Last time we proved Venkampan's theorem and then derived that if you take the fundamental group of the wedge of circles then it is a free group. The rank was equal to number of circles involved there. We shall use that and try to do now the computation of fundamental group of any one-dimensional simplicial complex. Indeed, we would like to include the case like wedge of circles also. So we would like to extend the notion of this one-dimensional complex slightly a little more general. So such things are called pseudographs. By a graph I will mean a one-dimension simplicial complex. So a more general thing, a pseudograph, some people may call it just graph itself but for me a pseudograph means that you can have a single vertex and then a loop around that or two vertices with many edges between them which these things are not allowed in a simplicial complex, one-dimension simplicial complex, remember that. So this instead of defining like this ad hocly, I will do it systematically in a slightly different way which can be used full for us in doing more rigorous mathematics. So here is the concept of attaching cells back to the first chapter there wherein we have defined adjunction spaces. So this is a special case of adjunction spaces now. So fix an integer k greater than or equal to 0, 0 is also allowed. Let y be a topological space and f alpha indexed family of continuous functions from sk to y. So sk is the sphere of dimension k, okay. So I have indexed family of functions k is fixed by the way, okay. Alpha is varying, there are a number of them. It may be 1, 2, 3 and any number of them, okay. Take a as disjoint union of all these the disks of one-dimension higher indexed again over the same set, okay. The space x obtained by attaching k plus one-cells to y, y are the map of alpha. This is what I am going to define, okay, this entire phrase. So this is by definition in the quotient space of A union y by the identifications on the boundary of these each of these disjoint disks namely sk, I have a map here. So identify x with f alpha of x, x is an element of the boundary which is sk, f alpha is an element of y. So identify this. This you do for all x in the boundary of s alpha and for all alpha, okay. If you have just one, one thing or thing here, this is what is you would have called adjunction space. But here is the same adjunction space, this is A and this is y and this is map f.  So it is adjunction space, nothing else, okay. But this is a very special adjunction space wherein all the, all the disks on same dimension are coming extra from y. So y, we have attached these disks, x is the resulting space, okay. Now a pseudograph I mean a space x as in the above definition where I start with y a discrete space just a collection of points and discrete topology, okay. And the k which I take here is 0. That means what I have this s naught which is minus 1 plus 1 to y discrete space. So minus 1 plus 1 is also discrete. So any function from a two element set to y is continuous automatically. Either the two elements here will go to the same element or they may go to different element that is the only two cases, okay. Then I am attaching a one cell k plus 1, k is 0, k plus 1 is one cell. That means what d alpha, d alpha is minus 1 to plus 1 the closed interval, okay. The minus 1 goes to some point of y, the plus 1 goes to some other point of y, maybe same point of y does not matter, take the coefficient space, okay. That will be called as a pseudograph, okay. So we will have this terminology y instead of writing y, I will denote y as x naught. That means the zero skeleton just like in the case of temperature complex. It is just the set of points that is y to begin with and call it the zero skeleton, okay or those points will be called as vertices also. This is similar to what we have done for simply one dimensional complex. Then q which is a union x naught to x, okay. This is the quotient map, okay. Let q be the quotient map. That means what q restricted to each each one cell, okay. That is either the end points are identified or the end points are going to different points. So depending upon that either it is actually a homeomorphism or in any case the interior it is a homeomorphism. The end points may be the same in which case the image will be a circle otherwise it will be an arc. It will be an edge, ordinary edge, okay. So x naught is actually which is y is the space of x under this quotient map. So again points of x naught are zero cells or you can call them as vertices or zero simplexes and so on. So simplex word we will not use because this is not a simplex complex, okay. Indeed a simplex complex one dimension can also be described by this one. Only thing is then you have to put a lot of other conditions, okay. A pseudograph is in this sense a slight generalization of a one-dimensional complex. Clearly it is locally path connected. It is connected if q a the quotient map, okay itself is x. All the points of y are covered by the alphas, f alphas then it is automatically connected otherwise it is not connected obviously, okay. q restricted to a itself is a quotient map in that case. You do not need y at all, okay. Why is there because there may be some extra vertices hanging. So if it is connected then you can define on this whole thing this quotient space just on a itself. How? Whenever alphas are going to same point you identify them otherwise do not identify anything that is all. Wherever it goes to I fall for something why some other every time I also come to that one you identify them. That is the way it has to be done. So this y helps to describe those relations, okay. Otherwise it is more complicated within a but it is done inside a. If you use y then it is easy to see what is happening, okay. So here is a picture of a pseudograph how it could be different from a symbolical complex. To look at all these heavy lines and heavy lines that is a symbolical complex. But I have attached a loop here a one cell here of which the end points have gone to the same point. Here also the same thing here also same thing. Here they are going to different points but there is already another edge here. So these are violating these things are violating the symbolical complex structure, alright. So this is the general picture of a pseudograph. Immediately you can see that if I put two more vertices here on this loop this part becomes a symbolical complex on this part. Here what should I do? Here also I should put two more vertices, okay. Even if I put one it is enough because this point and this point would be now a single edge this is another edge so this will be like a triangle. So here I can do with just one vertex putting two more vertex no problem. What is the meaning of that?  