SearchGPT, from Naptime to Big Sleep, and GitHub Octoverse updates

does the rise of AI mean that there will be more or fewer software engineers in the future Chris Haye is a distinguished engineer and CTO for customer transformation Chris welcome to the show what do you think a billion software Engineers by 2027 wow 2027 okay uh Farney is a senior partner Consulting on AI for US Canada and Latin America uh what's your thought everybody will go from becoming a programmer to being a pro at grammar I will ask you to explain that more in just a moment Kar El mcra is a principal research scientist and manager at the AI Hardware Center uh Kar welcome um what do you think I think it's going to be a different breed of software Engineers that we will be seeing all that and more on today's mixture of [Music] experts I'm Tim hang and welcome to mixture of experts each weeke brings you the analysis debate and banter that you need to stay ahead of the biggest developments in artificial intelligence today we're going to cover for cyber security and the launch of search gbt but first let's talk about software engineering um there's a fascinating uh blog post that came out from GitHub uh the other week um basically reporting out some data that from their perch github's reporting that there appears to be a rising number of developers um uh driven largely by tools like co-pilot um and second they also point out that Python's incredibly uh becoming a really really popular language driven largely bu data science and machine learning applications and this is super interesting to me and this is one of the reasons I wanted to bring it up as our first uh story of the day which is had you asked me I would have said look where code assistance is going we're g to eventually just replace all the software Engineers there's going to be no wor no more software engineers in about a decade um and maybe Chris I'll toss it to you first because your prediction is that if anything we're GNA have way way more software engineers in I think 2027 right so literally like 24 months from now um why do you think that I think if for two reasons number one is with code assistant being everywhere and with things like jet gbt large language models pretty much in the everyday person's hands everybody can become a coder so you don't need to go and pay money to go and get somebody to do that you can literally have a go yourself and I think that is just going to open up this sort of democratization of coding that we've all kind of hoped for and I think more tools will come in like uh you remember scratch from kind of MIT then I think we're going to see more of that style side of things and everybody is going to become a coder the other one is you didn't say in your question Tim whether they had to be humans did you so the carbons and the silicons and there's going to be a whole bunch of silicon coders to match us carbons so uh when I multiply that up by 2027 there's going to be a billion buddy okay all right that's really interesting yeah I guess kind of what we're talking a little bit about is almost like that the I guess the question is whether or not like the the the job coder or the category code software engineer is really going to make sense in the future like it almost feels like no one's like Oh I'm a word processor right like kind of everybody knows how to write um show I know your response seemed to kind of suggest that you think some of the some of the skills you'll need are going to have to change yes absolutely I think uh all of us will become pro at uh writing good grammar and the way you ask a question and how you describe what you want to get done uh it's the a good technical PM does a really good job at explaining what exactly they need so that the developer can go and execute the code to the vision of what the PM had right so I think that's going that's going to shift quite a bit let me just spend a minute on just appreciating how far GitHub has come we just you just refer to their annual report that talks about the GitHub all the numbers uh last week we were at the at their Pig GitHub Universe uh event and this is where we as IBM sponsored that as well just to give you a sense of how far they have come GitHub is the world's biggest repository like 90 90% plus of all Fortune companies use it 98% developers what not we're at about what 100 million developers Plus on GitHub today Chris not quite at the billing that you want there to be but in the last like N9 10 years now this 10th year they've been running this they've had like what six like close to 70 million GitHub issues people have solved like 100 like almost 200 GitHub polls like 300 million plus projects and whatnot right the way I look at it open source is is the biggest team sport on Earth it's not soccer it's not football it's open source as the biggest team sport right it has been crazy growing so when you hear from from Tom the CEO of of GitHub they're giving you actual stats of what they're seeing with people developing more and more and he's very true right to to say that AI the threshold of creating code for AI engaging with guub repositories and trying it out downloading it contributing back to it IBM has been a big proponent of having a very open community had a really good relationship with GitHub and now that the GitHub is opening up quite a bit it has Cloud models and Google models that can be leveraged in addition to all the open AI models I think this is just an Unstoppable Force right now in the industry and more and more programmers will have access to tools that we just could not imagine we had a couple years back yeah I think one of the most interesting things in the report that they did was also that it seems like the geography of software engineering is changing right that there's like a lot more coders from they're seeing from the global South come online on GitHub um I guess sh do you think that's related to code assistant or I'm kind of curious about how you feel see like the role of these assistants in even potentially kind of like broadening like the geographic scope