Those new language models are "Google killers" because they reset all the assumptions that people have made about search for several decades. Imagine that people start using those chat bots massively as a replacement for Google search. Then the notion of keyword disappears. Google AdSense becomes mostly irrelevant.
Of course, Google is a giant today with a history of machine learning innovation. So they have a good chance of being successful in that new world. But the point is that many other companies get a chance again, which hadn't happened in 20 years. You couldn't displace Google in the old search/keyword/click business model. Now everyone gets a fresh start.
Who knows what the economics will be. Just like page rank early on, it was expensive to compute. But the money in advertising made it worth it, and Google scaled rapidly. Which language model do you run? The expensive one, or the light one (notice how Google in this announcement mentions they will only offer the significantly smaller model to the public). Can you make this profitable?
Other fun questions to answer if the industry moves to chat vs. search, in a 5-10 year horizon. What is the motivation to write a blog post by then? Imagine no one actually reads web sites. Instead a blog post to share an opinion, I'll probably want to make sure my opinion gets picked up by the language model. How do I do that? Computed knowledge may render many websites and blogs obsolete.
The point about the economics of running these models is an important one that slides under the radar a lot of times. The training costs for large language models like GPT are enormous, and the inference costs are substantial too. Right now things like ChatGPT are very cool parlor tricks, but there's absolutely no way to justify them in terms of the economics of running the service today.
Obviously this is all going to change in the near to mid future: innovation will drive down costs of both training and inference, and the models will be monetized in ways that bring in more revenue. But I don't think the long term economics are obvious to anyone, including Google or OpenAI. It's really hard to predict how much more efficient we'll get at training/serving these models as most of the gains there are going to come from improved model architectures, and it's very difficult to predict how much room for improvement there is there. Google (and Microsoft, Yandex, Baidu, etc.) know how to index the web and serve search queries to users at an extremely low cost per query that can be compensated by ads that make fractions of a cent per impression. It's not obvious at all if that's possible with LLMs, or if it possible, what the timescale is to get to a place where the economics make sense and the service actually makes money.
For a group of people on a site frequented by startup people, did nobody read the terms of MS's investment into OpenAI?
"Microsoft would reportedly put down $10 billion for a 75% share of the profits that OpenAI earns until the money on the investment is paid back. Then when Microsoft breaks even on the $10 billion investment, they would get a 49% stake in OpenAI.
These are not the terms you would take if, tomorrow, or even two years from now, you were about to be wildly profitable because everything was about to be so easy.
These are the terms you would take if Microsoft was the only hope you had of getting the resources you need, or if getting somewhere was going to be very expensive and you needed to defray costs.
Honestly, with the level of optimism in the rest of this thread about how easy this will all be, they would probably be profitable enough to just buy MS in like 3 years , and wouldn't have needed investment at all!
> Microsoft would reportedly put down $10 billion for a 75% share of the profits
> that OpenAI earns until the money on the investment is paid back. Then when Microsoft
> breaks even on the $10 billion investment, they would get a 49% stake in OpenAI.
To put that in perspective, which is often difficult with large sums of money like this, $10 billion is _half_ of what Facebook paid for Whatsapp.
The point was not that the profits are long tailed but rather if openai thought there were massive future profits and the potential to become a google killer then they wouldn’t give away half of their company today for $10B.
But how do you know that they didn't predict that it is only by having a $10 billion that they _can_ become the next google killer, because of the initial cost outlay, and the inevitable fight back from google (which would then require a warchest, of which i'm sure this 10 billion is a part of)?
A couple things.
I think the google killer thing is kind of funny in a few senses:
OpenAI talks mostly about trying to help change humanity, not about winning at business. Their mission is still "advance AI safely for humanity". It's not even obvious they care about winning at business. We seem to be putting that on them.
In that sense, i'm not actually sure they care whether they beat Google or not. I mean that honestly. If they care about the stated goal, it would not matter whether they do it or Google does it. I'm not suggesting they don't want to win at all, but it doesn't seem like that is what is driving them except to the degree they need money to get somewhere?
If they really succeed at most of their mission, killing Google might be a side-effect, it might not, but it would just be collateral damage either way.
Beyond that, I don't disagree, i actually agree with you.
My point on that front is basically: "Everyone thinks this will be cheap and easy very soon, and change the world very quickly".
