particular resource/object; frequent geographical feature), and
2) instruct your embodied AI to control a "player" model (to whatever degree of accuracy in articulation/mobility) to wander and perform certain types of tasks.
I suspect that if a nuclear war brings humans to extinction tomorrow, this project could be looked at by hypothetical aliens, visiting our planet in the future, as the "Antikythera mechanism" of our times. (well.... if we can trust the video)
This is the "Wozniak Standard" (sometimes called the Coffee Test) - Drop an AI enabled robot in front of a random house and ask it to bring you a cup of coffee. The robot would need to enter the house, locate the kitchen, locate the coffee machine, locate the coffee, locate the filters, locate the coffee mugs, locate a measuring spoon - then add the correct amount of water, the filter, the correct amount of coffee, start the brew cycle, wait for the brew cycle to finish, pour your coffee, then exit the house and deliver the mug to you. Extra points for adding cream and sugar.
I like that test. If the AI couldn't find a measuring spoon it would need to grab any spoon it could and just "eyeball it". Also, if there wasn't an actual mug then maybe a glass will work (but not a pint glass) and certainly not a plastic cup. When delivered it would have to know to say "couldn't find a mug so i grabbed a glass". there's other things too, can't find regular coffee but it found some instant coffee? The AI would need to decide if that will work or should it ask first. All of those things are petty easy for a human.
You might refuse to do it, but I doubt you'd ever actually completely fail it. If someone offered to pay $10 million if someone could go into a house, make a cup of coffee, and come back out with it, I imagine just about any functional adult would figure out a way to return with a cup of some sort of liquid resembling coffee. I don't see anyone saying, "Sorry, making a cup of coffee is too difficult, I'm going to forfeit the $10 million."
But sure, without proper compensation a lot of people would probably just say "I can't do it" as a way of avoiding the task.
Repeatedly? As in you would come back and tell whoever you're with "I gave up"? Like I can understand wanting to ask for e.g. "where do you keep the coffee", but if that wasn't possible -- say the host is asleep, and I'm there taking care of them -- I would certainly be able to figure it out. Just open cabinets and peek / carefully rummage around until you find what you need.
It is not a high standard, I am sure you could train a chimp to pass this test[1]. If you know how to use a standard coffee maker and live in a typical American home, and the test is done in an typical American home with a standard coffee maker, you can definitely pass this test 100% of the time.
I understand that many people don't live in America and don't know how to use a coffee maker. That is 100% irrelevant. There is a frustrating tendency in AI circles to conflate domain knowledge with intelligence, in a way that invariably elevates AI and crushes human intelligence into something tiny.
[1] The hard part would be psychological (e.g. keeping the chimp focused), not cognitive. And of course the chimp would need to bring a chimp-sized ladder... It would be an unlawful experiment, but I suspect if you trained a chimp to use a specific coffee maker in another kitchen, forced the chimp to become addicted to coffee, and then put the animal in a totally different kitchen with a different coffee maker (but similar, i.e. not a French press), it figure would figure out what to do.
"locate the filters, locate the coffee mugs, locate a measuring spoon" in a random house in America is a very high standard. We’ll have to agree to disagree on that. If you teleport me into a random house, I’ll likely spend at least an hour trying and failing at that task, and most of their cabinets and drawers will be open by the end of it.
It also excludes corner cases like "what if they don’t have any filters"? Should the robot go tearing through the house till they find one, or do nothing? But what if there were some in the pantry — does that fail the test? There’s all kinds of implicit assumptions here that make it quite hard.
You can't honestly claim that it would take you an hour to accomplish such a high probability task - have you never visited the house of a friend or family and had to open a few cabinets to find a water glass or a bowl or a spoon?
As for the point of corner cases being hard - I mean that's the point here, isn't it?
and what if there's only a Nespresso machine, a Keurig machine, instant, a french press, a moka pot, or a cappuccino machine (we can argue if an americano is actually coffee, but if that's what the house has, and no drip machine + accoutrements, you're not getting anything else)? Human or bot,
that's a lot of possibilities to deal with, but for a bold human unfamiliar with those, they're just a YouTube video away (multiple ones if it's a fancy cappuccino machine). Until AI can learn to make coffee or change an oil filter on a 1997 GMC from watching a YouTube video, it'd be hard to consider it human-grade, even if it has been trained on all of YouTube, which assumedly Google has done. There are certainly things people do on YouTube that I couldn't do after a lot of intense practice, though, so I'm not totally convinced that's the right standard. It doesn't cost millions of hours and dollars of training and fine tuning time for me to, say, be able to tie a bow tie from a YouTube video though, even if it does take me a couple of tries.
It probably shouldn't continue to surprise me how often people's "AI benchmarks" exclude a significant fraction of actual, living, humans from being "human-grade".
it's about to reach that point soon actually. There is no reason these models can't be optimized/distilled. And actuators be cheaper (it's happening already).
I don't think this argument really matters. Consumer drones are being used in active warfare today, as we speak, with minimal modifications. The cat is out of the bag no matter which way you look at it. You could just as well say that many chemicals at the construction store are easily weaponizable, they make for an explosive and there's plenty of guides online on how to do so.
Even with access to explosives, you still need a bit of gusto to carry out the rest of the crime. I think it's a bit different when you can ask the weapon to find and eliminate its target without you ever having to leave your garage.
Not being a high profile target myself, I'd rather take that risk and see where it goes. Unfortunately it's the high profile targets themselves that make the decisions, so after the first few incidents I figure there will be this whole mess where they try to clamp down on access to such things without sufficient forethought.
What's interesting is the vision language capability they have. Being able to verbally describe tasks and determine if a task was completed means they might be able to do self-play for a massive number of different tasks to improve motor skills.
To me the part where the two robots clean the desk while the person is working would be a dream come true. This could easily increase my productivity by 100%.
The video demonstrations are underwhelming to be honest. Obviously has been pre-trained to do these “random” tasks. Wonder how many cuts they had to do before it was picture perfect.
Also, I vaguely remember similar demos without the AI hype. Maybe it was from DeepMind, or another upstart back in 2015.
i love these robots and all but it's still the world's most expensive paper folder. none of these are energy efficient enough for production or are ever going to be as profitable as a simpler automated process that misses some targets every now and then
I love how everything is just "AI" now.
Machine Learning? AI
Random Forest models: AI
Some basic curve fitting? AI
People in India Mechanical Turk-ing responses ? AI.
A guy in a van running the robot pouring you a drink? AI.
Anyone else is just not interested in deepmind? They keep releasing "breakthrough" after "breakthrough" with zero code release. I just checked and I still can't do anything with alphaproof, almost a year later. They might as well tell me they solved world hunger, can stop aging and discovered a way to travel FTL.
They have a tendency to make impressive blog posts way, way before they can figure out how to make products. In the spirit of openness it's nice to know what they're working on, but yeah it's important to add a couple years to any availability estimation.
The problem with Google is that they keep putting out "videos" but almost ship an actual product. I'm not sure what's the end goal of this other than "get some people excited" or "justify R&D spend to shareholders".
This is a great achievement and I'm not underestimating the work of the people involved. But similar videos have been put together by many research labs and startups for years now.
I feel like Google's a bit lost. And Sundai's leadership has not been good for this, if we're honest.
GOOG is around the same price as it was in 2022, which means the AI wave went by through them with zero effect. With other tech companies doubling/tripling their market cap during this time, Sundai really left 1 trillion of unrealized value on the table (!); also consider Google had all the cards at one point, quite mediocre imo.
I'm cynical, but several hundred thousand patents are issued every single year, if you don't get one then your competitors will.
You don't have to release a profitable product, but to compete over the next several decades you are going to need to own valuable land in remote territories where patent wars being fought today. I'm guessing Google's meta-strategy is a type of patent-colonialism.
Even after the massive total market correction in the last few weeks, the earliest that GOOG was the same price as today is not even a full year ago. In fact, it's up 90% since 2022.
Any stock market source would tell you GOOG was ~140 USD at the start of 2022. Today it is ~170 USD. A 20% increase over three years, about the same rate as inflation and S&P.
This is extremely trivial to verify. Was this written by a GPT bot?
> > GOOG is around the same price as it was in 2022
That's the comment pb7 replied to by saying it's up 90% since 2022 (which is true, or even an underestimate, depending on where you measure from) and to which you responded calling them a bot because your own measurement, from the start of the year, gives a lower number.
cvhc is pointing out that it's the different choice of where to measure from that caused the difference in results - neither are incorrect.
My friend why so irritated... I was just explaining why you two got different numbers. And my numbers of other companies are also calculated from the start of 2022.
I guess my broken English doesn't match today's bot quality :)
does anyone know if any of the robot arms being used in these videos, especially the ones that look like just an aluminium extrusion, are off-the-shelf things that can be purchased somewhere? even if its a kit?
I would love to experiment with something like this but everytime I try to figure out what hardware to do it with there's a thousand cheap no-name options and then bam 30k+ for the pro ones.
Google is probably the most undervalued tech company there is currently, by far:
1.) Has cutting edge in house AI models (Like OpenAI, Anthropic, Grok, etc.)
2.) Has cutting edge in house AI hardware acceleration (Like Nvidia)
3.) Has (likely) cutting edge robotics (Like Boston Dynamics, Tesla, Figure)
4.) Has industry leading self driving taxis (Like Tesla wants)
5.) Has all the other stuff that Google does. (Like insert most tech companies)
The big thing that Google lacks is excitement and hype (Look at the comments for all their development showcases). They've lost their veneer, for totally understandable reasons, but that veneer is just dusty, the fundamentals of it are still top notch. They are still poised to dominate in what the current forecasted future looks like. The things that are tripping Google up are relatively easy fixes compared to something like a true tech disadvantage.
I'm not trying to shill despite how shill like this post objectively is. It's just an observation that Google has all the right players and really just needs better coaching. Something that isn't too difficult fix, and something shareholders will get eventually.
On one hand, I agree with you, on the other, as a former Googler I think that "just needs better coaching" is a huge barrier in Google's current corporate culture and environment.
Google as a whole has a long history of not being able to successfully build great products out of great tech. That seems wrong from looking at search, Gmail, Maps*, Docs*, etc., but I think these are cases where a single great insight or innovation so dominated the rest of the product qualities that it made the product successful on it's own (PageRank, AJAX, realtime collaboration). There have been so many other cases where this pattern didn't hold, and even though Google had better tech, it wasn't so much better on one axis as to pull the whole product along with it.
That's the problem I see here. Maybe they have a better model. Can they make it a better product? OpenAI and Anthropic seem to ship faster, with a clearer vision, and more innovation with features around the model. Is their AI hardware acceleration really going to be a game changer if it's only ever available in-house?
I do believe in Waymo, but only because they've been incrementally investing and improving it for 15 years. They need to do that with all products, instead of giving up when they're not an instant hit.
*Maps, Docs, and YouTube were acquired with their key advantages in place, so I wonder how much they even count.
Yeah and even Gemini only seemed to come about because OpenAI forced their hand and gave them a product vision to follow. If OpenAI didn't exist I bet Google would still be fumbling over how to make a product out of transformers.
Even OpenAI wasn’t going to release ChatGPT because the product internally was that it wasn’t that good but with some obvious internal pressure we are where we are at now.
On the topic of better management, I can't believe they haven't replaced Sundar Pichai. Satya Nadella by comparison really seemed to have turned MS around.
Larry Page was making the rounds when all of this AI hype started. He seemed to have a much more aggressive stance, even ruffling feathers about how many hours Google employees should be working to compete in AI. And there is obviously Demis Hassabis who is the most likely contender for a replacement.
I doubt it is an easy position to fill. But Pichai has presided over this lackluster Google. Even if he isn't strictly to blame, I am surprised he hasn't be replaced.
Google (Alphabet's) stock price has generally gone up 200% in the past 5 years. That is the only reason he is there, and that is the only way he is judged.
Yes, that is fair and probably the accurate assessment. A bit like Tim Cook. He may not be innovative but Apple sure has been profitable.
I guess it is easy to view it from my own perspective, one tinged with a hope for invention and innovation. But the market probably loves the financial stability Pichai has brought to the table and doesn't care about the flaws I see.
And I 'm not sure why I have rose tinted glasses for Nadella. I believe MS has been doing well financially (not something I've studied) while also supporting things I believe are valuable (e.g. VS Code, GitHub, TypeScript). Maybe I just wish I felt the same kind of balance in Google.
I just saw an interview w/ Nadella where he said straight up: Open Source takes half of every market, and this will happen with AI.
That’s such a refreshing change from the “DIE OPEN SOURCE DIE” attitude that Gates/Ballmer had.
I also love GitHub, TypeScript, and VSCode. These have become the foundation of my development toolset. That was something Gates did well, and Ballmer gave lip service to (“developers! developers!”) but for me only recently has Microsoft actually been maintaining good quality developer tools again.
That’s where my goodwill comes from anyway.
Google makes a better Office Suite (Gmail, Docs, Maps), ironically. But it’s hard for me to get too excited about that. It’s been pretty stagnant for 10 years.