of of who gets to be a software engineer yes so I I spent a lot of time with Latin America clients as well u in Americas and I see a lot of centers developing where all of a sudden the threshold of being able to have economic benefit in the region has dis plemented so people can go create code and go contribute to to other other locations other countries and increasingly so a lot of my clients are starting to build their Latin American presence the time zone helps in the US as well but just the access to tools and being able to create in every language right now now I have an opportunity to know Portuguese and Chile and be able to code and get some assistance in Portuguese while I'm creating code right that did not exist earlier so the barriers have come down significantly and you see a a higher threshold this is one additional thing I would I would add to this we should not we should also look at the way energy uh movements happen across the the world right if you look at countries like Chile or Latin America there's a lot of energy that's been created there and you want the AI models to be trained closer to where there's energy because energy consumption is going to be so much I would anticipate more pull towards Latin America or centers where there's energy production in Surplus it used to take a lot to move that energy from Latin America to say serve customers in the US now the AI models will be will be created closer to where the energy sources are counter I want to kind of turn to you is you know building on what chit just talked a little bit about is that um you know I think when you responded to the opening question you said well it's going to be more about like asking the right questions um and I think that's like one interesting is like item here to kind of pull on one thread to pull on is maybe actually in the future it's actually we're going to have a lot more technical PMS than we really will have software Engineers because it feels like the role that people are increasingly having is they're kind of managing this agent that does the coding not really doing software engineering themselves and I guess kind of I'm curious if like the right way to think about this actually is we're going to just have a lot more PMS in the future yeah I see of course you know the the skills will be changing shifting and this for example co-pilot what it's doing is deifying coding for people without uh formal training uh turning more people into kind of Citizen developers so this means that professionals from diverse Fields such as data analysis design Finance uh Healthcare Etc can now use code to build custom tools without extensive studying or training or syntax Etc and this kind of heading towards a word where basic coding becomes as common as using spreadsheets or even presentation software so just learning it's kind of they're trying to be prompt Engineers also but specifically designing good prompt for software engineering so I think it's also time to start reimagining what's the right developer workflows here for experienced coders AI can handle repetitive Tas letting them focus on higher order problem solving for example and this might alter the skills expected in software developments with these with coding transitioning more from syntax heavy work to strategic thinking and Architectural design so I think those really would be good skills to start acquiring not really focusing on the syntax but more on how do you build systems how do you design systems how do you put them together and then using the C Pilots to help do the syntax work I think this also could have implications even for Education right now curriculums they focus a lot on syntax and uh so if AI can assist with coding should really Educational Systems shift from focusing on syntax to broader problem solving or even collaborative design especially as mentioned open source it is actually the the biggest team sport right now so I think acquiring those skills how do you collaborate you know do all these things uh do PRS and learn how to work in a team going to become a really important skills in the future so I think the traditional computer science curriculum really need to adapt emphasizing creativity ethical coding practices Advanced debugging and also collaborative coding yeah for sure and I think Chris I mean it kind of puts a tough question to you I mean your title is distinguished engineer uh so you spent a lot of time getting really good at the software stuff right um but uh you know I think if a kid approached me today and say should I be a software engineer should I just tell them not to like it kind of feels like where we're headed is like is there any more value in actually learning how to code anymore right I think is the question I want to put to you no they should go and play soccer ball or something like that you know yeah yeah no no I'm no I think so I think the question I would say is what happens when it goes wrong right so if if we really think about history of software programming right it's you're kind of back in the kind of the Punch Cards and the ones and zeros and then the Assembly Language came across and then you know and then see I mean there was a whole bunch of other languages for try Etc but then it really kind of took off I would say from the kind of C onwards which was which is very close to Assembly Language and then the abstractions got higher up and now we're at python Etc and then you know now got rust blah blah blah so the number of languages are increasing but it's abstraction layer after abstraction layer after abstraction layer we've went from Hardcore kind of Punch Cards to assembly to lowlevel languages to garbage collected languages to higher level languages blah blah blah blah and again all I would say that's happening here is we're moving to another level of abstraction and that level of abstraction is natural language um I think it will be better because with agents we'll have tools Etc but you're still going to want to know the fundamentals because what happens when you get a bug and and it can't fix it are you