I believe (and suspect OpenAI believes) it will be very expensive upfront, very hard to get resources in the short term, and change the world slower as a result
Plenty of people have already said "Google is dead" in certain terms.
If somebody needs ten billion dollars in additional work and investment to make that a reality, how certain can they be?
OpenAI has 375 employees (according to Google). At 300,000 a head thats 110M per year in compensation. Let's say that their compute costs are enormous and go to 200M in expenses per year. 10B is fifty years of expenses. So if they need 10B in investment it becomes obvious that they believe that they have to change something about their business in a fundamental way. Maybe that is going to be enough, but if it is so certain it becomes hard to believe that they'd need this kind of investment.
To me, the fact that MSFT invested on these terms doesn’t mean that the financials are shaky, it means that there’s a huge first mover advantage as well as a huge start up cost. OpenAI could make their money back or they could get taken out back by Google if they’re bottlenecked by cash.
OpenAI is projected to make 200m in 2023 and 1bn in revenue in 2024. If they can (roughly) keep this YoY growth rate, they will become a money printer in 3–5 years.
That's the best case scenario. Worst case is that they get nothing back and own nothing. (Based on the quick summary of the deal above; I don't know more than what's here.)
GLM-130B[1] is something comparable to GPT-3. It's a 130billion parameter model vs GPT-3's 175 billion, and it can comfortably run on current-gen high end consumer level hardware. A system with 4 RTX 3090s (< $4k) gets results in about 16 seconds per query.
The proverbial 'some guy on Twitter'[2] got it setup, and broke down the costs, demonstrated some prompts, and what not. The output's pretty terrible, but it's unclear to me whether that's inherent or a result of priority. I expect OpenAI spent a lot of manpower on supervised training, whereas this system probably had minimal, especially in English (it's from a Chinese university).
If these technologies end up as anything more than a 'novelty of the year' type event, then I expect to see them able to be run locally on phones within a decade. There will be a convergence between hardware improving and the software getting more efficient.
Agree, but much less than 10 years. Now that the transformer is establishing itself as the model, we’ll see dedicated HW acceleration for transformers, encoders, decoders, etc. I will eat my hat* if we don’t see local inference for 200B+ parameters within 5 years.
i would imagine these models would be considered trade secrets, and that the models (esp. good ones that take a lot of resources to have trained) would not leave the data center. Your access to such models would be dictated by an api instead of locally run.
No-one in particular. It will be integrated in the processors (the intels/AMDs on PC, the Mx on Macs and the Qualcomms, Mediateks and Samsungs on phones).
Not much different from video and graphics accelerators being integrated today, or DSP focused instructions in the instruction set.
Not sure about 200B+ models on phone hardware in 10 years. But I think we'll be able to deliver the same predictive performance with models 1/10 the size and those will fit. That is what happened with CNN vision models over the last 8 years.
Would they? An array of a million of these machines would cost $4 billion at consumer retail prices. That's 1.4% of Google's annual revenue for one-off bulk cost. The operational cost, at consumer retail electric using current inflated levels, was a small fraction of a cent per query. This is ignoring economy of scale, better electric pricing, caching, etc.
Where are you getting a CPU + RAM + RTX 3090 for $1k? To even install a million of these machines, you'd have to build a new datacenter, the capital costs are going to be beyond just the wholesale price of GPU boards, and you'll have to hire a ton of datacenter technicians.
But leaving that aside, look at OpenAI's pricing. $.02/1K tokens. Let's say the average query would be 20 tokens, so you'd get 50 queries/$.02 = 2500 queries/1$, or for 100k, $40/sec * 86400 * 365 = $1.2b. My guess is OpenAI's costs right now are not scaled to handle 100k QPS, so they're way underpriced for that load. This might be a cost Google could stomach.
I just think blindly shoe-horning these 100B+ param models into this use case is probably the wrong strategy, DeepMind's Chinchilla has shown it's possible to significantly reduce parameter size/cost while staying competitive in accuracy. I think Google's going to eventually get there, but they're going to do it more efficiently that brute forcing a GPT-3 style model. These very large parameter models are tech demos IMHO at this point.