Imo this is just Tim Cook’s public image. By all accounts, comparing Sundar to him is just not fair.
Just off the top of my head, under Tim Cook the company managed to:
* Propel smartwatches as a brand new product category into the mainstream and be the leader in that category.
* Propel AirPods as a brand new product category into the mainstream (and be the leader in that category as well).
* Smoothly transition to ARM (aka Apple Silicon) with great success.
* Various behind the scenes logistical/supply-chain achievements (which makes sense, as Tim Cook is the logistics/supply-chain guy by specialization).
None of those things were simple or uncontroversial. In fact, I remember the pushback people and the press had against smartwatches and airpods, calling Apple washed out and Tim Cook a bean-counter. And these are just the largest examples off the top of my head, there are definitely more. However, Google doesn’t seem to have even a singular product win of such magnitude in the past 10 years.
In the meantime, what did Google do productwise? Catching up on the cloud compute game to AWS (while nearly killing it due to their PR nightmare announcements during 2019-2020 iirc), killing their chat app that finally managed to gain enough mainstream traction (Hangouts) and then rebranding/recreating it at least twice since then, redoing their payments app multiple times (gWallet vs gPay vs whatever else there was that I forgot), etc.
I am trying to be generous here, and of course Apple had their misses too (the butterfly keyboard on 2016-2019 intel macbooks, homepod is kinda up in the air as a product category, mac pro stagnating, etc.). But I legitimately cannot think of a single consumer product that Google knocked out of the park or any that wowed me.
This sucks, because I know for a fact it has nothing to do with their engineers lacking the skill to execute on a new innovative product (as evident by Google being early to the AI/transformers era and being fundamental to what is happening with AI right now). Google has all the technical prerequisites to succeed. But the product and organizational strategies there are by far the most cartoonishly bad I’ve ever seen for such a company.
I don’t want to blame it on Sundar, because I cannot say for sure that the root of this dysfunction is at his level. I just know it is on some level between org directors and Sundar, but not where exactly. I just know that killing off a whole org working on a truly innovative AR org/product, only for most of those people to switch to Meta and continue working on an improved version of the exact same thing (the Orion glasses) wasn’t the move. And I just know that having 5+ major reorgs in one year for a single team is not normal or good.
TLDR: apologies for the long rant, but the short version is that Google under Sundar has absolutely zero sense for internal organization management or delivering products to consumers. And comparing him to Tim Cook (who has been the CEO through the AirPods/Apple Watch/ARM macbooks era) is unfair to Tim Cook and is based purely on the public image.
Because we are talking about what product wins they had. Apple Car was never officially announced, and Vision Pro is clearly their experimental/devkit sort of a product.
Vision Pro might succeed or fail, and that’s fine. I tried it, and it is clearly a significant step towards the future, but I am not sure of it becoming a successful product at its current price point and in its current state.
I am not judging CEOs or companies negatively for taking ambitious product bets and not always striking gold on those bets. I am judging them negatively for not having any product wins and not taking any ambitious product bets.
Exactly. If the founders (who still have majority voting control) or board wanted an innovator they wouldn't have picked Sundar in the first place. His job is bean counting and increasing profits, and he is doing that brilliantly.
But why does that matter to Google? They'll never need to issue more stock to raise cash; last year they had $200 billion in gross profit, money they literally didn't find a reason to spend.
Imagine being so replete with cash that after paying all your costs, all your salaries, all your R&D - you still can't find a way to spend 200 billion, so you threw a chunk of it away as tax and put the rest in the bank.
The price of a share should be utterly irrelevant to them.
Take chat, one of Google's biggest fumbles. They had a good thing with Gtalk. Really screwed things up with Hangouts (thanks, Vic!), added the weird Allo to the mix, almost turned things around, and then brought in Chat to compete with Slack as opposed to AIM...WhatsApp.
If they had just incrementally invested in chat, even if they swapped out back ends, they could have kept most of their user base, maybe even have grown it. Gchat was pretty popular, even during the rise of Facebook Messenger.
But they screwed around with the public-visible product side of things too much, and revealed their tech stack and org chart as product changes. There was no product-first, continuity-oriented planning.
The main problem with chat is that there are too many angles to communication, making it impossible to fulfil all requirements with a single tool. Apple does IM, period, they don’t want any of the Slack-type team communications and that's fine for them. Even Facebook realised that having multiple chat apps is fine as long as they offer value on their own. Meanwhile, Google has gone through several iterations, with internal groups competing for the top spot in defining what a chat app should be, but ultimately falling short because there's no single chat app for all requirements. They aimed too close to the average and failed to deliver anything useful enough for any specific group.
Or we need to break it up. The ai search team should not be afraid of killing the traditional search engine.
Many of the decisions companies make are to ensure the cow they are currently milking very efficiently does not die. This is bad for the rest of us, specially if they place barriers to innovation.
You couldn't break up the AI search engine and the traditional search engine. They're basically one and the same. The AI search engine relies on the index. The index uses AI in various places. The "traditional" side has long used AI for query understanding, ranking, and fact extraction.
Legislators don’t (and should not) care about your implementation. The old company will be banned from using ai for their search for x years the new company will get employees and assets including source code to startup the new entity.
Waymo clearly stands out as an exception amongst all moonshots that Google went after. However, they don't seem to have that one axis advantage in Waymo. I can't believe they didn't double, triple down on their efforts to build a fully integrated car. Compared to Apple, they were at a much better position to do this because of all the underlying tech/models and research.
May be that's the problem - that there is no one rallying individual for Waymo. They should just spin it off and make it an independent private company and retain % ownership.
I somehow feel Google will be way better if it's run like Berkshire, the CEO just focuses on capital allocation and let's the managers do their jobs in their respective companies - YT, Waymo, search, cloud, deepmind.
I'm not sure that culture can dissipate in Google at this juncture.
Only disagree with the last part of your footnote. YouTube was acquired with an underpants gnomes' business model: spend $$$$ on network traffic; ????; profit! The "key advantage" that enabled YouTube was dirt-cheap global networking. And I think that is the thread that ties together all of Google's products. They are the protobuf moving company, first and foremost. Even on AI one of their key advantages is the ability to reliably and rapidly start training, literally they have blogged about their cutting-edge protobuf tsunami capabilities.
This is a bad take. The business model is pretty clear: subsidized new line of business using the search revenu until it is so dominant that no competition is viable, only then heavily monetize it.
> *Maps, Docs, and YouTube were acquired with their key advantages in place.
I don’t think the same logic applies to Google Docs as it does to YouTube. The original companies behind Docs, Sheets, and Slides were practically unknown, and Google deserves credit for their evolution, features, and clear vision. Developing an office suite might be “easier” from a vision standpoint since the category already exists, whereas marketing something like Gemini Robotics is an entirely different challenge. Just my two cents.
Google needs to be broken up. The DOJ / FTC want to do it.
There's far too much value and scale in the company and they can't even focus their energies appropriately.
YouTube is the most valuable media property in the world. As a standalone company, it would still outperform Netflix on the basis of ads alone.
The monopolistic stuff Google is pulling off with Chrome/Android/Search is unfathomably market distorting, so those business units alone could/should be pulled apart. The tech sector would probably be better off if YouTube, Waymo, and GCP/AI efforts were similarly split up.
As a consumer I don't have any great desire to see it broken up. Youtube has worked well for me for years. If they spun it off it would probably get way more aggressive in trying to extract money and sell data.
Maybe, but IMO the DOJ's current proposal would be harmful for users and the web. Chrome is not worth as much to anyone else what it is to Google. And with Google barred from paying for default search engine placement, all browser investment everywhere will be severely cut back. Mozilla will probably finally fall, Safari will stagnate, and Chrome will rot.
Apple would no longer get $20 billion from Google for default search engine placement. Microsoft... and DDG or Yandex? might pay some, but nothing like that with the biggest bidder off the table. Safari funding would _definitely_ take a huge hit.
I don't think we know how much google pays for it right now. That 20B figure is from 2022. Also, that payment is mainly for iOS's Safari. Google would still pay Apple for search engine placement on iOS even if Apple stopped updating Safari today. What I'm poorly trying to say: I don't think safari development funding related to how much it brings in.
Also there is MS that wants to pay for search engine placement and it's fact.
I think you might be on to something. I heard Google Gemini has a best in class system for depicting historical figures accurately, it is extraordinarily unphased by “modern audience” political bias.
Their AI strategy is just baffling. It lacks direction and vision.
They have a thinking model way back ago, which is pretty good with clean CoT and good performance close to R1. But it never gets any marketing whatever.
Veo2 has really good performance too, yet it is so slow in its rollout now Chinese competitors are getting all attentions because it is just easier to access.
It feels to me that Google is reliving its experience with messengers where you they have multiple competing roadmaps from different parties. The execution is disoriented and slow.
They will have to catch up in 2025, the fact grok is this good in one year is a wake up call to everyone, especially Google.
If they failed to do so, Gemini is going nowhere, it already has no tractions outside of Google, nobody’s first instinct when it comes to AI is Gemini
The Transformer LLM came from Google's NLP research and input method(phone keyboard) development. Prompt processing and next word prediction is exactly what CJK keyboard software always did for past 30+ years, only datacenter sized now.
Doesn't ring a bell that very few, if any, of "AGI achieved" people seem to have backgrounds with or exposures to either classical NLP, or Google, and/or cultures that make heavy use of IME? To me the situation looked like that Googlers "have seen that trick" previously, and are doing bare minimum to defend the company from losing presence in this AGI hype storm.
I think its 2 things, but Google is big and slow but also they do not need to monetize the models like OAI. If they believe models get commoditized (Meta's plan), heavy investment is wasteful. AI summaries keeping Search strong and people using the Google bar instead of chatgpt is probably their priority.
They have Gemini and rolled out AI in Workspace and I believe they still have the most capability million token model
I don’t think it is do not need to, it is mainly they can’t at this moment. None of their LLMs are better than competitors, then it is not monetizable.
ChatGPT is already top 5 websites people visit, it is behind Google, but it will eat into its business very soon. That will happen regardless.
> Their AI strategy is just baffling. It lacks direction and vision
It's an artifact of their size -- no large corporation has vision or direction. Best they can aspire to is "stay the course". It's just something that inevitably happens as companies grow and age.
Sounds like Xerox, they had cutting edge everything in the 70s, did nothing with it. Or AT&T, with Bell Labs inventing Unix. Or Kodak inventing the portable digital camera in 1975.
I’ve seen too much inaccurate info from AI to have any trust in it. From declaring the Eiffel Tower the world’s largest Ferris wheel to claiming that hippos can be trained to perform complex medical procedures, it all seems a hot mess.
You might say, yeah, but I can spot those mistakes, but can you really? I showed my fifth-grade son the result of asking if hippos were intelligent and the absurdity of the answer didn’t leap out at him. Now, consider something that’s more subtly wrong like an invented precedent in an AI-generated legal brief or a non-existent citation or citation that doesn’t support the claim and it’s all a disaster.
If you connect ChatGPT to a traditional search engine, it will suffer much less such issues. It essentially digests 100 webpages for you, then render it in a single answer.
For sure, hallucinations will always be there, but I don't think it will hinder its take over, the usage trumps its shortcomings
Yesterday I tried asking ChatGPT "Can an Amazon L6 software engineer afford a house in [location]", without explicitly using the search mode. It went to levels.fyi to look up salary and redfin to look up housing price (exactly how I would have done it myself), and gave me a reasonable answer that agrees with my own analysis, and is definitely much faster than clicking things around myself.
I used to think that the multidirectional aspect of GPT would be a killer feature. But really it's too flaky which remove the initial alleged value. And then results are too artificial or wildly too "imaginary", even asking to compile a list of books on a medical topic you'd get half false titles. Sadly.
really? just unsubscribed OpenAI today, was one of the first to subscribe, now it lost all its edge to me, so many options elsewhere, paid or free to use.
OpenAI is fading away fast. Plus all major leaders left, Microsoft is leaving too, I don't feel its future is promising anymore.
That's fair. I would argue that OpenAI capitalized on transformer tech in a way that Google was late to do, but we shall see if Google will adapt faster than Xerox could
As anecdata, I would offer that the conversations I've had with ChatGPT over these couple of years have been incredible for me. Even just for relieving loneliness, it's been worth the monthly subscription a few times over.
Maybe the company and their business model are doomed to fail, but I'm grateful for what they enabled so far.
that's true, there are just many options these days and I think OpenAI was not keeping its team together and innovating fast enough. the first-move advantage is disappearing quickly.
I agree with you, but even though OpenAI is much lower in my esteem than Google, I would give OpenAI slightly better scores in general on productization. In the last day I have played with Gemini functionality (see https://ai.google.dev/gemini-api/docs) that I have not tried before, and I also played with OpenAI's just released openai-agents-python library. OpenAI's examples seemed a little easier to play with; that said Gemini product manager Jason Stephen reached out to me yesterday on social media in a very helpful way after I commented on Gemini's code execution sandbox.
On other similar products like Google's NotebookLLM and Open AI's GPT 4.5 Research Mode: both products are awesome.