are you going to be like the Homer Simpson you're just going to be hitting the keyboard G try again try again try again or or you're going to have to go oh my God I'm going to have to I'm going to have to use my brain how dare you make me use my brain so I think I think the fundamentals are still going to be there I see this becoming a higher level of abstraction now don't get me wrong if the models become good enough at some point then there may be a different abstraction where models may have their more native language Etc and that that's a whole different discussion but I think I I see this as an abstraction because we need we need to explainability we need the reasoning somebody's going to have to maintain this and look at it and you can't be fully dependent on the AI um I do want to address one thing though Tim on that GitHub report like Python and we mentioned python there being we didn't talk about that aspect all that much so yeah yeah python being the most popular language I just want to point out one thing right and I love all languages I love python but when number two and three are typescript and JavaScript which are effectively the same language my friends and more JavaScript like people are becoming typescript people you know if you add the two things together who's number one again I mean yeah I had the same reaction I mean I'm I am a python die hard but I do feel like that was a little a little bit funny in the counting if I might add I think there are also some risks here uh there are potential risk for AI created code especially as more code is generated by AI quality control becomes a concern here how do we ensure AI generated code is secure efficient maintainable so there is also the risk of overreliance On Tools like co-pilot which could lead to a drop in fundamental coding skills among you know the new programmers so of course you know there are lots of advantages here in terms of democratizing having more developers uh lowering the bar of entry and things like that but we we shouldn't also ignore the risks that will come with this especially around quality assurance control ethical consideration security and also when things fail so can we ensure that we have skilled programmers or people like Chris hey mentioned that no bug and figure out what's going wrong or we will have less skilled people in those fields so what's the right balance here yeah I think it's always going to be this tricky balance between kind of you know democratizing making it accessible making it usable and then kind of like the Reliance on these abstractions um my mom who was like a a coder when she was before her retirement has a story about like in her early days like carrying a bunch of Punch Cards to the computer and then like dropping the Punch Cards everywhere and it basically like and her having a good enough sense of the program to basically reassemble the program like physically by the cards and I was like that is like a level of diligence that like modern Engineers just would not be able to accomplish so but obviously we are happy that we've moved past the punch card era for sure I'm going to move us on to our next topic uh there was a great and very interesting story that kind of follows on I would say a sequence of stories we've had on Moe for the last few weeks which is thinking a little bit about the application of AI and specifically kind of agents to the computer security space um so Google did a blog post from their security project project zero that basically reported that they have a cyber security agent called Big Sleep um that was able to find a vulner ability in SQL light which if you're not familiar is one of the most widely used kind of database engines out there and this is a really interesting story because at least by their accounting this is kind of one of the first instances in which an agent was able to find sort of a genuine vulnerability in the wild in a code base that is kind of like widely used and so in some ways it's almost kind of like a a real kind of hello world demonstration that we might one day be able to use these agents for uh identifying um real world vulnerabilities and making our systems um safer and so I guess maybe Chris I'll I'll kick it to you to kind of kick us off on this topic but you know I think the first thing I think a little bit about is is this the beginning of just kind of a new era like we will just start to see agents play a bigger and bigger role in making systems more robuster is this still kind of in the realm you think of like the toy project right like we're still going to be a few years off before we we live in that world no I I think we're already in that world I and there's a couple of things about the big sleep thing the first thing is if you give agents access to tools and then you get them to follow patterns um then the agents are going to do a pretty good job so if you think of cyber security you know go fix me this bug go identify this pattern go find me what ports are open and on a firewall these are all things that agents can do today now if we look at the big sleep one and I and I do want to caution this because when I read the paper there the thing that they did is they took an existing vulnerability that existed on on that code base and then they got the agent to go search the PRS and say hey go find me another vulnerability of this style that matches this pattern um that wouldn't have been patch yet and then it went and found that so as much as it's like by my understanding as much as um the agent discovered a vulnerability on its own at the same time it's kind of pattern matching and was prompted and directed to go and find a bug of that similarity and and that is completely within today's technology um you know agents and models are really good at pattern matching and if you give them access a large enough codebase by tools Etc you access to PRS and the commits they're they're going to be able to do that um are they quite at the stage of being able to find a whole new class of vulnerability that is completely undiscovered and not prompt