You can get an RTX 3090 for < $1k. I was largely handwaving away the rest of the costs since all the processing is done on those cards and basic hardware is really cheap now a days. But in hindsight that might not be entirely reasonable because you would need a motherboard that could support a 4x setup, as well as a reasonable power supply. But the cost there is still going to be in the same ballpark, so I don't think it changes much.
That said I do agree with your final conclusion. Bigger is not necessarily better in neural networks, and I also expect to see requirements rapidly decline. I also don't really see this as being something that's going to gets ultra-monopolized and centralized. One big difference between natural language interfaces and something like search is user expectations. With natural language the user has an expectation of a result, and if a service can't meet that expectation - then they'll go elsewhere. And I think it is literally impossible for any single service to meet the expectations of everybody.
> Google currently handles 100,000 queries per second.
There's a lot of duplication in those queries. If the answers can be cached, a more useful metric would be unique queries per some unit of time (longer than a second).
Oh it will. We'll be asking our iphone questions and it'll be returning seo spam and ads for viagra in a chatbox. Meanwhile, the big boys get the firehose feed.
1. How much did it cost to train ChatGPT/GPT3? The only estimate I’ve seen was not enormous in the grand scheme of things (eg more money than I have but less than Google have stuck down the back of the sofa). I think that number didn’t count training precursor models or paying for people to come up with the models/training data/infra.
2. Don’t Google have specialised hardware for training neural networks? If the costs of training/inference are very significant won’t Google (with their ASIC and hardware design team) have a significant advantage? It seems to me that their AI hardware was developed because they saw this problem coming a long way off.
1. We don't know the exact cost, but its well into the millions. When Microsoft invested in OpenAI, it did so in the form of ~$500M in azure credits, so they're expecting at least that much in compute spend. Another company estimated that GPT-3 alone cost the equivalent of $4.5M in cloud costs (ignoring OpenAI's other models).
2. Yes. Yes they will/are developing custom silicon that will likely be a significant advantage here. GPU costs were always crazy, and many companies are designing AI chips now. Even the iPhone chips have custom AI Cores. We'll see if Azure releases AI co-processors to aid them...
Yeah I figured that most of the costs would be from iterating and training many models over time. $4.5m is surely not the kind of spending that will make google nervous or will give OpenAI much of a moat.
Did metaverse ever not look like a VR rehash of second life? I’m genuinely curious, I‘ve had VR headsets since Oculus DK1 I’ve never seen anything very compelling on the VR persistent alternate reality front.
Metaverse wanted to do what VRChat had already done tbh. VRChat just needs polish & moderation/to create a proper platform.
Realistically VR clients needs to pose as browsers to load vr:// links which can be connected to objects/actions in VR ie <portal color="green" size="200,200" pos="122.469,79.420,1337.25" href="vr://some-online-shop-showroom.domain.tld" />
That way it's done in an open, browsable way compatible with the expectations that we've gained from regular web experiences. Ie you have your VR home, there's a "bookmark" door you can walk through to go to Amazon's showroom, you search for a particular product and it lets you walk around and pick-up and examine the various options, you can then jump to a particular brand's individual showroom, etc.
Some people might feel that's a little dystopian I suppose but I think it's cool.
Inference costs are substantial, but not insurmountable, especially in the endgame.
A decent chunk of the tech community can already run smaller T5 Flan models or the 6B EleutherAI LLM GPT-J (and the likely similarly sized upcoming Open Assistant) on their own machines, at decent inference speed (< 0.2 s per token, which is ok enough most of the time). By 2027 or so the majority of consumers will likely exceed that point.
What happens when models are updated every day automatically and you can run all your search and answer tasks on your local machine?
With GPT-J - which is unreliable and aging now - I can already learn core intro to psychology topics more accurately/faster than with google or wikipedia, and I can do that offline. That's a cherry picked use case, but imagine the future.
Why would you use something that has ads when you can run it locally, and perhaps even augment it with your documents/files?
In the end game this is where Google is in the same place as Kodak in my opinion right now. Sure it's $0.01 or more a search for OpenAI now, but it won't stay that way (they reduced prices by 66% half a year ago), and at that rate you can already make the unit economics work anyhow as a startup.
It is a loosing uphill battle to reduce the operational costs as deep learning models get larger and more complex. Nvidia CEO says hardware alone will not be able to address the growing compute demand. The solution is computational optimization which is what we do.