I still maintain the reason they're playing catch-up with everyone else wrt. LLMs is because their Gemini models were not available in the EU until recently. Back when they were doing their releases, years ago, like everyone else here I took one look, saw the "not available in your country" banner, and stopped caring at all.
That's because normal people are supposed to use the Gemini chat interface, which has access to the same image generation model as ImageFX, and I'd imagine video is coming.
Google had really great products, that almost everyone I knew used, then they scrapped them for new shiny thing that competed. The one that angers me most is Google Talk, it used to work with any XMPP client, until it did not, and now its long since dead. They made their own version of tinychat (hangouts) and then mostly killed that too.
Obligatory overview of things Google has killed, because its easy to forget some of the gems:
I think if they make a product, they should support it long-term (within reason of course). Hangouts was great, for example. It could do SMS, voice and video calls, and regular web-based text chat. It was everything you need from a messaging client, all in one app. It was so close to being a real iMessage/FaceTime competitor, but instead they killed it and launched Allo/Duo instead, which was an incredibly baffling decision.
Sure it could've used a bit of a facelift and some other tweaks, but they have a history of launching new, half-baked products instead of just maintaining the existing ones.
I think GTalk being spun off into its own thing might have seen Google Talk succeed beyond whatever Hangouts became. Google Talk had a native client plus it had native clients that supported its protocol.
I even messaged from my GTalk to my Facebook as a test, which worked because both were Jabber. Both companies closed both services off to anyone else. Sadly.
I disagree. As a former googler, that company has never had a problem creating IP.
It has a problem executing on that tech to create great products. It has a real problem with canning any project that doesn't have a billion users within a year.
Honestly they fail to understand how lucky they got with doubleclick and culturally the entire project evaluation criteria is based around the assumption that they can do another computer science rain dance to make it rain ads-level cash.
This is interesting because I think the opposite. What is amazing about Google seems to be their incredible ability to squander their lead in absolutely every area.
Maps used to be the absolute best and now I frequently get baffling driving directions in a US major metro area. No improvements within the last 10 years. New pixel phones are worse than latest Samsung. Some huge lead in AI absolutely totaled, their investment in anthropic their only hope. Inference HW accelerators that noone uses.
They are becoming like M$ - I expect M$ to be this terrible at product development - but at least M$ is fantastic at making money despite terrible products.
Google has allowed search experience to slide so much people would rather use some slow-ass unreliable chatbot. Are they really losing the war on SEO or have they decided that the internet-of-shit (i.e. affiliate marketing) is more valuable?
Maps are still pretty good. I use street view and the reviews and opening hours a lot. Which competitor can I use for that? I think the driving directions may have got messed up by merging with Waze.
Yes, the reviews are still very good, but I think this is more due to users (as a 6-point local guide myself) and less due to google.
I am seeing bot-generated reviews more and more often, and when I look at what happened to search, I don't have a lot of faith in google to do a better job with maps. But I sure hope they do, because I'm with you - I really do rely on maps reviews.
Google make almost all their money from search, an extremely lucrative property, which is under threat from all the new ai players.
So while they have a bunch of cool tech on the possibility horizon the only thing the market cares about is the ability to make money and there's some uncertainty on that front.
My experience there was that good tech was held back by an inability to have a consistent long term vision. My and many of my friends were on lots of projects that would get abruptly "reprioritized", often after yet another re-org. I'm not knowledgeable enough to know what the solution is but it didn't give me confidence in their ability to execute on a long term vision, it was very demoralizing and my work ended up feeling sorta pointless (which having talked to someone recently about the state of the projects I worked on it sorta was pointless). Though that being said it's a big company so it's very possible that other orgs will execute more effectively.
I think all those are basically true, but I still don’t see them actually dominating any space besides their “gross monopolist” categories - ads and their dominant Chrome and Android that enhances those ads. In everything else (look at GCP) they’re performing worse than their products merit.
I think what keeps Google up at night is knowing that their ads business which pays all of the bills could be upended by regulation or by disruptive consumer AI of some kind and they’d then have approximately nothing in terms of revenue.
It hired a project manager to be the CEO, who has zero charisma comparing to other big companies(Tesla, Nvidia, Microsoft, OpenAI,Oracle,AMD, Apple, etc), that made the company "boring"
Agreed. Google isn't aggressive enough to productize many of their ideas and their existing products feel like they're developed by N different companies with no unified experience.
They have due to circumstances a different business model to OpenAI, Claude, Grok etc.
Open-Claude-Grok: "our AI is so cool, AGI next year" but we are losing money so invest in us at a $crazy bn valuation
Google: We are swimming in money from ads so no need to hype anything. If anything saying we will dominate AI as well as search, email, video, ads, browsers, phones etc would just get us broken up. So advance quietly.
Agree. A majority of people on HN are in a startup mood so they feel a company should market aggressively to attract investments and expand. But I don't think Google would achieve more than marginal gain were they to aggressively make Gemini/Imagen/Veo available to Search/YouTube/Workspace users, and the cost could be terribly high.
Gemini has been one of the most cost-efficient models. Probably this is exactly what Google needs for productization.
And I think this is the problem. They have all the necessary pieces and are not yet very successful at stitching them together. Google has a very strong foundation and execution skills but failed to effectively govern it.
Not sure if this is "vision" or "management" or whatever, it feels like that they're just self shackling in every single possible direction. There are something like 50 different teams involved at a major launch and they make some process/infra requirement/review/integration or whatever, from good will. Imagine how much time, effort and compromises you would need to appease all of them.
I think the recent memo from Sergey shows that the leadership finally acknowledges this problem at heart. But solving it is a different story of course. But a long time disconnection between IC, managements and leadership has been the culprit of this problem and at least some awareness might not hurt.
I think they hired the wrong people for too long. At least this is my impression. (no, I did not apply).
Based on P/E the US stock market is overvalued. So I would be careful with "undervaluation". Most undervalued tech stocks are probably in China.
Google also lost a lot to LLM. I use perplexity now 50% of the time, where I would have used Google. I also read a lot of "degoogeling" and "going off Amazon". My impressions of both companies are not the best. I have a gmail account I never got access back, even with the right password. And Amazon defrauded me of 40 USD. Claimed in a chat that they would reimburse express shipment after they f. up but then did not and called it a "misunderstanding".
I have somewhere list of the most valuable companies. And it changed every decade. So, past performance is not a guarantee for future performance :-)
The Chinese stock market is still a crapshoot, so even if stocks are undervalued, if you don't have inside information you can't make much money beyond trying to ride the waves of those who do. So the undervaluing makes sense to a degree (the stock market can't operate very efficiently).
Chinese tech ETFs haven’t done very well, basically as anemic as emerging market funds are now (which are heavy into Chinese tech companies anyways). You aren’t making money with these right now, which might mean they are undervalued, but they seem to go boom the bust too quickly to be long term holds.
Well, they are of performing well, this means they might be bad or they might be undervalued. It was asked, what is undervalued? Not what did perform well (and might be overvalued).
Bust is unlikely with an ETF. They rebalance without you having to do anything. Most tech might come from China in the future.
Names stick, it’s as simple as that. In most practical situations (such as this discussion), the distinction between Google and Alphabet doesn’t matter.
I once tried to rebrand an in-house, purely dev facing product. I failed.
First of all if you are going to talk about valuation then that should be included here. And Google has always been terrible at developing and managing products. The list is too long to begin writing down. One funny example is the Pixel. I had a meeting with a slew of Google managers regarding mobile strategy (maps, reservations) and every single one of them had an iPhone. I doubt any of them ever even tried a Pixel. Same with the dozens (hundreds?) of software products that have died off or languished over the past 20 years.
Google is an Engineering company. What they're really bad at, in my opinion, is productizing their technology.
Google Cloud is decent, again in my opinion, because they can more or less copy the product vision from AWS and focus on the technical excellence.
When were you last excited to use a Google product or service?
Part of the problem is also their internal incentives that lead to lot's of products being retired waay too soon, leaving behind a lot of users and hurting their reputation a lot.
I think OP is suggesting that because Alphabet purchased Boston Dynamics in 2013, and then sold in 2017, that they were able to take their learnings from the acquisition and integrate it in-house, but haven't shown the world the extent of their capabilities. Potentially supported by the Gemini Robotics announcement highlighting extremely dexterous robots.
It's somewhat debatable based on lack of results that have made it to market.
In addition to the other comment mentioning Boston Dynamics, they are also the employers of a lot of folks that were formerly at the Open Source Robotics Foundation(?) (OSRF) (it's more complicated than that) which is behind the ROS1/ROS2 framework that are widely (not universally) used; They also have an internal division or whatever, Intrinsic Robotics (or is it Intrinsic AI? too lazy to check). Plenty of smart people that I've met are involved there!
But I remain skeptical of the top level comment's take, given the lack of any robotics product execution of note by Google for a very long time now.
This is all true, but at the end of the day, the shareholders care about return on value, and they get that from selling ads. All this amazing tech doesn't generate any revenue.
People said the same thing about Bell Labs and they were profoundly wrong.
There is nuance. Saying A about B and being wrong does not imply saying A about C means you're wrong. It is indeed possible to lose focus on revenue and die. But it is also possible to focus too much on revenue and die. It is unclear if Google will achieve anything from it's "pure research" investments, but certainly they have room to try, and I personally am glad they are doing so.
> People said the same thing about Bell Labs and they were profoundly wrong.
They were profoundly wrong, but not about Bell Labs' ability to create value from their research. That, they were absolutely dead-on about. AT&T and Bell Labs were absolutely awful at reading the room about what their technology could do and how it could be monetized.
Some of that was just packaging things the right way, and some of it - like charging absolutely insane license fees for UNIX in the 80s and 90s during the beginnings of the personal computing revolution - was because of lazy execs who didn't want to really put in any effort. Either way, I'm not using a Bell Labs LabsBook Pro to write code for a UNIX OS, and I'm not using Bellgle to search for information. AT&T ultimately thought the best way to create value from Bell Labs was to sell that division.
We're in a long, hot AI summer, but we've had winters too. Who knows which hemisphere they're in at Google right now.
Google had a revenue of $348 billion in 2024. For a new product to generate 1% more revenue, it needs to generate $3.48 billion annually.
Even extraordinary products are rarely going to do that. Their AI products could be a huge success, and still not significantly change how valuable the company is.
Following up on the Factorio metaphor from the other thread, the bigger your factory is, the more difficult it is to change it to get to the next organization level needed for long-term success.
Confusing "having the tech" with "having product-market fit" is huge here. If the company was so undervalued they wouldn't try juicing their search profits at the cost of enshitiffying their product.
> Google lacks is excitement and hype
People(me) used to look up to Google and the projects they had. 80/20 work/project time, moonshot projects, all the google perks, etc. It felt like the place to be. Fast forward 10 years, I just want antitrust to shatter it into smithereens.
> that veneer is just dusty
The problem is systematic, affecting the whole org from top to bottom and especially the top. They either get a new CEO that turns things around or become another IBM.
And yet I worked on a Google X robotics project which was later canceled and doesn’t even appear in this announcement despite those machines notionally going to Google brain for research purposes. They have a very hard time capturing value with any innovations that aren’t ads.
Sounds like Xerox. They have everything, some employees will become multi-billionaires within 10 yrs after they leave the company and create their own startup. But I have zero conviction this corporate moloch will be the one to productize any of it.
Google has massive technological assets, but as an organization it has shown repeatedly that it is completely unable to leverage pretty much any of it as a viable business.
On the tech side they are excellent, but on the management/business/corporate culture side they have repeatedly proven that they are much less competent than pretty much everyone else.
Fortunately for them, they have a very prolific cow to milk with their ads business, and that's where they get their valuation from, but there tech is legitimately undervalued because they have repeatedly shown that they don't know how to convert that into business.
Your assumptions are actually not correct. They are behind in many AI areas. Their LLM models for example are not in the same level as the frontier models. The main reason Flash 2.0 is so popular is that it's good enough for most things and is 30 times cheaper from Sonnet 3.7 for example.
They definitely have pricing power and also a large stake in Anthropic, so I'm not worried about them.
This is the biggest cloud looming over Google right now, for sure. The stock will have a lot of interested buyers the moment this issue is resolved and evaluated.
>Google is probably the most undervalued tech company there is currently, by far: [reasons]
The only thing you left out of this analysis is their valuation. The market values Google at $2.05T (just over $2,000,000,000,000) which is 21 times their earnings (net profit). They are valued at $250 per person on Earth while selling, annually, $43.75 per person on Earth (sales) of which $12 per person is their profit.
How much would you pay to own a golden goose laying $12 in gold per year? Like, $250? If so you are the proud buyer of Google right now. (There is a buyer on every sale of every stock and this is the price they are paying right now.)
An alternative viewpoint is the consideration of the P/E of all of the Mag 7. These numbers might be slightly off since there's been a lot of market movement lately, but...
Apple (AAPL): 34.07
Microsoft (MSFT): 35.07
Amazon (AMZN): 36.69
Alphabet (GOOGL): 21.82
Meta Platforms (META): 24.49
Nvidia (NVDA): 41.33
Tesla (TSLA): 87.87
from this perspective Google, and to a lesser extent Meta, stand out as being valued quite conservatively.