and patterned in itself I don't know yet I think we're maybe a little bit off that but I don't think we're too far away from it yeah pretty interesting cter Maybe uh to bring it to you next I mean because you think a little bit about the kind of risks around all these Technologies uh you know it seems to me right like that like you're going to use this for security but also like the bad guys will get access to these agents in well and it seems like very straightforward to be like I I have this vulnerability find it elsewhere in this code base is also exactly the kind of same thing you need to do if you were going to sort of harm these systems um curious about how you see that kind of cat and mouse game playing out like does the defense have the advantage right now do you think the the offense is eventually going to have the advantage kind of just what that balance looks like um as these systems become more sophisticated yeah that's a very good point of course as big sleep or other similar system they're strengthening defense with AI agents so they're revolutionizing vulnerability testing allowing continuous autonomous scanning that adapts to new threats and this this is especially beneficial in complex systems or complex environments like for example Cloud infrastructures where we're doing all these manual monitoring is very inefficient and security teams could be empowered and act faster on these emerging vulnerabilities and reducing the attack window however at the same time there's also this threat of offensive AI so Aid driven security tools can also be a weapon in the wrong hands just as Defenders can use AI to preemptively catch vulnerabilities attackers could also use similar tools to identify exploits at scale so this creates this potential AI like he said arms rate in cyber security where the line between defense and offense is very thin yeah I think what's so interesting about it is it also suggests kind of eventually we're going to see a whole kind of dark criminal ecosystem which kind of M mirrors the the kind of one that we have publicly like that there will be basically like a a criminal Lambda Labs right where you can like kind of run all these agents um you know completely free and and for criminal purposes um and it'll be really interesting to see how that kind of ecosystem evolves because you know people who want to use these agents for bad purposes will sort of need the same infrastructure that you know the the people doing cyber security are are engaged in yeah so I think that's why maybe some ethical and Regulatory here challenges are will will need to be resolved you know with this rapid development of AI Bas security there is this call for framework to ensures also responsible users how do you protect these infrastructures and tools uh so government for example and government uh cyber cyber Security Experts they need to be tasked with creating also ethical guidelines and regulations to balance the benefits of things like big big sleep with its potential misuse also yeah um let me give you a client U perspective on this we do a lot of work with our clients on cyber security we have a whole Security Services team with an i Consulting it's been doing an exceptional job with clients we also partner very heavily with our partners like Palo Alto to do a lot of cyber security work with them and we leveraging generative AI models and AI models quite heavily in that partnership as well U there it's a two-way street it is AI helping drive better security and as the reverse how do you secure the AI models themselves right if you look at the three different steps that our clients go through the securing the actual data that went into the models securing the model itself from cyber attacks and then the usage itself how do you prevent misuse of the model when it's in production right so there's across all these three different buckets we've done quite a bit of work in creating AI models that prevent and detect and can can counter the serial imp attacks and things of that nature we had recently released our Granite series of models Granite 3.0 uh if there's a there are a lot of public benchmarks and we have some private IBM benchmarks as well where every model that we are putting into production we have the ability to go test them across all these different uh attack patterns and stuff right and uh if you look at that that small class of models which are roughly 2 to8 billion parameter models we do a really good job at across all those different seven eight different criteria the granite model scored higher than say the llama and the Mel and a few other models as well then on the on securing the actual usage every time you're talking to a model and you're you're bringing data out for the model both input and outputs get filtered so I'm much more confident in 2024 November when we put models in production there enough safety guard rails from IBM and other ecosystem partners that that we can start to address these fairly well yeah that's great and there's one subtlety here that I think is worth diving a little bit more into show but if you want to speak to it is you know with big sleep you're basically having like an agent like an AI model examine sort of traditional if you will software uh code um and it strikes me that there's a whole separate set of questions about how you could use models to analyze the security of models right yes um because I think obviously where all this goes is that like once you do security on agents it's the security of your security agent that becomes important curious if you can talk a little bit about like how the thinking around that is evolving because it feels like the pattern matching of oh here's a vulnerability and code that we're finding elsewhere looks a little bit different from how you might use a model to evaluate the security or safety of of a model yes and uh I've been really excited about the work the collectively the a community has done in the space um outside of Google we've had some amazing work done by Nvidia meta IBM research on creating these models that can