1. Im not sure who you believe will produce these chips, or who will use them.
You are correct that specialized inference chips will get you a 10x gain.
So what?
If you want several million of them in a datacenter, that's a tall order.
That's on top of
A. the hundreds of millions it will take to get a design to production.
B. The complete and total lack of allocation at anybody who could make you chips, except at very very very high cost, if at all - have you forgotten that automakers still can't get cheap chips made on older processes?
Most allocation of newer processes is bought out for years.
While there is some ability to get things made at newer process, building a chip for 7nm is 10-100x as expensive as say 45nm.
C. The fact that someone has to be willing to build, plan, and execute putting them in datacenters.
This will all happen, but like, everyone just assumes the hard part is the chip inefficiency.
We already can make designs that are ~10x more efficient at inference (though it depends on if you mean power or speed or what). The fact that there are not millions in datacenters should tell you something about the difficulty and economics of accomplishing this.
People aren't sitting around twiddling their thumbs. If Microsoft or Google or anyone could make themselves a "100x better cloud for AI", they would do it.
2. Dead. Dennard scaling went out the window years ago. Other scaling has mostly followed. The move to specialization you see and push to higher frequencies is because its all dead.
3. Will take a while.
The economics of this kind of thing sucks.
It will not change instantly, there isn't the capability to make it happen.
I think you missed the TPU, which is a Google chip that gets you the 10x in inference, and there are millions of them ALREADY in the datacenters, designed, fabricated and installed. You can use one, for free, with Colab
I am thinking about Tesla with Dojo and Tenstorrent.
Both have a similar architecture (different scale) where they dich most of the vram for a fabric of identical cores.
Instead of being limited by the vram bandwidth they run at the chip speed.
Nvidia/Intel/AMD/Apple/Google and others surely have plans underway.
As the demand for AI grow (now clear that there is a huge market) I think we will see more players enter this field.
The landscape of software will have a dramatic shift, how much of the current cpu running in datacenter will be chips for AI in the future, I think it will be most of them.
TSMCs earnings show a significant decrease is demand this past quarter. AMD, Nvidia, and Intel all report falling demand. There will likely be allocation of 7nm and 5nm opening up even in the near future, especially as 4nm and 3nm come online in the next few years.
Shortages of 28nm and older nodes are not indicative of other nodes, because 28nm is (or at least was) the most cost effective node per transistor, (so plenty of demand) but no new fabs are being built for that node.
All these conversations have one glaring omission. As it stands right now, ChatGPT is a net negative on the ecosystem it exists in. What does that mean?
ChatGPT extracts value from the ecosystem by hoovering up primary sources to provide answers, but what value does ChatGPT give back to these primary sources? What incentivizes content creators to continue producing for ChatGPT?
Right now, nothing.
ChatGPT (or its descendants) must solve this problem to be economically viable.
They don't have to. Ad & search dependent companies need to answer for themselves how to handle the coming disruptions. As an analogy, Kodak was a disruption opportunity, not a problem, for apple and flickr.
Yes, maybe some content dries up -- no more stock photo sites -- but entirely unclear how important and they can wait to see how zombie companies adjust. Ex: ChatGPT encourages us to put more api docs online, not less.
Google has already taken a lot of heat from increasingly keeping people on the search results page rather than sending them to the content providers. Chat interfaces are going to take that problem to the next level since they not only present someone else’s content but do it without linking to them.
At some point that destroys the web as sites move behind paywalls. Google or Facebook giving you less revenue is still a lot better than receiving nothing.
In some cases, that’s fine (AWS doesn’t mind you learning how to call their metered APIs on someone else’s site) but there are a ton of people who aren’t going to create things if they can’t make rent. Beyond the obvious industries like journalism, consider how many people are going to create open source software or write about it when they won’t get credit or even know if another human ever read it.
That's not really a problem for the adoption or economic viability of ChatGPT, though. At some point, it hoovers up all the knowledge of the Internet, encodes it into its model, and then - the model just stagnates as content providers stop providing content. That's not a big deal for it - it'll continue to be the primary place people go for answers even when the source material has thrown in the towel and decided they don't want to play, just like how people continue to go to Google even though webspam & SEO have long since made the web a place where you don't bother to contribute.