Do I think Microsoft is performing 50% better than Google? Not really, no.
If the goose is likely to live for significantly longer than 20 years and has potential to lay $15 or $20 in the future then yes I'd probably buy that goose for $250. Of course there's risk with it (eg. Google might significantly lose business to competitors) but that's why you diversify. A PE of 20 for a mature company like Google isn't crazy. Even Coca Cola has a high PE at 28.
Google also has a shit reputation for privacy, a terrible (or worse) reputation for customer safety or resolution of issues, and all of that on top of psychopathic executives.
All of the technology in the world doesn't make up for that.
Imagine working there : you could create the best thing you always dreamed of... but you know they will cover it in ads and violate every ones privacy, and sell it to isreal to kill, then use your hard work to create an AI to replace you and fire you.
> The physical safety of robots and the people around them is a longstanding, foundational concern in the science of robotics. That's why roboticists have classic safety measures such as avoiding collisions, limiting the magnitude of contact forces, and ensuring the dynamic stability of mobile robots.
Uhhh, I mean that's nice, but how about: "That's why we will never sell our products to military, police, or other openly violent groups, and will ensure that our robots will always respond instantly to commands like, 'stop, you're hurting me', which they understand in every documented human language on earth, and with which they will comply regardless of who gave the previous command that caused violent behavior."
Who is building the robot cohort that is immune - down to the firmware level - to state coercion and military industry influence?
I read too much scifi, and almost none of it has updated on the current state of AI. For example spaceships swarming with low skill level crew members that swab the decks and replace air filters. Or depending on a single engineer to be the only one with the crucial knowledge to save the ship in an emergency.
If scifi authors aren't keeping up it's hard to expect the rest of us to. But the macro and micro economic changes implied by this technology are huge. Very little of our daily lives will be undisrupted when it propagates and saturates the culture, even with no further fundamental advances.
Can anyone recommend scifi that makes plausible projections around this tech?
> For example spaceships swarming with low skill level crew members that swab the decks and replace air filters.
This is largely a function of what science fiction you read. Military SF is basically about retelling Horatio Hornblower stories in space, and it has never been seriously grounded in science. This isn't a criticism, exactly.
But if you look at, say, the award-winning science fiction of the 90s, for example you have A Fire Upon the Deep, the stories that were republished as Accelerando, the Culture novels, etc. All of these stories assume major improvements in AI and most of them involve breakneck rates of technological change.
But these stories have become less popular, because the authors generally thought through the implications of (for a example) AI that was sufficiently capable to maintain a starship. And the obvious conclusion is, why would AI stop at sweeping the corridors? Why not pilot the ship? Why not build the ships and give them orders? Why do people assume that technological progress conveniently stops right about the time the robots can mop the decks? Why doesn't that technology surpass and obsolete the humans entirely?
It turns that out that humans mostly want to read stories about other humans. Which is where many of the better SF authors have been focusing for a while now.
This reminds me of my favorite note [1] from Ursula Le Guin on technology:
> Its technology is how a society copes with physical reality: how people get and keep and cook food, how they clothe themselves, what their power sources are (animal? human? water? wind? electricity? other?) what they build with and what they build, their medicine — and so on and on. Perhaps very ethereal people aren’t interested in these mundane, bodily matters, but I’m fascinated by them, and I think most of my readers are too.
> Technology is the active human interface with the material world.
While it doesn’t touch on AI at all (that I remember, I think there is some basic ship AI but it’s not a major plot point and it never “talks”) the Honor Harrington series is “Horatio Hornblower in space” and I highly recommend it.
Also I love the Zones of Thought series and The Culture.
Yeah that tracks. If we're being real, there won't ever be much of actual human exploration beyond Earth, it'll all be done with fully automated systems. We're just not physically made for the radiation and extremely long periods of idle downtime. Star Wars has the self-awareness to call itself fantasy as some kind of exception, even though 99% of all other other sci-fi is pretty much that too.
Seeing drones do all the work unfortunately isn't very interesting though.
Vernor Vinge has argued that far-future SF makes no sense because of the "wall across the future" that The Coming Technological Singularity will create. [0]
If you're open to Theory Fiction, you can read Nick Land. Even his early 1990s texts still feel futuristic. I think his views on the autonomization of AI, capital, and robots - and their convergence - are very interesting. [1]
In The Mountain in the Sea by Ray Nayler there are fleets of fishing boats that are all controlled by AI to maximize the catch. Each boat also has it's own AI that can act somewhat independently, but they all communicate with the main corporate AI as well as with other boats in the vicinity. Initially the boats are all fully automated and have robots doing all the work, but in the ocean environment the robots tend to break down a lot due to corrosion. At some point the AI in charge of the fleet figures out that it can use kidnapped humans in place of the robots. The humans are kidnapped and drugged so that they don't wake up until the ship is well out at sea. Even after that they're kept drugged to some extent so that they aren't inclined to escape. They're given just enough food to enable them to do their work and no more. When they become sick they're thrown overboard and new kidnappees replace them.
This is just one of the side plots of the book, I think it could've been the whole plot of a book.
Oooh edgyyyyyy comment! Truly you are awake and the rest of us are asleep.
Tell me, which corporation exactly is kidnapping and drugging people to enslave them and then discard their bodies at sea to feed the capitalist global machine?
It seems like you have a big scoop if you are doing on the ground reporting, because that seems like it would be international news if it was real!
Actually, as bizarre as it sounds, drugging and kidnapping people to enslave them on fishing boats is a real problem, and has been reported on by the international news.
The sun is dying. A capable team is assembled and put into cryosleep in an automated ship for a journey to a neighboring star system to try to diagnose the problem. Only one member survives, and they have amnesia.
The novel does a great job of explaining the process of troubleshooting under pressure and with incomplete information.
Strong warning: Start with either book 2 (Player of Games) or book...7, Look to Windward.
I strongly suggest you skip book 1 until you're comfortable with the rest of the books that focus on the Culture itself, and not some weird offshoot story that barely involves the Culture.
I also thought about the Minds in the Culture novels. That universe has many gradations of artificial brains.
Though I wouldn't recommend starting with any of the stories in the series. Or reading any at all. Find a summary or a Cliff's Notes instead. Iain M Banks has a talent for making great stories tedious.
Strongly recommend murderbot diaries (starts with "All systems red").
Has a cyborg/AI as protagonist and paints a really interesting world with AIs and synthetic biology in it. Also does a good job at just shutting up about things it can not talk about, like interplanetary travel.
It's not that they don't keep up, and more that it's hard to make a truly compelling and exciting space opera story it you abide by the reality of physics. The reality of space travel and war will be much closer to the forever war than to the countless water navy inspired stories out there.
Yeah it really makes you think about what life would be like if intelligence could infuse anything- be it a ship or a datapad- even if his vision wasn't quite how I imagine it would turn out.
I've also seen it suggested that Harry Potter might be a more realistic look at what proliferated AI might be like.
It does such a good job building a convincing world, and its really good in just not going into details that it can't speak on (like how interplanetary travel works), while some of it's takes (e.g. small anti-personel drones) seem almost prescient after Ukraine.
All the synthetic biology and even the depictions of AIs and their struggles are really compelling, too.
Nah, stories for robots by robots would probably be more like "can we gently and patiently explain to humans that all their problems come from their own lack of understanding without them turning on us"
"The Hitchhiker's Guide to the Galaxy" by Douglas Adams does a great job at being a timeless and priceless way to learn about the relativity of things.
I'm surprised that nobody has mentioned Blindsight. I don't think it's a spoiler to say that it is a book about the place of human intelligence in a universe with other options, both biological and artificial.
I found Vernor Vinge is spot on. I recommend focusing on recent work. E.g. the Bobiverse (https://www.goodreads.com/series/192752-bobiverse) by Denis E. Taylor is a super easy read that touches on that. He takes a shortcut in early books by capping the progress in US via turning the country into a theocracy and then a bad WWIII that wiped out most of the mankind. Note that I haven't read the latest books, but even the previous ones are full of automation and humans are "ephemerals" - they don't live long.
I am recently reading Adrian Tchaikovsky's Children of time series. It goes beyond AI and mind uploading, expanding into biotech, the next big deal. With the right understanding of proteins and DNA/RNA, hacking living things is way easier than creating robots, as they self-repair, replicate, feed themselves, recycle things effectively, create ecosystems. The only reason we are not doing it is because our understanding of these mechanisms is very shallow.
Dan Simmons' books often include AI plot elements and contemplate the consequences of humans becoming overly reliant on AI such that they lose basic competencies.
No matter how well documented system us, how helpful error message is or how good self diagnostics are - some humans will act dumb. Access to knowledge (I assume by embedded you mean better access) is clearly not enough.
A bit of a jump but have a look at Pantheon the tv series, it is on Netflix at the moment. Based on a book by Ken Liu, the end of the series, blew my mind.
I'm guessing it'll only be a matter of time before we see more stories about AI. For example, a spaceship that crashes into strange planets killing the humans on board because AI hallucinated, resulting in a civilization of aliens built around the combined wisdom of every youtube comment and facebook post that the surviving AI was trained on creating the largest and most destructive religion/dumpster fire in the universe.
It's pretty normal for it to take a few years to write a good book so I wouldn't look to science fiction to keep up to date on the latest tech hype train. This is probably a good thing because when the hype dies down or the bubble bursts, those books would often end up looking very dated and laughably naive.
There's a lot of books about AGI already which is probably more fun to write about than what passes for AI right now. Still, I'm sure that eventually we'll see characters getting their email badly summarized in fiction too.
One of my previous coworkers put it best: the cool looking proof of concept or prototype is 10% of the effort, and getting something that works in the real world and that people actually want is the other 100%.
If we see a real world application that a business actually uses, or that people want to use, that's great. But why announce the prototype with the lab demos? It's premature. Better to wait until you have a good real life working use case to brag about.
> But why announce the prototype with the lab demo
Because that's how you attract the media attention, talent, and financing you need to both go from prototype for product, and to have a market ready for the product when its ready.
Especially when other people are already publicly known to be working in the domain.
Lol, you need to drive up hype and convince investors you are not falling behind. Not even being cynical here, I think it's a good idea from a business perspective.
I don't understand the negativity here. We have made enormous progress both in language models and in reinforcement learning for robotics. Is it really hard to believe that putting it all together like Google is apparently doing, is possible?
Gemini was a solution looking for a problem. And whilst doing so, to keep up with the Joneses, they kept stepping in it along the way. To me, it seems Gemini is another service thats going to fall by the wayside.
Had they focused more on driving innovation and not profit/being relevant, they could have had another win instead of another Google+
Instead, we got African-German Nazi's.
Promotion of Indian/Indian American CEOs, in established companies, after the founders have cashed in, is proof of shareholders value maximizer having won control of the firm. Their main contribution being offshoring, not just the labor, but the culture as well.
Google was already an advertising monopoly by the time this happened and his job is to sell ads and minimize costs...the rest of Google is just there for marketing & public relations
Turns out he was just writing LLM prompts way ahead of his time.
I don’t think that’s the main problem, there are a lot of moral dilemmas where even humans can’t agree what’s right.
for some reason that one wasn't even included in the list of books in the series on the inside jacket of the other books that I had.
I remember I had to really hunt for it and it was from a different publisher. never knew why.
Or so says Ted Chiang: https://en.m.wikipedia.org/wiki/The_Lifecycle_of_Software_Ob...
And now I wouldn’t even trust them to understand the laws 100% of the time.
I recall that story of the guy who tried to use AI to recreate his dead friend as a microwave and it tried to kill him[0].
You couldn't sell a sci-fi story where AIs just randomly go insane sometimes and everyone just accepts it as a cost of doing business, and because "humans are worse," but that's essentially reality. At least not as anything but a dark satire that people would accuse of being a bit much.
[0]https://thenextweb.com/news/ai-ressurects-imaginary-friend-a...
Sorting garbage is a terrible job for humans, but it's a terrible one for robots too. Those fancy mechanical actuators etc are not going to stand up well to garbage that's regularly saturated with liquids, oil, grease, vomit, feces, dead animals, etc.
[1] https://loe.org/shows/segments.html?programID=96-P13-00022&s...
An example: A friend worked on accurate built-in weighing machines for trucks, which could measure axle weight and load balance to meet compliance for bridges and other purposes. He found it almost impossible to make units which could withstand the torrents of chemical and biological wet materials which regularly leak into a truck. You would think "potting" electronics understands this problem but even that turns out to have severe limits. It's just hard to find materials which function well subjected to a range of chemicals. Stuff which is flexible is especially prone to risks here: the way you make things flex is to use softeners, which in turn make the material for other reasons have other properties like porosity, or being subject to attack by some combinations of acid and alkalai.
These units had NO MOVING PARTS because they were force tranducers. They still routinely failed in service.
Rubbish includes bleaches, acids, complex organics, grease, petrochemicals, waxes, catalyst materials, electricity, reactive surfaces, abrasives, sharp edges..