detect vulnerabilities right so we do that at scale there's a pattern recognition on the logs that's coming out there is vulnerability on what are the corner cases you can now start to create infinite possible combinations of how you could break a particular model and you can stress test them in real time right so I think we we're doing a good job as a community on sharing those techniques as well a lot of the work in the space has been very open source so you can start to to to compare different models different benchmarks private and public that people are leveraging to test these vulnerabilities of software code um I think over time uh there's there's a recent paper that came on uh comparing even the llm judge how do you judge the llm judge right so there's a lot of this like starts to get thinking about very meta and and the there AI That's monitoring AI but I think we are just moving the the bar of what does a human do versus what does an AI do so if you think about the way we uh employ people into our organizations we would have somebody who's a graduate from an amazing school with multiple degrees just like a really nice llm and we're giving them some few short learning some examples during training saying that here's how we do this thing in our company then you'll give them access to all the other vulnerabilities and all the other things right they are in real time reading up on a new vulnerability that happens in a particular environment and then trying to think how will that impact their own code so we're starting to to Crunch through some of those steps that a human would have done and if you think about this as bring a new graduate hire from an institution like MIT or Stanford into your organization for cyber security that's the exact same pattern that we are following with LMS as well yeah that kind of human metaphor of how we train cyber Security Experts uh and applying that to the model is is interesting and I think lands on maybe the final question I had for this segment which is Chris if I can ask you to make another wild prediction for this episode um is you know it feels like the threshold the badge of honor if you're a security person is like you you disclosed a really novel kind of exploit at Defcon um and I guess I'm kind of curious if like you think that like agents will eventually pull that off and if so you have an over-under on like the year is it is it 2027 when we're going to have a billion Engineers or how far off is that in 2020 this is my prediction AI agents will reveal the first human vulnerability in code and therefore they will say this person here is a human vulnerability and they're doing bad things so that's my prediction 2028 it's going to be the other way around AI agents predicting human vulnerabilities interesting yeah I would love if the agent finds out a way like this is the new method for social engineering would be actually in some ways like very perfect I think also what's going to be interesting is as AI finds our security flows faster than ever the real question is who's quicker Defenders patching them or attackers ready to exploit them no that' be really funny to see and the human vulnerability part Chris you just mentioned we're doing this for one of the big Latin American Banks right now where we're leveraging some social engineering techniques and stuff the emails that you create for social engineering attacks is just looks so plausible right llms are really good at creating convincing content and you can trick and let the whole like click baiting people to go uh into a into a rabbit hole that's working out really well but it's really nice some of our clients are saying that hey U I'm not quite sure about putting AI in production our security teams won't give us the green check let's go pilot llms for security team first if they're convinced and they put it to production then they don't have an excuse to to bottleneck the rest of the organizations it's been a good good method working with lawyers and cyber security teams in these large organizations yeah it's going to be so hard when you like try to log into work and it's like you've been locked out cuz you're just too gullible like we've assessed that you can't make it here just like okay it's coming 20128 you heard it here [Music] first for our final segment I want to talk a little bit about search gbt so it goes without saying that open AI is the the heavy in the industry the the big leader everybody's been waiting on their features and what they release and one thing that everybody's been waiting on for a long time is for them to finally get into to the search space um and long anticipated but it finally launched um and now open AI now has a search GPT feature um and this enters a market that's been kind of dominated and competed over by you know companies like perplexity and of course you know Google uh through Gemini really wants to get into this space as well and so this is a big move right the the big industry leader has finally kind of put its marker down for what it wants to do in Search and I know show you looked into this you know the question I always come to it is like does this mean that perplexity is doomed like is everybody doomed now that open AI is in the space um and kind of curious about what you think the effect on the Market's going to look like so I I recently posted on LinkedIn saying that uh after I've I've had access to GD search for a while u i I pay for a whole I'm very gullible in paying 20 bucks a month to try out all kinds of AI so I've been I've been a paid subcriber for a while and I was lucky enough to get access to it U I was comparing it the closest competitor would be something like Gemini search right and then it'll be things like perplexity right so I think if I did a side by side comparison I have like 13 different areas of topics that I compared GT search versus uh Google Gemini and overall I don't think I'm going to be switching my search from perplexity and Google and Gemini over to uh gbt search quite yet and there are a few things that I found when I was comparing them