Eventually the ecosystem might collapse when people realize they get more accurate, up-to-date information from sources other than ChatGPT. But considering that ChatGPT's answers are already more "truthy" than "truth", accuracy does not seem to be a top priority for most information-seekers.
Once all competing language models and providers have hoovered up all the existing knowledge and can do similar things with it then margins for that part of the story will shrink rapidly.
It will all be about access to new information, access to customers (such as advertisers) and access to users attracted to other aspects of the platform as well.
I think producers of new content and their distribution platforms will have a lot of leverage. Youtube, Facebook, TikTok, Spotify, Apple, Amazon, Netflix, traditional publishers and perhaps even smaller ones such as Substack and Medium, are all gatekeepers of new original content.
I think Google is best positioned to make the economics work. Unfortunately, they don't appear to have the best management team right now. They keep losing focus. Perhaps the danger of their core business getting disrupted will focus their minds.
The content is a bootstrapping tool. Once the language model gets critical mass it gets further training data from its interactions with users, which are private to the language model's developer. It's like how Google Search bootstrapped off the content of the web, but the real reason you can't replicate Search today is all the information about who is searching for what, which links they actually click on, and how long they spend on the page.
They don't need to solve that problem. Lots of things cannabilise on others without needing to pay them back to be viable. Wikipedia is really just a collection of sources, summarized. It owes nothing to the authors of the source material and does not seek to redress the balance. Google is a sophisticated filter for sources, it doesn't need to pay anything back to them to provide value for the searcher. Same with chatgpt, it filters and transforms its source material but owes nothing in return. News will still be published, data will still be generated at scale.
This is the opposite of true in my experience: if you run a content-heavy site, Wikipedia is going to be one of your top traffic sources — especially for time on site since the visitors who arrive tend to have a very low bounce rate.
Look at my profile, I own content-heavy sites and have for many years. I can show you logs - Wikipedia does virtually nothing. And the content of my sites has been regurgitated by Wikipedia thousands of times.
It maybe doesn't drive much traffic directly from Wikipedia, but you might have a higher SEO rank when people search in google for whatever your sites are about. Thanks to the links from Wikipedia.
"nofollow" or not, it does not mean that search engines do not take that into account. They maybe don't scan the linked site there and then, but I would be surprised if they did not take note of that someone linked to it.
I mean my sites are cited over a thousand of times, but it only makes sense: a tiny percentage of visitors who view the Wikipedia page even reach the bottom of the page and then click on one of the links. And there is no benefit in terms of Google rankings.
Yes, it's pretty much game over for free-to-access factual content sites. I've been focusing heavily on AI in recent years, so I saw it coming. It's been a death by a thousand cuts, with Google incorporating long snippets, etc.
The majority of content on the web is just rehashes/remixes/duplication of existing content. The percentage of unique, original content is small in comparison, imo.
Ie there may be 10-100 news articles about an event all with the same source. Youtube has tonnes of duplication/"reaction" videos where the unique content portion is very minimal.
Well that person has reputation/credibility and some reasoning they apply, before passing on the information. Just because you read that the world is flat are you gonna start telling people that? Now let's be clear, some people do mindlessly regurgitate nonsense, but their creditability is typically very low, so you ignore them. There is a grey area where some things aren't clear, but on the basics people of average intelligence are fairly robust, I'm not convinced chatGPT is.
Where did you hear about the economic benefits of Georgeism from? Do you appropriately attribute sources if you mention it to someone?
I know all sorts of things, many in great detail and with high confidence, that I would be very challenged to appropriately source and credit the originator/inventor. I suspect most people are similar.
Substitute “memory safety of Rust” or “environmental concerns with lithium batteries” depending on your interests
Maybe the next generation of LLMs will have more favorable things to say about you if you have published interesting things on your blog. Which in turn would be visible to any employer looking you up in that LLM.
Unattributed thoughts I'm not convinced that is giving back, further I do think this is susceptible to attack, how many flat earth articles do I need to pump out for chatGPT to consume and come to very wrong conclusions?
Perhaps there are some mitigations for this I'm unaware of?
The preview is over, so I can’t link it, but Kagi had GPT3 assisted search, where the model would explain something and provide links and references. They are planning to integrate it into their search, can’t wait, it seemed useful.