They are not saying "dont try" they are saying "don't be surprised if it doesn't work at scale, over time"
Humans can generally stand this without an issue.
In fact you wouldn’t replace a lot of jobs that involves this : doctors, nurses, emergency workers, caregivers…
It just happens to be difficult. But people love doing difficult things as long as it’s : a) rewarding, b) respected, and c) sufficiently paid
Why's everything gotta have arms and graspers it's so inefficient.
Robots aren't climbing trees or chasing food. They don't need tails, either.
We have designed a lot of processes and workplaces around the assumption that the 'machine' working there will be be around 160-190 cm tall, with two arms with graspers on the end and equipped with stereo colour vision cameras. The closer you make your new machine match that spec the less changes you have to make to your current setup. It also makes it easier to partially swap in robots over time, rather than ripping everything out and building something completely new.
Having worked at a company close to this field, the real answer through is that both approaches are being done right now. People building new facilities from scratch are building entirely automated system where the 'robot' is the whole machine. People with existing facilities are more interested in finding ways to add robots to their current workflow with minimal changes.
You can just send explosives into both those things, and it's cheaper and more effective.
Just had cameras, visual detection, some compressed air nozzles, and millisecond (nanosecond?) reaction time to separate the non-recyclable materials.
The mapping between sensor signals and material types is usually hardcoded from laboratory test results.
For long time the term "artificial intelligence" has just gone out of favour, but I do remember the days where a good AI research lab had a bunch of symbolics lisp machines
Laser interferometry and DCT image distance, primarily.
The Gemini robot tech is cool as heck, don't get me wrong, but it doesn't seem particularly well suited to industrial automation.
https://www.youtube.com/watch?v=nUrBBBs7yzQ
1. https://www.pbs.org/wgbh/frontline/documentary/plastic-wars/
This is because if it becomes easy then it won’t matter and all the marketing, non profit orgs and everything goes away, making it a non problem.
While I am sure you will find people who will like these ideas and want them, they will have zero control.
At this point recycling is a marketing thing. And it’s more important that people think about the cause than solve the problem.
Get everyone to dump their crap into one pile and actually invest in industrial processes to sort the crap out.
Huge con: this is a complex problem with possibly poisonous/explosive ramifications if it goes wrong.
Huge Pro: If we can solve this issue, that is a society changing capability, forevermore.
Or until armageddon/robot overlords/singularity/zombie plague at least.
We are asked to do hundreds of little things that mildly inconvenience us in order to maintain some social contract. Sure they could be made easier/nonexistent with better technology, but I:
1) don't see why asking people to do their part is silly
2) don't see why this particular problem would be more frustrating than e.g. the others I've mentioned. I feel like they are all similar on the "effort" scale.
Although I guess I'd admit that asking people to sort recycling properly is very different than relying on them to.
If 10% of people don't put their cart away, then 90% of carts still get put away. If 10% put things into the recycling bin that shouldn't go there, then 100% of that batch of material becomes unsuitable for recycling process unless expensive remediation is done first.
But I don't think this is a good system of caring for our environment. If we cared properly, rather than half-arsing it we'd have a proper industrial system with known outputs that we could improve upon. Instead we seem to have a "feel good you did your part, now forget about it" process. I guess it is shambling it's way to something more but it doesn't seem like it's in a rush - kinda the same way the world agrees on acting on climate change but no one is in a rush.
That’s about 25% by weight of all that gets recycled in the country.
Metals, industrial scrap, and other sources are 75% of what gets recycled in the US.
We are blue collar businesses, with high labor costs. Many are exploring robotics actively for repetitive tasks. We have some robots in our process, looking for more when the ROI makes sense.
It may not be 100x, but there will be value in robots in recycling.
[1](https://en.wikipedia.org/wiki/PET_bottle_recycling)
If plastic recycling was actually being done and was profitable I don't think there'd be a Pacific garbage patch and pfas in my heart right now.
# The Reality of Plastic Recycling:
- Low recycling rates: Only 9% of all plastic worldwide is actually recycled[1][2]. In the United States, the recycling rate for plastic waste is even lower, at just 5-6%[5].
- Limited recyclability: Most types of single-use plastic cannot be recycled in the United States. Only plastic #1 and #2 bottles and jugs meet the minimum legal standard to be labeled recyclable[1].
- Downcycling: The majority of recycled plastic is of inferior quality, resulting in downcycling rather than true recycling[2].
- Economic challenges: Recycling plastic is often not economically viable compared to producing new plastic[4].
# Industry Deception:
The myth of plastic recycling has been perpetuated by the plastic and oil industries for decades:
- Misleading labeling: The Resin Identification Codes (RICs) on plastic products were created by the industry to give the impression of a vast and viable recycling system[3].
- Disinformation campaigns: The fossil fuel industry has benefited financially from promoting the idea that plastic could be recycled, despite knowing since 1974 that it was not economically viable for most plastics[3].
- Lack of commitment: In 1994, an Exxon chemical executive stated, "We are committed to the activities, but not committed to the results," regarding industry support for plastics recycling[5].
#Environmental and Health Impacts
- Pollution: Most plastic items labeled as recyclable often end up in landfills, incinerators, or polluting the environment[1].
- Health hazards: Plastic waste contamination affects soil, water, and air quality, potentially impacting human health[4].
Conclusion
The concept of widespread plastic recycling is largely a myth propagated by the plastic industry to distract from the real issues of plastic pollution and to avoid regulation. While some plastic can be recycled, the current system is far from effective or sustainable. To address the plastic crisis, focus needs to shift from recycling to reducing plastic production and consumption.
[1] https://www.greenpeace.org/usa/the-myth-of-single-use-plasti...
[2] https://www.plasticsoupfoundation.org/nl/blog/recycling-myth
[3] https://www.earthday.org/plastic-recycling-is-a-lie/
[4] https://kosmorebi.com/en/plastique-le-mythe-du-recyclage/
[5] https://www.pbs.org/newshour/show/the-plastic-industry-knowi...
(Also there seems to be some kind of video auto-play/pause/scroll thing going on with the page? Whatever it is, it's broken.)
The trick was in that the belt was too tight for an average human to put on with brute force, and disabling the tensioner or using tricks would require better than average mechanical skills their specially chosen 'random humans' lacked.
Reference video (saw your clip is robot-only, but the robot vs human video is more telling):
https://youtu.be/x-exzZ-CIUw?feature=shared&t=65
https://techcrunch.com/2023/12/07/googles-best-gemini-demo-w...
edit: it didn't.
Don't need the robot to smash a grape when we can use a fake grape that won't smash.
This video reeks of the same shenanigans as perpetual motion machine videos.
It's an impressive demo but perhaps you are misremembering Jarvis from Iron Man which is not only far faster but is effectively a full AGI system even at that point.
Sorry if this feels pedantic, perhaps it is. But it seems like an analogy that invites pedantry from fans of that movie.
Time to think bigger.
I want to strap robot arms to paralyzed people so they could walk around, pick up stuff, and climb buildings with them.
Doc Oc style.
Isn't programming just text anyway ?
Ehh, no need - just let the LLM figure out what to build in your garage.
Well in developing countries you can hire people to do house chores.
Search is in real danger of mostly obsolescence. Ads aren't safe.
It's not just researchers. Engineers at Google get to spin up products and throw spaghetti at walls, too. Google has more money than God to throw around.
Google's ad dominance will probably never go away unless antitrust action by the FTC/DOJ/EU force a breakup. So they'll continue to lead as long as they can subsidize and outspend everyone else. Their investments compound and give an almost unassailable moat into deep tech problems.
Google might win AI and robotics and transportation and media and search 2.0. They'll own everything.
With a reasonable degree of success. In their last quarter (see https://abc.xyz/investor/earnings/) 25% of their revenue was non-ads, and that percentage has been consistently increasing.
That's just one of their many business units.
chatgpt has good chance to kill google search -> kill google.
This might have been true 10-15 years ago. I assure you it is not the case today.
We're the crab.
Can these spatial reasoning and end-effector tasks be reliably repeated or are we just looking at the robotic equivalent of "trick-shots" where the success percentile is in the single digits?
I'd say Okura and Vinci are the current leaders in multi-axis multi-arm end-effectors and they have nothing like this.
I assume Google is being very careful to keep the speeds well below the “oops, it took your jaw off” threshold.
Well, for now, at least.
I know who will be the first shown the door when the next round of layoffs comes: the guy saying "you can't make money that way."
When I see open roles at these companies I think the projects I'm going to work on in the future will be more and more irrelevant to society as a whole.
Anyway, this is amazing. Please delete/remove my post if it seems like this adds nothing to the conversation
The man behind the curtain here has an army of engineers, unlimited cloud nodes and basically has harvested all the data currently available in the world.
It doesn't get any better than this right now.
What's next? They'll ping you later on Linked-in with this awesome idea that you need to make sure runs in a $1 USD microcontroller with a rechargeable battery that is supposed to last at least all day.
The actual scary stuff is the dilution of expertise, we contributed for a long time to share our knowledge for internet points (stack overflow, open source projects, etc), and it has been harvested by the AIs already, anyone that pays access to these services for tens of dollars a month can bootstrap really quickly and do what it might had needed years of expertise before.
It will dilute little by little our current service value, but you know what, it has always been like this forever, it is just faster.
In the meantime, learn to automate the automator, that's the way to get ahead.
Everyone has to collect their own data and pool it together or else there won't be any progress.
And it won't ever get any worse.
These were developed without the big bucks, so the tech has improved for smaller players at least.
But yes, in general, models won't get worse than they are now (or if they do, they won't stay that way.) At Google, search has been enshittified for business reasons, not technical ones.
What scares me more is the opposite of that: information scarcity leading to less accessible intelligence on newer topics.
I’ve completely stopped posting on Reddit since the API changes, and I was extremely prolific before[1] because I genuinely love writing about random things that interest me. I know I’m not the only one: anecdotally, the overall quality of content on Reddit has nosedived since the change and while there doesn’t seem to be a drop in traffic or activity, data seems to indicate that the vast majority of activity these days is disposable meme content[2]. This seems to be because they’re attempting desperately to stick recommendation algorithms everywhere they can, which are heavily weighted toward disposable content since people view more of it. So even if there were just as many long discussion posts like before, they’re not getting surfaced nearly as often. And discussion quality when it does happen has noticeably dipped as well: the Severance subreddit has regularly gotten posts and comments where people question things that have already been fully explained in the series itself (not like subtext kind of things, like “a character looked at the camera and blatantly said that in the episode you’re talking about having just watched” things). Those would have been heavily downvoted years ago, now they’re the norm.
But if LLMs learn from the in-depth posting that used to be prominent across the Internet, and that kind of in-depth posting is no longer present, a new problem presents itself. If, let’s say, a new framework releases tomorrow and becomes the next big thing, where is ChatGPT going to learn how that framework works? Most new products and platforms seem to centralize their discussion on Discord, and that’s not being fed into any LLMs that I’m aware of. Reddit post quality has nosedived. Stack Overflow keeps trying to replace different parts of its Q&A system with weird variants of AI because “it’s what visitors expect to see these days.” So we’re left with whatever documentation is available on the open Internet, and a few mediocre-quality forum posts and Reddit threads.
An LLM might be able to pull together some meaning out of that data combined with the existing data it has. But what about the framework after that? And the language after that? There’s less and less information available each time.
“Model collapse” doesn’t seem to have panned out: as long as you have external human raters, you can use AI-generated information in training. (IIRC the original model collapse discussions were the result of AI attempting to rate AI generated content and then feed right back in; that obviously didn’t work since the rater models aren’t typically any better than the generator models.) But what if the “data wells” dry up eventually? They can kick the can down the road for a while with existing data (for example LLMs can relate the quirks of new languages to the quirks of existing languages, or text to image models can learn about characters from newer media by using what it already knows about how similar characters look as a baseline), but eventually quality will start to deteriorate without new high-quality data inputs.
What are they gonna do then when all the discussion boards where that data would originate are either gone or optimized into algorithmic metric farms like all the other social media sites?
[1]: https://old.reddit.com/user/Nathan2055
[2]: I can’t find it now, but there was an analysis about six months ago that showed that since the change a significant majority of the most popular posts in a given month seem to originate from /r/MadeMeSmile. Prior to the API change, this was spread over an enormous number of subreddits (albeit with a significant presence by the “defaults” just due to comparative subscriber counts). While I think the subreddit distribution has gotten better since then, it’s still mostly passive meme posts that hit the site-wide top pages since the switchover, which is indicative of broader trends.
I'm just scared about a future where humans (say the next generation, kids 1-5 years of age right now) lack in-depth knowledge of almost everything and it's mostly AI writing low-level code, so there are no more "human experts."
We've already seen this happening where Gen Z mostly interacts with the world using phones and struggle with older operating systems/desktops, just like older generations. AI is going to make that 10x worse going forward.
In 10 years, no trace of our current practices will remain in a recognizable form. It'll take longer than most of us think -- imagine how nonplussed Winograd and the rest of the SHRDLU-era AI gurus would have been to see how long it took to pull off the dice-matching trick in the video -- but when it does happen, it'll happen faster than we think. We're not yet at the tipping point, but it's close.