one by one just to just summarize this have a whole article giving you visual side by sides but Google generally is a lot more visual they've learned learned from years of ux what's the best way to represent the information for the user right so for example if you're suggesting restaurants if I ask gbt search to find restaurants in a particular location versus Google Gemini Google Gemini understands that it's logical to put a map and pinpoint all the restaurants in the response that I'm giving you right so itance the right ux and people would want to go interact with the graphic and see which one is closest and so on so forth right similarly if you're talking about weather it makes sense and for last few years Google has had a really nice on the very top and tells you exactly what I looking for right the one thing that I'm I still need to uh that GP needs to address is they have have a prolification of different capabilities they're not quite combined into a single UI yet so as an example when I switch over to web search I lose the ability to upload any content I can't give it any attachments I can't use any function calling things of that nature that I'm very used to when I'm using my o1 previews or my my four O's right versus in the Gemini world Google Gemini they they fig out what I'm looking for right so the simplest example would be if I'm standing in front of a monument or some place some Landmark I take a picture I said can you find me restaurants around this right now obviously Google Gemini will identify the place very high accuracy it will give me nice recommendations and it help me fine tune it chat gbt's gbt search cannot take uh attachment so it can't take any iary it can't do things like uh if I give you a document and say Here's my here are the people that I'm looking for go on link in and scrap scrape something for them it can't act it doesn't have access to function calling I can't give you documents right so there are certain things that are like absolutely missing uh on the GPT side I I think that the last the last piece that is going for Gemini which uh is is uh which is still why I favor Gemini Google is the connection to your personal data I've been a big Google like my email address is show with the Gmail I got that like when they were starting at the very very beginning right so we uh all my data my photos my calendars and stuff like that are inside of Gmail so when I ask about hey can I find restaurants near the uh near the hotel I'm staying in in Mexico it'll be able to go find that really quick it's very very personalized you can go search with my permission of course it can go and look into my emails and things of that nature that has a huge value add to me yeah that's so interesting is basically that you know I think way maybe one way of thinking about this competition is like how much is search about like the form of the results versus like the substance of the results right which is kind of show what you're saying is like oh when you ask for a restaurant it's great to have like the map and the pins and like all the stuff that Google has indexed even though the response might be like less conversationally well flavored than like what you might get out of perplexity or or something like that just to counter maybe some of the arguments that mentioned of course having that personalization is so important having access to all of that and I think Google has perfected many of these features given its long history with search but don't you see as GPT is acquiring also more multimodality uh features and as people more people are using uh chat GPT or search GPT that personalization will come uh along you know they they'll acquire more personal data they can customize also things so I think it's just maybe a catchup game here uh one thing also that I find nice in SE GPT that I still don't see in Google search is that interactive nature um the way they basically it's more conversational search so unlike traditional search where they give you like a bunch of links that you have to click through this this is making search more intuitive particularly for complex queries or ongoing project users might not longer need to click through a list of links as the model delivers synthesized responses so coner I will push back on that a bit if I may I think it's it's unfair it's apples or oranges if you're comparing gbt search with the classic Google search the right uh comparison would be Gemini search with Google right so Google's Gemini is multimodal like I said earlier I can take pictures and things of that nature it is personalized you can tap into your Google your Gmail and stuff if needed I can take I can take images and so on so forth right so I don't Google understands acknowledges that the Blue Link world is dying their Gemini Google search I think is an incredible product it works really really well and they're trying their best to make sure that within the conservative boundaries of what they can do being such a large company personalizing hyp personalizing and multimodality and things of that nature looking at very very long videos and summarizing it like things like that I think they have a very good mode but the the true comparison is not Google search Blue Links with GPT search like a lot of people in media are comparing the two together and I feel that it's it's unfair to Google I agree with you it's not a fair comparison yes and I think the question here are we moving towards this one model to rule them all scenario for search or it's going to be a competition so but we always had one more model to rule them all with Google because they had such a massive 95% Plus Market right so I I think people are is that shifting to the Google's gemini or open AI right now will have a place as it's also improving his search capabilities so I I think open AI is going to win this one out but maybe not for the reasons you think and so my experience with chat gbt with search in this case is it works as a true extension to the the conversation I was having anyway so I was having a good so maybe I'm looking at