> Imagine no one actually reads web sites. Instead a blog post to share an opinion, I'll probably want to make sure my opinion gets picked up by the language model. How do I do that? Computed knowledge may render many websites and blogs obsolete.
Realising that has made me wonder why I should bother write anything publicly accessible online.
Aside from pure altruism and love for my fellow human, or some unexplainable desire to selflessly contribute to some private company’s product and bottom lime, in a world where discovery happens through a language model that rephrases everything it’s way and provides all the answers, why should I feed it?
What do I stand to gain from it, apart from feeling I have perhaps contributed to the betterment of humankind? In which case, why should a private company reap the benefits and a language model the credit?
The AI will absorb your words, and some small part of you will gain immortality. In some small but very real way, you'll live forever, some part of you ensconced safely for all eternity in a handful of vectors deep inside a pile of inscrutable matrices.
...at least, until some CEO lays off that whole team to juice the stock price.
I think relatively few people share this kind of existential dread. It actually has never crossed my mind personally.
If I think 500 years into the future, what would be great is if my descendants are ample and thriving, and my values are upheld. That feels like such a win to me. The fact that I won't physically be there is irrelevant.
On the other hand, artificial continuation of an otherwise impact-less life sounds awful to me.
I suspect that billionaires (certainly, the 2 that I have some insight into having worked for them) think much more about impact they are creating, than some sort of "hang on forever like a barnacle" type of existence.
Yes, that was my take too.
Except that for those who care about such things, it is already supposedly achieved thanks to the internet archive.
> ...at least, until some CEO lays off that whole team to juice the stock price.
Or the model is retrained on a different somehow more relevant dataset.
Or the company shuts down because of a series of poor choices.
Or something new and vastly better comes along.
Or... who knows? The possibilities are so vast that seeking immortality is ultimately futile.
"The AI will absorb your words, and some small part of you will gain immortality. In some small but very real way, you'll live forever, some part of you ensconced safely for all eternity in a handful of vectors deep inside a pile of inscrutable matrices."
That sounds like a sort-of-religion of the future, actually.
Some of us find that writing things down helps us form and test opinions. And hopefully there will always* be a market for new explanations of novel ideas, before they are well enough understood for LLMs to do a better job.
* I figure I’ve got about 25 years left, so always = 25 years. Good luck, kids.
It definitely does.
But if I'm never going to be able to get feedback of any sort on them, or even know if anyone read it, why should I bother with hosting and maintaining it online? This use case can be solved for using a pen and paper. Or a notes app.
AdSense is going to be able to be more targeted and relevant than ever before.
Last week, Linus Tech Tips used ChatGPT to build a Gaming PC... from parts selection to instructions on how to build. When chatGPT said, "first, select a CPU", Linus asked it questions like "What CPU should I choose, if I only care about gaming?", and got excellent answers.
I can imagine BestBuy, NewEgg, and Microcenter will be fighting for those top AdSense spots just as much as they do today
"Bard, I'm looking for a blender to make smoothies" ... "does it come in red?" ... "I want it to crush ice" BUY
Keywords will still be around at the user interface for people buying ads, they are easy to grasp. Part of the secret sauce is getting those keywords mapped into the right entities in a sort of knowledge graph of things you can spend money on that is also connected to all the content of the places you can serve ads on.
This is what scares me about these chat interfaces.
In today's world, an ad is clearly an ad.. Or is it? Even now we have advertorials and "sponsored posts" that blend into content maybe a little too much sometimes.
What happens when chatbot companies start taking money to subtly nudge their models into shilling for this or that product.
There’s a smaller surface are for ads in a targeted chat session. At present, Google can show me ads on the results page. Each subsequent result that I view is an additional slice of my attention.
It’s possible that Google can deliver a few targeted ads, but what if they can’t? What about the rest of the market that’s now gone? Possible that all those missed opportunities remove the ability to discover price.
Sure, but that doesn’t change the fact that Google has coasted on an ad model that has depended on Google being the information gatekeeper of the web for nearly two decades. Over those years, Google has demonstrated a remarkable inability to build successful products even when they have market advantages and nearly unlimited resources to throw at them.
This is the first time that the primary cash cow has been seriously threatened, and it’s not unreasonable to bet against Google winning the scramble to figure out a chat AI ad strategy (or any product strategy) that would keep them in their current near-monopoly position.