Definitely believe we are reaching some sort of global maxima in terms of intelligence in our current structure of society
Isn’t that the ultimate goal?
But let's say it's accomplished. What will end up happening is that AI (should it work to the extent it's been hyper) will replace all the 'fun' jobs and we'll be left with either no jobs (and no income), or the most menial physical labor imaginable.
We'll probably spend all day consuming media and socializing. Thats the optimistic view of course.
This includes reading, gardening, playing sports, learning to play the piano, etc. Of course some of that is consuming media, but I don't think people will just become couch potatoes.
Think of all the things "high society" accomplished in Victorian england because they had the time, energy and resources.
While the ERP is boring as hell compared the the creative coding results, the latter is novelty and often has no intrinsic value.
Also, I see these videos and get deja vu of boston dynamics demos from years back. Not seeing anything new here except this is just early beta version of boston dynamic robots backed by different models.
Also amount of people around the demo set tells me, a lot of supervision and retakes happened. I often do not trust such demos(experience from seeing what goes behind the scene with cherry picked takes being published).
Anyways, my point is, just chill out. I remember how AWS/GCP/Heroku etc were eradicating IT admin but instead now we have dedicated DevOps and IAM specialist roles… and every day I see 7:1 ratio of job vacancy for DevOps:SWE.
The math, well, basic signal processing means you know some algebra and differential calculus. Which is enough, unless, again, you don't want to prove theorems or invent new algos.
I think where the real work will be is taking these models and creating real products out of them.
I'd be more worried being a junior front-end mobile or web dev.
It's like we automate their creation, and the users automate hiding / clicking them.
27 years later, 99.99% of trips are driven by humans.
Real-world robotics takes multiple decades to pan out. These demos are just that: demos. What you are seeing will not remotely impact your life before the 2050's, if ever.
What you should be worried about, however, is you (not your job) becoming irrelevant if you don't learn to write firmware using state-of-the-art AI tooling.
At the minimum: learn to work with Cursor (or equivalent). Make sure you work at a company that uses state-of-the-art AI tooling.
If you want to go further: learn to code (e.g. in python). Take undergrad/grad level courses in math, statistics and fundamentals of deep learning.
And FFS, chill.
[1] https://www.youtube.com/watch?v=2KMAAmkz9go
LLMs were a breakthough out of like a multitude of breakthroughs in AI in the past decade. I think there only needs to be a couple more breakthroughs in the next decade for it to come full circle.
Whenever I here a naysayer open his mouth it's like he's insulting a baby. Look it can't talk! no way it can ever program!
Either way it's not just you. The people creating AI are also as a side effect creating the training data for AI to replace them. So no one is safe.
One key point we must first understand, coding is NOT software engineering or even programming! Writing code is the last bit, a minimal fraction of the job description(unless you are actually indie dev or working for consulting firms). The core tasks include actually untangling the numerous vague requirements, understanding the domain, figuring out best approaches, performing various tests and checks, validating ideas, figuring out a cost effective solution, preparing rough architecture, deciding on an actual set of tech, align a hoard of people that everyone is on the same page, then start coding.
My IDE can already read my mind by auto-suggestion since many years and patterns/frameworks exist to reduce the amount of code I need to write. The issue is that, with these AI model, I just need to abuse my fingers slightly less. The other core duties are not yet solved and remains same archaic procedural everywhere in any/all serious roles.
And speaking of consulting firms, they are also clever and often has several implementations of same stuff, which they can modify a bit and sell for big money.
So in the end, people who jump into the pit because they are afraid of the juju mask are the prime target of the juju mask, for the rest of us, life goes on with minor bumps when the MBA comes to the desk and asks if it is possible to layoff few people to jack up the stock price this quarter yet… while subscribing to that new agenting engineer product suite for double the fees of what the laid off people actually costed, because their best friend in the golf club said the price will eventually become reasonable but the benefits are immediate.
People overestimate the changes that could happen within a couple years, and totally underestimate the changes that would happen in decades.
Perhaps it's a consequence of change having some kind of exponential behavior. The first couple years might not feel like anything in absolute terms, but give it 10 or 20 years and you'll see a huge change in retrospect.
IMHO, I don't think anyone needs to panic now, changes happen fast these days but I don't think things are going to drastically change in these ~2-3 years. But the world is probably going to look very different in 20 years, and in retrospect it will be attributed to seeds planted in these couple years.
In short I think both camps are right, except on different timescales.
Anecdotally the amount of hype and interest has been growing exponentially. This will push progress to a maximal pace. The next 10 years will be significantly faster then the last 10 years.
If you are a professional, please tell me about how much new code you write vs perform other stuff(meetings, alignment, feature planning, system design, benchmark, bug fix, release). For me, the ratio of coding:non-coding is around 10:90 on average week. Some weeks, only code I write are the suggestions on code reviews.
Consider the unmistakable trend: In the early 2010s, deep learning fundamentally transformed machine perception, image recognition, and natural language processing, setting new standards far surpassing earlier methods. By 2016, AlphaGo decisively defeated human champions, showcasing unprecedented strategic depth previously assumed beyond AI’s reach. Shortly after, AlphaFold solved the protein-folding problem, revolutionizing computational biology and drug discovery by rapidly predicting complex molecular structures. In parallel, generative adversarial networks (GANs) and diffusion models ushered in a new era of AI-driven image creation, enabling systems like DALL·E and Midjourney to generate strikingly detailed images, surreal artwork, and hyper-realistic visuals indistinguishable from human craftsmanship. AI’s ability to synthesize realistic voices and human-like speech has dramatically improved through innovations like WaveNet and advanced text-to-speech technologies, leading to widespread practical adoption in virtual assistants and accessibility tools.
Beyond imagery and voice, generative AI has also broken new ground in music composition, where models now produce compositions so sophisticated they are difficult to distinguish from professional human creations. Transformer-based models like GPT-3 and GPT-4 represent a seismic shift in language generation, enabling nuanced conversation, creative writing, complex reasoning, and contextual understanding previously believed impossible. ChatGPT further pushed conversational AI into mainstream utility, effortlessly handling complex user interactions, problem-solving tasks, and even creative brainstorming. Recent innovations in AI-driven video generation and editing—demonstrated by advancements like Runway’s Gen-2—indicate rapidly expanding possibilities for automated multimedia creation, streamlining production pipelines across industries.
Moreover, reinforcement learning breakthroughs have expanded significantly beyond gaming, improving complex logistics operations, real-time decision-making systems, and autonomous navigation in robotics and vehicles. The impressive capabilities demonstrated by autonomous driving systems from Tesla and Waymo further underscore AI’s advancing proficiency in real-world environments. Meanwhile, specialized large language models have emerged, demonstrating near-expert performance in fields such as law, medicine, and finance, streamlining tasks like legal research, medical diagnostics, and financial forecasting with unprecedented accuracy and efficiency.
These advances are not isolated phenomena—they represent continuous, accelerating progress. Today, AI assists with summarization, automated requirement analysis, preliminary architecture design, and domain-specific problem-solving. Each year brings measurable improvements, steadily eroding the barrier between supportive assistance and true cognitive engagement.
Your recognition of AI's limitations today is valid but dangerously incomplete. You fail to account for the rapid pace at which these limitations are being overcome. Each "core task" you've identified—domain understanding, requirement analysis, nuanced decision-making—is precisely within AI's increasingly sophisticated reach. The clear historical evidence signals a near-inevitable breakthrough into human-level reasoning within our professional lifetimes.
In disregarding this aggressively upward trendline, you're making the same grave error committed by those who previously underestimated transformative innovations like personal computing, the internet, and mobile technology. Recognizing current limitations without acknowledging clear indicators of impending revolution isn't merely shortsighted—it's strategically negligent.
The hardest part is data collection.
And many popular robotics demos are either controlled by humans or scripted, so it's useful to have the "Autonomous" label as well to clear up confusion. For example, I know a lot of people who thought the recent Unitree G1 demos were autonomous.
As a matter of fact,they may very well end up being the last bastion.
Being able to order food and handle bureaucracy in these languages while speaking only English would be amazing. This seems like a simpler problem than tackling robots in 3D space, yet it’s still unsolved.
These models are nowhere near that for now, but I'll be watching to see if the big investments into synthetic data generation over the next few years get them closer.
They can be competent enough to cook meals in a controlled environment: (environment built for machines and specific dishes), without ever being able to replicate the same in a human restaurant.
There are a few companies that do robotic cooking, and it has its challenges due to the above reason. I am not aware of the cost problem though.
How cool would it be if this replaced someone at Subway though
Perhaps it goes beyond the brightest minds at Google that people can grasp things with their eyes closed. That we don't need to see to grasp. But designing good robots with tactile sensors is too much for our top researchers.
All the best ideas are tried repeatedly until the input technologies are ripe enough.
But perhaps a great benefit of tactile input is its simplicity. Instead of processing thousands of pixels, which are passive to interference from changing light conditions, one only has to process perhaps a few dozen tactile inputs.
Ex-Googler so maybe I'm just spoiled by access to non-public information?
But I'm fairly sure there's plenty of public material of Google robots gripping.
Is it a play on words?
Like, "we don't need to see to grasp", but obviously that isn't what you meant. We just don't need to if we saw it previously, and it hadn't moved.
EDIT: It does look like the video demonstrates this, including why you can't forgo vision (changing conditions, see 1m02s https://youtu.be/4MvGnmmP3c0?t=62)
Those are realistically the 'natural' developments in the domain knowledge of Robotics/Computer Science.
However, what GP (I think) is raising is the blind spot that robotics currently has on proprioception and tactile sensing at the end-effector as well as a along the kinematic chain.
As in you can accomplish this with just kinematic position and force feedback and Visual servoing. But if you think of any dexterous primate they will handle an object and perceive texture, compliance, brittleness etc in a much richer way then any state-of-the art robotic end-effector.
Unless you devote significant research to creating miniaturized sensors that give a robot an approximation of the information rich sources in human skin, connective tissue, muscle, joints (tactil sensors, tensile sensor, vibration sensors, Force sensors) that blind spot remains.
For the inverse of the robot problem: younger me, spoiled by youth and thinking multitouch was the beginning of a drumbeat of steady revolution, distinctly thought we were a year or two out from having haptics that could "fake" the sensation of feeling a material.
I swear there was stuff to back this up...but I was probably just on a diet of unquestioning, and projecting, Apple blogs when the taptic engine was released, and they probably shared one-off research videos.
If you want to start a list of all the bozos Google wasted oodles of money on, you're going to be here a while.
1) tweak something like that to increase the likelihood of certain situations (abundance of a particular resource/object; frequent geographical feature), and
2) instruct your embodied AI to control a "player" model (to whatever degree of accuracy in articulation/mobility) to wander and perform certain types of tasks.
But sure, without proper compensation a lot of people would probably just say "I can't do it" as a way of avoiding the task.
I understand that many people don't live in America and don't know how to use a coffee maker. That is 100% irrelevant. There is a frustrating tendency in AI circles to conflate domain knowledge with intelligence, in a way that invariably elevates AI and crushes human intelligence into something tiny.
[1] The hard part would be psychological (e.g. keeping the chimp focused), not cognitive. And of course the chimp would need to bring a chimp-sized ladder... It would be an unlawful experiment, but I suspect if you trained a chimp to use a specific coffee maker in another kitchen, forced the chimp to become addicted to coffee, and then put the animal in a totally different kitchen with a different coffee maker (but similar, i.e. not a French press), it figure would figure out what to do.
It also excludes corner cases like "what if they don’t have any filters"? Should the robot go tearing through the house till they find one, or do nothing? But what if there were some in the pantry — does that fail the test? There’s all kinds of implicit assumptions here that make it quite hard.
As for the point of corner cases being hard - I mean that's the point here, isn't it?
Hope to share the details here soon.
And you can train the model by yourself without relying on cloud services.
Some URLs to get your started:
https://huggingface.co/lerobot
https://github.com/huggingface/lerobot/blob/main/examples/10...
the latest project: Le Kiwi, using the SO-ARM100 arm:
https://github.com/huggingface/lerobot/blob/main/examples/11...
the super advanced HopeJR shoulder + arm + hands:
https://github.com/TheRobotStudio/HOPEJr
cool video: https://www.youtube.com/watch?v=VKHfy2vACyw
Not being a high profile target myself, I'd rather take that risk and see where it goes. Unfortunately it's the high profile targets themselves that make the decisions, so after the first few incidents I figure there will be this whole mess where they try to clamp down on access to such things without sufficient forethought.
Now, clean up the kitchen.
Fuck it, make the arms big enough and it can do laundry, load/unload dishwasher, clean up after cooking/eating.
I can finally see this happening. Probably Tesla first tho.
Also, I vaguely remember similar demos without the AI hype. Maybe it was from DeepMind, or another upstart back in 2015.
I expect they are more honest than the Telsa men in suits debacle, but my trust is low.