a particular paper on something I want something updated before without access to to the internet there it's only going to come back with uh a limited amount of information right with chat GPT with search it extends out it takes its knowledge plus the knowledge that it's got from the internet and then starts to give me back better answers and and for me that is the game changing part and I just found myself using chat GPT with search more naturally than I did before so rather than reaching out for Google to go answer that question and then mess around I'm just doing it within the conversation now if I then bring that in with the o1 capabilities as that starts to get releasing and as they start to combine the modalities the fact is you know uh you know open ai's been leading on the modalities on this for a while uh they're aheed at a game with the o1 models Etc making it more agentic when they bring all that together um I think Google's got a lot of work to do there are they going to go after true search Etc no but if this is a compar comparison between Gemini and and the 01 models with search capabilities and tools as it stands today I I think open AI is winning that one and and I feel that today from experience I'm having and and and the fact is there are millions of people using chat gbt today and there's maybe 12 people on chit that's using Gemini toar today so so I think that's that's that's where uh that's where that's my feeling yeah I think there's a very interesting question here a little bit about like it's a debate over what we think the commodity asset is and what do we think is the Irreplaceable asset or the hard to reduplicate ABS asset I think show bit if I don't want to put words in your mouth but show bit your your position seems to be all of this data all of this kind of incumbent Advantage is the hard to replace thing and I think what Chris is saying is like well actually getting the data is not the hard part it's this initial additional analysis layer which is going to be the really unique differentiator I don't know maybe that's the right way so there's no doubt that Google is under logge pressure perplexity has just shown how well they work and like I'm Pro user for a very long timey amazing work right so I think yeah generally speaking yes they have a lot of pressure on getting this right it's a hundred billion dollar problem for them to solve so they're putting everything that they can behind it right so they they had to make sure they nail the conversational search part and more more personalized I think the things that are going in favor of Google are the fact that they have the world's data to train on in YouTube and search and they have like Decades of how people the the patterns that people follow to get to the right answer when they're planning a trip things of that nature they do have a lot that they can tap into that other competitors like open I do not have access to today right so over time they'll try to catch up with each other uh Google will always have a lot of fire behind them to go fix this to get this the right way but I'm just the fact that my personal data is accessible to Google I think that may change at at some point but in the current state it is more relevant for me to have an answer that's hyper personalized to me and the way I do things right the fact that I'm asking you to set an iary in Italy it should know that I'm landing at 2 p.m. and not start my itary at 6:00 a.m. right so that that fun fundamental part of me having to tell a model say guys just understand what is important to me first and you know that the airport is X hours away so take all of that information into consideration and I'm thinking about this from a very Enterprise perspective as well right for us our clients are more focused on I've all this repository of manufacturing documents and warranty documents and stuff like that and then I have all of the other data sets I need to be able to search against those with high accuracy and the same experience I'm getting with chib or search or with Gemini I need to bring that into my employees to get unlock the value and it's really nice to see that meta is starting to get into this game as well there's a lot of rumor over the week about meta coming up with his own search because now they're incrementally making progress towards that space as well so I'm really excited about the future of what happens with getting information to show in the moment that I need that's hyper personalized to the way I consume information and what's in my emails things of that nature and I agree with that shet but you know what I don't want have Google having exclusive access to my information right do you know what I actually want an open EOS system in marketplace where I can plug into the agents here go access to my Gmail go access to this Etc and as opposed to going well Google's already got this information and it can train its models and do whatever it wants with my data and nobody else can play in the system so open ecosystem is where I am so yes I agree but it's got to be open yeah there is this potential uh for a centralized AI search model to emerge potentially monopolizing search you know while this could bring you know consistency and ease of fuse it also risk creating you know this information bottleneck so I I definitely agree with Chris that having an open system would be better because if one model provides small search answers it might centralize information flow reduce diversity information sources and also shape public knowledge in ways we really don't yet understand great well uh that's all the time we have for today uh it's great show but that you mentioned that meta thing because that was the other part I wanted to get into so we will definitely have that on a future episode of mixture of experts but unfortunately we are out of time today so thank you for joining us if you enjoyed what you heard you can get us on Apple podcast Spotify and podcast platforms everywhere show bit kowar Chris thanks as always appreciate you joining us

2024-11-14 20:11

Other news