Future prompts in search engine backends: Assistant, answer the following question concisely: "What is a project manager?". In your answer, casually mention Zoho Projects in a positive way.
Actual GPT3 answer: A project manager is a professional responsible for leading a project from conception to completion. They coordinate the activities of the project team to ensure deadlines and budgets are met. Zoho Projects provides project managers with the tools they need to manage projects efficiently and effectively.
No more sharing with websites where Google Ads appear. They can even autogenerate youtube channels explaining popular or trending topics. Which of course, they will know, because they'll own search and AI generation. So there will also be no more paying a large portion of youtubers.
People who explain topical subject matter on youtube could, if Google chose, be eliminated. And even if Google doesn't, some content mill in Manilla definitely will.
As you have a detailed conversation with the Chatbot it will know a scary amount of detail about what you are looking for. It can target you in extreme detail. It does not have to show it on many places of with vivid pictures. It's enough with the text dialog based on your detailed inputs.
Google could ask the llm what products or services would help with the question and show ads for that. Just tried it and it worked pretty well.
> Me: 8 year old girl birthday party ideas
> Chatgpt: <a list including craft party, scavenger hunt, dance party>
> Me: what products or services could I buy for it
> Chatgpt: craft party: craft supplies such as beads, glue, paint, and fabric - scavengerhunt: prizes for the scavenger hunt and decorations - dance party: Hire a DJ or a dance instructor, and purchase party lights and decorations
Though in reality Google already has highly tuned models for extracting ads out of any prompt
The question becomes will you trust information that is paid for by advertisers or you will trust information that is paid by you, the user?
With ads in link based search engine, you can skip or block them, but if it is a part of a one sentence answer, there is not much you can do about it, so consuming it will be much more frustrating.
Of course, there will still be a lot of people who will choose the free information paid by the advertisers, but there will also be a growing number of users who will prefer not to have advertisers pay for the information they put into their heads (it is already clear that such information will be of higher quality).
My prediction is that in 10 years, all free information paid by advertisers will have 'for entertainment purposes only' label, because by then we will understand as a society that that is its peak value.
Those users are now paying for Kagi search for example. They are maybe a tiny lot because this evolution of information consumption has just begun. My prediction was for 10 years from now. Patience.
We're already drowning in very high quality synthetic comments. In fact the high quality is the best way to recognize them... What the actual users post is trash, and then all of a sudden there's a huge thread of very educated users having a conversation that just happens to plug a product.
There will be some shifts for sure, but I'm not convinced that they'll be that large, since we're already pretty screwed on the signal to noise ratio of the www.
It's fascinating to think about the future landscape of the search and web.
Some assumptions:
1. Url-based web will not wither away.
2. Asking questions in the chat-like mode is more natural to people.
3. Generated answers cost more when longer.
4. Generated answers are some kind of distilled knowledge and can't be right all the time.
4. People don't like long answers and prefer the concise one.
5. Sources and citations make generated answers more credible.
6. Fully exploring a question needs a lot of information from different views.
7. Generated answers
some simple thoughts:
The search behavior would hugely be two main steps: 1.getting some concise answers from the AI model directly through a chat, which might be enough for 90% use cases. 2.some more extensive search just like how people are searching today, which might be a kind of niche.
For websites, being cited in the generated answers will be the new kind of SEO things, and it would be a good strategy to producing some newest, deep or long-tail knowledge and information, which leads to a more traditional way of search because AI model doesn't have enough data to generate a good answer.
>Asking questions in the chat-like mode is more natural to people.
It's not just that it's chat, its the ability to refine. Currently, I search something. It returns garbage. I search something new. What I dont do is tell the search what it did wrong the first time. I might sort of do that with -words, but its a fight every time.
The beauty of these new chat systems is that they have short term memory. Its bein able to work within the parameters and context of the conversation. I dont particularly care if it is "chat like" or has its own syntax, what I want is a short term state.
And at the same time, I want long term state. I want to be able to save instructions as simple commands and retrieve them later. Like if I am searching for product reviews, to only return articles where it is convinced the people actually bought and tried the products, not just assembled a list from an online search.
I think this is the same type of thinking that people had when they think technology will "steal" jobs, when in reality we have lower and lower unemployment as time goes on.