What do we know to be the facts?
https://news.ycombinator.com/item?id=43331358
I assume google made the choice of selling the "brain" for any "body" whoever develops it. Something like android.
This is a great achievement and I'm not underestimating the work of the people involved. But similar videos have been put together by many research labs and startups for years now.
I feel like Google's a bit lost. And Sundai's leadership has not been good for this, if we're honest.
GOOG is around the same price as it was in 2022, which means the AI wave went by through them with zero effect. With other tech companies doubling/tripling their market cap during this time, Sundai really left 1 trillion of unrealized value on the table (!); also consider Google had all the cards at one point, quite mediocre imo.
You don't have to release a profitable product, but to compete over the next several decades you are going to need to own valuable land in remote territories where patent wars being fought today. I'm guessing Google's meta-strategy is a type of patent-colonialism.
I see, are you a VC in the valley?
Even after the massive total market correction in the last few weeks, the earliest that GOOG was the same price as today is not even a full year ago. In fact, it's up 90% since 2022.
Any stock market source would tell you GOOG was ~140 USD at the start of 2022. Today it is ~170 USD. A 20% increase over three years, about the same rate as inflation and S&P.
This is extremely trivial to verify. Was this written by a GPT bot?
It's just the up and down of the entire market (and these big techs dominate S&P 500). I don't think that actually indicates anything.
But I did specify "start of 2022".
Is this another bot account?
Your initial comment didn't:
> > GOOG is around the same price as it was in 2022
That's the comment pb7 replied to by saying it's up 90% since 2022 (which is true, or even an underestimate, depending on where you measure from) and to which you responded calling them a bot because your own measurement, from the start of the year, gives a lower number.
cvhc is pointing out that it's the different choice of where to measure from that caused the difference in results - neither are incorrect.
I guess my broken English doesn't match today's bot quality :)
I would love to experiment with something like this but everytime I try to figure out what hardware to do it with there's a thousand cheap no-name options and then bam 30k+ for the pro ones.
However, when I looked at the BOM I was surprised that the actual arm they use is an incredibly expensive off-the-self arm https://www.trossenrobotics.com/viperx-300
For a much cheaper option take a look at https://github.com/huggingface/lerobot (this is an AI training library/framework) which uses the SO-100 arm https://github.com/TheRobotStudio/SO-ARM100 (one arm is $123). See: https://www.youtube.com/watch?v=n32OmyoQkfs
There's also the Parol6 arm, which is more performant than the SO-100, but more expensive: https://source-robotics.com
1.) Has cutting edge in house AI models (Like OpenAI, Anthropic, Grok, etc.)
2.) Has cutting edge in house AI hardware acceleration (Like Nvidia)
3.) Has (likely) cutting edge robotics (Like Boston Dynamics, Tesla, Figure)
4.) Has industry leading self driving taxis (Like Tesla wants)
5.) Has all the other stuff that Google does. (Like insert most tech companies)
The big thing that Google lacks is excitement and hype (Look at the comments for all their development showcases). They've lost their veneer, for totally understandable reasons, but that veneer is just dusty, the fundamentals of it are still top notch. They are still poised to dominate in what the current forecasted future looks like. The things that are tripping Google up are relatively easy fixes compared to something like a true tech disadvantage.
I'm not trying to shill despite how shill like this post objectively is. It's just an observation that Google has all the right players and really just needs better coaching. Something that isn't too difficult fix, and something shareholders will get eventually.
Google as a whole has a long history of not being able to successfully build great products out of great tech. That seems wrong from looking at search, Gmail, Maps*, Docs*, etc., but I think these are cases where a single great insight or innovation so dominated the rest of the product qualities that it made the product successful on it's own (PageRank, AJAX, realtime collaboration). There have been so many other cases where this pattern didn't hold, and even though Google had better tech, it wasn't so much better on one axis as to pull the whole product along with it.
That's the problem I see here. Maybe they have a better model. Can they make it a better product? OpenAI and Anthropic seem to ship faster, with a clearer vision, and more innovation with features around the model. Is their AI hardware acceleration really going to be a game changer if it's only ever available in-house?
I do believe in Waymo, but only because they've been incrementally investing and improving it for 15 years. They need to do that with all products, instead of giving up when they're not an instant hit.
*Maps, Docs, and YouTube were acquired with their key advantages in place, so I wonder how much they even count.
A better management with long term thinking would utilize Google's enormous base of talented engineers far better.
[1]https://www.hbs.edu/faculty/Pages/item.aspx?num=46
Larry Page was making the rounds when all of this AI hype started. He seemed to have a much more aggressive stance, even ruffling feathers about how many hours Google employees should be working to compete in AI. And there is obviously Demis Hassabis who is the most likely contender for a replacement.
I doubt it is an easy position to fill. But Pichai has presided over this lackluster Google. Even if he isn't strictly to blame, I am surprised he hasn't be replaced.
I guess it is easy to view it from my own perspective, one tinged with a hope for invention and innovation. But the market probably loves the financial stability Pichai has brought to the table and doesn't care about the flaws I see.
And I 'm not sure why I have rose tinted glasses for Nadella. I believe MS has been doing well financially (not something I've studied) while also supporting things I believe are valuable (e.g. VS Code, GitHub, TypeScript). Maybe I just wish I felt the same kind of balance in Google.
That’s such a refreshing change from the “DIE OPEN SOURCE DIE” attitude that Gates/Ballmer had.
I also love GitHub, TypeScript, and VSCode. These have become the foundation of my development toolset. That was something Gates did well, and Ballmer gave lip service to (“developers! developers!”) but for me only recently has Microsoft actually been maintaining good quality developer tools again.
That’s where my goodwill comes from anyway.
Google makes a better Office Suite (Gmail, Docs, Maps), ironically. But it’s hard for me to get too excited about that. It’s been pretty stagnant for 10 years.
Just off the top of my head, under Tim Cook the company managed to:
* Propel smartwatches as a brand new product category into the mainstream and be the leader in that category.
* Propel AirPods as a brand new product category into the mainstream (and be the leader in that category as well).
* Smoothly transition to ARM (aka Apple Silicon) with great success.
* Various behind the scenes logistical/supply-chain achievements (which makes sense, as Tim Cook is the logistics/supply-chain guy by specialization).
None of those things were simple or uncontroversial. In fact, I remember the pushback people and the press had against smartwatches and airpods, calling Apple washed out and Tim Cook a bean-counter. And these are just the largest examples off the top of my head, there are definitely more. However, Google doesn’t seem to have even a singular product win of such magnitude in the past 10 years.
In the meantime, what did Google do productwise? Catching up on the cloud compute game to AWS (while nearly killing it due to their PR nightmare announcements during 2019-2020 iirc), killing their chat app that finally managed to gain enough mainstream traction (Hangouts) and then rebranding/recreating it at least twice since then, redoing their payments app multiple times (gWallet vs gPay vs whatever else there was that I forgot), etc.
I am trying to be generous here, and of course Apple had their misses too (the butterfly keyboard on 2016-2019 intel macbooks, homepod is kinda up in the air as a product category, mac pro stagnating, etc.). But I legitimately cannot think of a single consumer product that Google knocked out of the park or any that wowed me.
This sucks, because I know for a fact it has nothing to do with their engineers lacking the skill to execute on a new innovative product (as evident by Google being early to the AI/transformers era and being fundamental to what is happening with AI right now). Google has all the technical prerequisites to succeed. But the product and organizational strategies there are by far the most cartoonishly bad I’ve ever seen for such a company.
I don’t want to blame it on Sundar, because I cannot say for sure that the root of this dysfunction is at his level. I just know it is on some level between org directors and Sundar, but not where exactly. I just know that killing off a whole org working on a truly innovative AR org/product, only for most of those people to switch to Meta and continue working on an improved version of the exact same thing (the Orion glasses) wasn’t the move. And I just know that having 5+ major reorgs in one year for a single team is not normal or good.
TLDR: apologies for the long rant, but the short version is that Google under Sundar has absolutely zero sense for internal organization management or delivering products to consumers. And comparing him to Tim Cook (who has been the CEO through the AirPods/Apple Watch/ARM macbooks era) is unfair to Tim Cook and is based purely on the public image.
Vision Pro might succeed or fail, and that’s fine. I tried it, and it is clearly a significant step towards the future, but I am not sure of it becoming a successful product at its current price point and in its current state.
I am not judging CEOs or companies negatively for taking ambitious product bets and not always striking gold on those bets. I am judging them negatively for not having any product wins and not taking any ambitious product bets.
Imagine being so replete with cash that after paying all your costs, all your salaries, all your R&D - you still can't find a way to spend 200 billion, so you threw a chunk of it away as tax and put the rest in the bank.
The price of a share should be utterly irrelevant to them.
Take chat, one of Google's biggest fumbles. They had a good thing with Gtalk. Really screwed things up with Hangouts (thanks, Vic!), added the weird Allo to the mix, almost turned things around, and then brought in Chat to compete with Slack as opposed to AIM...WhatsApp.
If they had just incrementally invested in chat, even if they swapped out back ends, they could have kept most of their user base, maybe even have grown it. Gchat was pretty popular, even during the rise of Facebook Messenger.
But they screwed around with the public-visible product side of things too much, and revealed their tech stack and org chart as product changes. There was no product-first, continuity-oriented planning.
Many of the decisions companies make are to ensure the cow they are currently milking very efficiently does not die. This is bad for the rest of us, specially if they place barriers to innovation.
May be that's the problem - that there is no one rallying individual for Waymo. They should just spin it off and make it an independent private company and retain % ownership.
I somehow feel Google will be way better if it's run like Berkshire, the CEO just focuses on capital allocation and let's the managers do their jobs in their respective companies - YT, Waymo, search, cloud, deepmind.
I'm not sure that culture can dissipate in Google at this juncture.
Waymo is all about partnerships with carmakers.
> they have blogged about their cutting-edge protobuf tsunami capabilities.
Not sure if you recall the blog post url or title, but I'm curious to read more.
Do you have a link to this?
I don’t think the same logic applies to Google Docs as it does to YouTube. The original companies behind Docs, Sheets, and Slides were practically unknown, and Google deserves credit for their evolution, features, and clear vision. Developing an office suite might be “easier” from a vision standpoint since the category already exists, whereas marketing something like Gemini Robotics is an entirely different challenge. Just my two cents.
There's far too much value and scale in the company and they can't even focus their energies appropriately.
YouTube is the most valuable media property in the world. As a standalone company, it would still outperform Netflix on the basis of ads alone.
The monopolistic stuff Google is pulling off with Chrome/Android/Search is unfathomably market distorting, so those business units alone could/should be pulled apart. The tech sector would probably be better off if YouTube, Waymo, and GCP/AI efforts were similarly split up.
Also there is MS that wants to pay for search engine placement and it's fact.
They'd just have to watch out for similar antitrust action.
They have a thinking model way back ago, which is pretty good with clean CoT and good performance close to R1. But it never gets any marketing whatever.
Veo2 has really good performance too, yet it is so slow in its rollout now Chinese competitors are getting all attentions because it is just easier to access.
It feels to me that Google is reliving its experience with messengers where you they have multiple competing roadmaps from different parties. The execution is disoriented and slow.
They will have to catch up in 2025, the fact grok is this good in one year is a wake up call to everyone, especially Google.
If they failed to do so, Gemini is going nowhere, it already has no tractions outside of Google, nobody’s first instinct when it comes to AI is Gemini
Doesn't ring a bell that very few, if any, of "AGI achieved" people seem to have backgrounds with or exposures to either classical NLP, or Google, and/or cultures that make heavy use of IME? To me the situation looked like that Googlers "have seen that trick" previously, and are doing bare minimum to defend the company from losing presence in this AGI hype storm.
They have Gemini and rolled out AI in Workspace and I believe they still have the most capability million token model
ChatGPT is already top 5 websites people visit, it is behind Google, but it will eat into its business very soon. That will happen regardless.
It's an artifact of their size -- no large corporation has vision or direction. Best they can aspire to is "stay the course". It's just something that inevitably happens as companies grow and age.
It won’t go anywhere, Windows is still a thing.
But ChaGPT is a fundamental threat to its search business. It replaces Google for me 50% of the time.
It is the natural language search engine people tried to build
You might say, yeah, but I can spot those mistakes, but can you really? I showed my fifth-grade son the result of asking if hippos were intelligent and the absurdity of the answer didn’t leap out at him. Now, consider something that’s more subtly wrong like an invented precedent in an AI-generated legal brief or a non-existent citation or citation that doesn’t support the claim and it’s all a disaster.
For sure, hallucinations will always be there, but I don't think it will hinder its take over, the usage trumps its shortcomings
Yesterday I tried asking ChatGPT "Can an Amazon L6 software engineer afford a house in [location]", without explicitly using the search mode. It went to levels.fyi to look up salary and redfin to look up housing price (exactly how I would have done it myself), and gave me a reasonable answer that agrees with my own analysis, and is definitely much faster than clicking things around myself.
OpenAI is fading away fast. Plus all major leaders left, Microsoft is leaving too, I don't feel its future is promising anymore.
Maybe the company and their business model are doomed to fail, but I'm grateful for what they enabled so far.
On other similar products like Google's NotebookLLM and Open AI's GPT 4.5 Research Mode: both products are awesome.
> VideoFX isn't available in your country yet
Maybe that's why?
I still maintain the reason they're playing catch-up with everyone else wrt. LLMs is because their Gemini models were not available in the EU until recently. Back when they were doing their releases, years ago, like everyone else here I took one look, saw the "not available in your country" banner, and stopped caring at all.
That's why nobody knows about it.
Obligatory overview of things Google has killed, because its easy to forget some of the gems:
https://killedbygoogle.com/
Sure it could've used a bit of a facelift and some other tweaks, but they have a history of launching new, half-baked products instead of just maintaining the existing ones.
I even messaged from my GTalk to my Facebook as a test, which worked because both were Jabber. Both companies closed both services off to anyone else. Sadly.
It has a problem executing on that tech to create great products. It has a real problem with canning any project that doesn't have a billion users within a year.
Honestly they fail to understand how lucky they got with doubleclick and culturally the entire project evaluation criteria is based around the assumption that they can do another computer science rain dance to make it rain ads-level cash.
Maps used to be the absolute best and now I frequently get baffling driving directions in a US major metro area. No improvements within the last 10 years. New pixel phones are worse than latest Samsung. Some huge lead in AI absolutely totaled, their investment in anthropic their only hope. Inference HW accelerators that noone uses.
They are becoming like M$ - I expect M$ to be this terrible at product development - but at least M$ is fantastic at making money despite terrible products.
Google has allowed search experience to slide so much people would rather use some slow-ass unreliable chatbot. Are they really losing the war on SEO or have they decided that the internet-of-shit (i.e. affiliate marketing) is more valuable?
I am seeing bot-generated reviews more and more often, and when I look at what happened to search, I don't have a lot of faith in google to do a better job with maps. But I sure hope they do, because I'm with you - I really do rely on maps reviews.
So while they have a bunch of cool tech on the possibility horizon the only thing the market cares about is the ability to make money and there's some uncertainty on that front.
I think what keeps Google up at night is knowing that their ads business which pays all of the bills could be upended by regulation or by disruptive consumer AI of some kind and they’d then have approximately nothing in terms of revenue.
They have due to circumstances a different business model to OpenAI, Claude, Grok etc.
Open-Claude-Grok: "our AI is so cool, AGI next year" but we are losing money so invest in us at a $crazy bn valuation
Google: We are swimming in money from ads so no need to hype anything. If anything saying we will dominate AI as well as search, email, video, ads, browsers, phones etc would just get us broken up. So advance quietly.
Gemini has been one of the most cost-efficient models. Probably this is exactly what Google needs for productization.
Not sure if this is "vision" or "management" or whatever, it feels like that they're just self shackling in every single possible direction. There are something like 50 different teams involved at a major launch and they make some process/infra requirement/review/integration or whatever, from good will. Imagine how much time, effort and compromises you would need to appease all of them.
I think the recent memo from Sergey shows that the leadership finally acknowledges this problem at heart. But solving it is a different story of course. But a long time disconnection between IC, managements and leadership has been the culprit of this problem and at least some awareness might not hurt.
Based on P/E the US stock market is overvalued. So I would be careful with "undervaluation". Most undervalued tech stocks are probably in China.
Google also lost a lot to LLM. I use perplexity now 50% of the time, where I would have used Google. I also read a lot of "degoogeling" and "going off Amazon". My impressions of both companies are not the best. I have a gmail account I never got access back, even with the right password. And Amazon defrauded me of 40 USD. Claimed in a chat that they would reimburse express shipment after they f. up but then did not and called it a "misunderstanding".
I have somewhere list of the most valuable companies. And it changed every decade. So, past performance is not a guarantee for future performance :-)
Bust is unlikely with an ETF. They rebalance without you having to do anything. Most tech might come from China in the future.
They foster the confusion themselves.
I once tried to rebrand an in-house, purely dev facing product. I failed.
Google Cloud is decent, again in my opinion, because they can more or less copy the product vision from AWS and focus on the technical excellence.
When were you last excited to use a Google product or service?
Part of the problem is also their internal incentives that lead to lot's of products being retired waay too soon, leaving behind a lot of users and hurting their reputation a lot.
In addition to the other comment mentioning Boston Dynamics, they are also the employers of a lot of folks that were formerly at the Open Source Robotics Foundation(?) (OSRF) (it's more complicated than that) which is behind the ROS1/ROS2 framework that are widely (not universally) used; They also have an internal division or whatever, Intrinsic Robotics (or is it Intrinsic AI? too lazy to check). Plenty of smart people that I've met are involved there!
But I remain skeptical of the top level comment's take, given the lack of any robotics product execution of note by Google for a very long time now.
There is nuance. Saying A about B and being wrong does not imply saying A about C means you're wrong. It is indeed possible to lose focus on revenue and die. But it is also possible to focus too much on revenue and die. It is unclear if Google will achieve anything from it's "pure research" investments, but certainly they have room to try, and I personally am glad they are doing so.
They were profoundly wrong, but not about Bell Labs' ability to create value from their research. That, they were absolutely dead-on about. AT&T and Bell Labs were absolutely awful at reading the room about what their technology could do and how it could be monetized.
Some of that was just packaging things the right way, and some of it - like charging absolutely insane license fees for UNIX in the 80s and 90s during the beginnings of the personal computing revolution - was because of lazy execs who didn't want to really put in any effort. Either way, I'm not using a Bell Labs LabsBook Pro to write code for a UNIX OS, and I'm not using Bellgle to search for information. AT&T ultimately thought the best way to create value from Bell Labs was to sell that division.
We're in a long, hot AI summer, but we've had winters too. Who knows which hemisphere they're in at Google right now.
Even extraordinary products are rarely going to do that. Their AI products could be a huge success, and still not significantly change how valuable the company is.
> Google lacks is excitement and hype
People(me) used to look up to Google and the projects they had. 80/20 work/project time, moonshot projects, all the google perks, etc. It felt like the place to be. Fast forward 10 years, I just want antitrust to shatter it into smithereens.
> that veneer is just dusty
The problem is systematic, affecting the whole org from top to bottom and especially the top. They either get a new CEO that turns things around or become another IBM.
On the tech side they are excellent, but on the management/business/corporate culture side they have repeatedly proven that they are much less competent than pretty much everyone else.
Fortunately for them, they have a very prolific cow to milk with their ads business, and that's where they get their valuation from, but there tech is legitimately undervalued because they have repeatedly shown that they don't know how to convert that into business.
They definitely have pricing power and also a large stake in Anthropic, so I'm not worried about them.
The only thing you left out of this analysis is their valuation. The market values Google at $2.05T (just over $2,000,000,000,000) which is 21 times their earnings (net profit). They are valued at $250 per person on Earth while selling, annually, $43.75 per person on Earth (sales) of which $12 per person is their profit.
How much would you pay to own a golden goose laying $12 in gold per year? Like, $250? If so you are the proud buyer of Google right now. (There is a buyer on every sale of every stock and this is the price they are paying right now.)
Apple (AAPL): 34.07
Microsoft (MSFT): 35.07
Amazon (AMZN): 36.69
Alphabet (GOOGL): 21.82
Meta Platforms (META): 24.49
Nvidia (NVDA): 41.33
Tesla (TSLA): 87.87
from this perspective Google, and to a lesser extent Meta, stand out as being valued quite conservatively.
Do I think Microsoft is performing 50% better than Google? Not really, no.
They are a roadblock to a lot of the startups backing the current administration
All of the technology in the world doesn't make up for that.
why work hard to be a part of that?
Uhhh, I mean that's nice, but how about: "That's why we will never sell our products to military, police, or other openly violent groups, and will ensure that our robots will always respond instantly to commands like, 'stop, you're hurting me', which they understand in every documented human language on earth, and with which they will comply regardless of who gave the previous command that caused violent behavior."
Who is building the robot cohort that is immune - down to the firmware level - to state coercion and military industry influence?
If scifi authors aren't keeping up it's hard to expect the rest of us to. But the macro and micro economic changes implied by this technology are huge. Very little of our daily lives will be undisrupted when it propagates and saturates the culture, even with no further fundamental advances.
Can anyone recommend scifi that makes plausible projections around this tech?
This is largely a function of what science fiction you read. Military SF is basically about retelling Horatio Hornblower stories in space, and it has never been seriously grounded in science. This isn't a criticism, exactly.
But if you look at, say, the award-winning science fiction of the 90s, for example you have A Fire Upon the Deep, the stories that were republished as Accelerando, the Culture novels, etc. All of these stories assume major improvements in AI and most of them involve breakneck rates of technological change.
But these stories have become less popular, because the authors generally thought through the implications of (for a example) AI that was sufficiently capable to maintain a starship. And the obvious conclusion is, why would AI stop at sweeping the corridors? Why not pilot the ship? Why not build the ships and give them orders? Why do people assume that technological progress conveniently stops right about the time the robots can mop the decks? Why doesn't that technology surpass and obsolete the humans entirely?
It turns that out that humans mostly want to read stories about other humans. Which is where many of the better SF authors have been focusing for a while now.
> Its technology is how a society copes with physical reality: how people get and keep and cook food, how they clothe themselves, what their power sources are (animal? human? water? wind? electricity? other?) what they build with and what they build, their medicine — and so on and on. Perhaps very ethereal people aren’t interested in these mundane, bodily matters, but I’m fascinated by them, and I think most of my readers are too.
> Technology is the active human interface with the material world.
[1] https://www.ursulakleguin.com/a-rant-about-technology
Also I love the Zones of Thought series and The Culture.
Seeing drones do all the work unfortunately isn't very interesting though.
If you're open to Theory Fiction, you can read Nick Land. Even his early 1990s texts still feel futuristic. I think his views on the autonomization of AI, capital, and robots - and their convergence - are very interesting. [1]
[0] https://edoras.sdsu.edu/~vinge/misc/singularity.html
[1] https://retrochronic.com/
This is just one of the side plots of the book, I think it could've been the whole plot of a book.
Tell me, which corporation exactly is kidnapping and drugging people to enslave them and then discard their bodies at sea to feed the capitalist global machine?
It seems like you have a big scoop if you are doing on the ground reporting, because that seems like it would be international news if it was real!
https://www.cbc.ca/radio/thecurrent/the-current-for-nov-12-2...
https://www.ap.org/news-highlights/seafood-from-slaves/2015/...
The sun is dying. A capable team is assembled and put into cryosleep in an automated ship for a journey to a neighboring star system to try to diagnose the problem. Only one member survives, and they have amnesia.
The novel does a great job of explaining the process of troubleshooting under pressure and with incomplete information.
Strong warning: Start with either book 2 (Player of Games) or book...7, Look to Windward.
I strongly suggest you skip book 1 until you're comfortable with the rest of the books that focus on the Culture itself, and not some weird offshoot story that barely involves the Culture.
Though I wouldn't recommend starting with any of the stories in the series. Or reading any at all. Find a summary or a Cliff's Notes instead. Iain M Banks has a talent for making great stories tedious.
Has a cyborg/AI as protagonist and paints a really interesting world with AIs and synthetic biology in it. Also does a good job at just shutting up about things it can not talk about, like interplanetary travel.
I've also seen it suggested that Harry Potter might be a more realistic look at what proliferated AI might be like.
"Will the security update finish before we're discovered and killed by the hunter seeker, stay tuned to find out more!"
It does such a good job building a convincing world, and its really good in just not going into details that it can't speak on (like how interplanetary travel works), while some of it's takes (e.g. small anti-personel drones) seem almost prescient after Ukraine.
All the synthetic biology and even the depictions of AIs and their struggles are really compelling, too.
Unironically, Wall-E. Humans leave earth behind on a ship where everything is automated.
This seems like it's rooted in reality.
It's pretty normal for it to take a few years to write a good book so I wouldn't look to science fiction to keep up to date on the latest tech hype train. This is probably a good thing because when the hype dies down or the bubble bursts, those books would often end up looking very dated and laughably naive.
There's a lot of books about AGI already which is probably more fun to write about than what passes for AI right now. Still, I'm sure that eventually we'll see characters getting their email badly summarized in fiction too.
My brother in Christ, ChatGPT blew up just 25 months ago. Give it time.
It seems unlikely that any company (Google included) will have a robotics moat.
If we see a real world application that a business actually uses, or that people want to use, that's great. But why announce the prototype with the lab demos? It's premature. Better to wait until you have a good real life working use case to brag about.
Because that's how you attract the media attention, talent, and financing you need to both go from prototype for product, and to have a market ready for the product when its ready.
Especially when other people are already publicly known to be working in the domain.
Lol, you need to drive up hype and convince investors you are not falling behind. Not even being cynical here, I think it's a good idea from a business perspective.
Had they focused more on driving innovation and not profit/being relevant, they could have had another win instead of another Google+ Instead, we got African-German Nazi's.
Google was already an advertising monopoly by the time this happened and his job is to sell ads and minimize costs...the rest of Google is just there for marketing & public relations