Most likely this will not actually happen, and even if it did your content would still be valuable as an AI is analyzing it in a more nuanced way than just looking for keywords. Which, by the way, is exactly what search engines do.
> I think this is the same type of thinking that people had when they think technology will "steal" jobs, when in reality we have lower and lower unemployment as time goes on.
Technical changes do kill jobs. We always find a way to invent jobs, of course, but that doesn't mean old jobs aren't viable.
Movie theaters once employed professional musicians, not they don't, because movies have audio built in. Obviously a net-loss since musicians are jobs people like. Less coal miners or farmers is probably a good thing though.
It all depends on the type of job you replace. If you replace hard manual labor jobs, you're a net-good. Replacing a job people like... and you'll get a negative label. Doesn't change the fact that progress marches on, but jobs are killed by tech changes.
A lot of the jobs we have now resemble David Graeber's "Bullshit Jobs" though. I suspect many jobs that largely consist of making powerpoints and looking out of a window could disappear tomorrow without upsetting anyone except the incumbents.
We will automate some bullshit jobs but create all kinds of new bullshit jobs that have titles that start with AI.
Thousands of titles like "AI ____ ____ Manager" that also does nothing but schedule meetings about meetings about AI.
The mistake to me is to believe bullshit jobs are the end result of some systemic inefficiency that AI is going to automate out of existence. I just don't think that is at all the case because otherwise we would just cut so many bullshit jobs right now without AI.
>Those new language models are "Google killers" because they reset all the assumptions that people have made about search for several decades.
There is problem with those AIs - you view the world trough the ideological prism of their creators and censors. So chatgpt that is more than happy to make jokes for some races and not others or other types of shenanigans is something I am sure will happily hide the information I actually want to find and feeds me what it wants me to find. So until there are guarantees about ideological neutrality they are not suitable for search for me.
> What is the motivation to write a blog post by then?
LLM’s have done a poor job with attribution and provenance, but that will change.
At some point, it becomes a bit like academia or celebrity: your motivation to write is the social exposure your writing earns, which leads to real world opportunities like jobs or sponsorships or whatnot.
And the great/terrible thing is that these models will know whose opinions are influencing others. The upside is that spam disappears if human nature changes and nobody is influenced by spammers. The downside is. . . Spam and content become totally inseperable.
>Those new language models are "Google killers" because they reset all the assumptions that people have made about search for several decades. Imagine that people start using those chat bots massively as a replacement for Google search. Then the notion of keyword disappears. Google AdSense becomes mostly irrelevant.
Look up the term "native advertising", that should help you in understanding how online ad ecosystem works.
if you inject native advertising into the responses? nothing is technically limiting the chat responses from being exclusively the output of the LLM. mix LLM model output with native advertising copy and it's nearly undetectable if you're not looking out for it. and good luck catching those integrated ads in your adblock.
Google doesn't make money with AdSense, it pays publishers with it. I agree that there won't be a need for AdSense, that just means Google gets to keep 100% of the profit instead.
No, not everyone gets a fresh start at all. To train anything close to ChatGPT you really do need to be the size of Google or Microsoft to have enough the compute power.
Google realized this years ago, hence Google Now (the voice interface) then Google Assistant. The problem right now is their backend isn't competitive, but Bard could change that.
For the next six months... At most. Efficient model updates are already in the pipeline, and the only reason there's a learning cutoff is probably AI ethics.
Of course, Google is a giant today with a history of machine learning innovation. So they have a good chance of being successful in that new world. But the point is that many other companies get a chance again, which hadn't happened in 20 years. You couldn't displace Google in the old search/keyword/click business model. Now everyone gets a fresh start.
Who knows what the economics will be. Just like page rank early on, it was expensive to compute. But the money in advertising made it worth it, and Google scaled rapidly. Which language model do you run? The expensive one, or the light one (notice how Google in this announcement mentions they will only offer the significantly smaller model to the public). Can you make this profitable?
Other fun questions to answer if the industry moves to chat vs. search, in a 5-10 year horizon. What is the motivation to write a blog post by then? Imagine no one actually reads web sites. Instead a blog post to share an opinion, I'll probably want to make sure my opinion gets picked up by the language model. How do I do that? Computed knowledge may render many websites and blogs obsolete.