So the in a hidden way the one-dimensional symbolical complex theory can be applied to pseudographs also. Okay I will use this remark again, okay. Now I will make a one more definition here namely a connected pseudograph is called a tree which is a definition if it is contractible as at a political space it must be contracted, okay. Then automatically it is connected of course such a thing will be called a tree by a subtree of G we mean a subcomplex T of G such that it is a tree it must be a pseudograph on its own but it must be a sub, okay. In a for example in this picture you can remove this one and look at the rest of the picture that is about subtree so there is a subgraph pseudosubgraph, okay. If I delete this one then also it is true you can I can just delete this edge and keep this vertex, okay but I should not delete this vertex and then I cannot keep this edge, okay. So edge has to have you know a complete vertex where it is attached that is the question. So both end points must be somewhere so here it is okay the both end points have gone here, okay. So you can remove this edge but you cannot just remove a vertex, you can remove a vertex only if it is isolated vertex. If you remove a vertex here all the edges which are emanating from there has to be removed that is the meaning of subgraph, subgraph, subseudograph, okay. A connected pseudograph is a tree if and only if it is simply connected so once it is simply connected it will be actually contractively so what I have to show. If it is contractively simply connected is obvious so I have to prove only the if part, okay. So start with x simply connected pseudograph given a vertex v0 belonging to x we shall define a homotopy x cross i to x such that x 0 is always this v0 and h of x1 is identity map. For this we first notice because of the connectivity of x that the quotient map okay restricted to a itself is a quotient map this I have already remarked earlier. Therefore constructing the map from x cross i to x is same thing as constructing it on all these d alphas or j alphas whatever okay the interval minus 1 to plus 1 copies of that cross i in a compatible way construct a quotient map on a quotient is always you can go back to the original space and then do that okay so j alphas I am denoting minus 1 plus 1 you can have a d alpha does not matter and let f alpha from is the q restricted to j alpha the q is the quotient map these f alphas we can say as characteristic maps this is actually earlier notation it was only defined on the boundary but I can extend it to this f alpha is extension of this one to the interior we can put a f alpha hat if you want because it is a castic maps okay given any vertex v now in x since x is path connected there is a path omega v starting at v not and ending at v fix those paths okay there may be many paths I do not care take some paths like this now you define h of v t equal to omega v t okay where I have defined h of v t omega v t for each v I have defined this one for each vertex cross i I have defined this one that is the meaning of this one now let f from f alpha from d 1 to x again I am writing d 1 or j okay minus 1 plus 1 this year be the characteristic map from one cell of x for each of them okay there is one cell in each one cell d alpha to cross i I have defined the map okay this map g is defined by g of s 0 is v not s 1 and the top is f s f s is this map f alpha every time I am not writing f f f f r 5 that is all g of minus 1 t okay is look at this omega by an f of minus f of minus 1 is a vertex right f of minus 1 is a vertex you take omega v t what is omega v omega is a path from v not to v okay and at one at one comma t you take its f omega f 1 okay so look at all these what I have done is on the boundary of d 1 cross i I have defined a loop okay on the bottom they are namely 0 cross i cross 0 a minus 1 plus 1 i minus 1 plus 1 to 0 I have taken it as v not then from v not I go by a path v f minus 1 omega f minus 1 on this part omega f plus 1 okay on that part these endpoints are joined by f alpha namely by the by the simplex there one simplex d alpha so you get a loop inside x and x is simply connected therefore this loop can be this function can be extended to d 1 cross i a function which is defined on the boundary of boundary of d 1 cross i which is d 2 in century can be extended to inside because it is not longer topic okay so x is simply connected it is used here okay g is continuous so you can extend it since x is simply connected you have an extension g alpha for each alpha okay now capital G you take the disjoint union over this one so that on each restriction it is g alpha all that you have to observe is that wherever you have identified it is the same old thing for each of them they it is compatible therefore this factors down to a continuous map as from x cross i corrects okay by the very choice when s is 0 it is v not when s is 1 it is the map surface okay so therefore it is identity map all right so let t be a subtree of a pseudograph g then a quotient map g to g by t is a homotopy to balance if it is a tree okay then it is contractible when we have done long back a a result when you can collapse the contractible subspace and yet the quotient map will be a homotopy to balance so i i would like to recall this namely when the inclusion map of this contractible space into the whole space must be a co-fibration remember that theorem so use that theorem to conclude that g to g by t the quotient map is a homotopy to balance namely when you collapse a tree tree means it is contractible thing okay like a vertex can be collapsed union of two sorry an edge can be collapsed union of two edges if they are not a loop they do not form a loop that can be collapsed and so on okay so this is what i mean if g is a graph this follows from t contains a g is a co-fibration by the way this co-fibration business was done only for simple complexes but the situation can be converted easily to a simple complex because a pseudograph can always be subdivided by putting extra vertices into a simple complex now let x be a connected non-empty pseudograph let t0 be a subtree in it then there exists a subtree in x that is t containing this given t0 such that this t contains all the vertices of x this is one way of telling that that this is a maximal tree you cannot make it into a larger tree by putting extra edges because when you put x all the all the vertices are there as soon as you put one extra edge there will be a loop so let us prove this one a rigorous proof requires now johns lemma or some such thing or hand varying it can be done and so on but in the finite case you can actually do this there is no problem for finite case you don't need johns lemma okay so let tau be the collection of all subtrees in x which contain t0 order it by inclusion okay this in this collection i have an order partial order now we shall apply johns lemma and then conclude there is a maximal one all of them contain t0 the maximal will also contain t0 so let ti be a chain in tau chain in this collection chain means what totally or not set we claim that union of ti is a tree okay so how do we know that it is a tree given any point in this union it will be in one of the ti s because it is a union then there is a path in ti from x to some point t0 because a tree is a connected thing already okay which will also path in t and so t is connected okay each ti is a tree so ti s are connected but now i approve that t is connected okay now suppose omega is a loop a loop means what it is continuous function from i to t therefore the image must be compact okay therefore we know that once it is image is compact it is contained in a finite sub graph finite sub pseudo graph okay it is compact it follows that the image contained in the union of finitely many closed one cells right closed one cells are means what i have told you that it is just either edges or full circles which are all contained some ti because here there are only finitely many of them let us say all the vertices are in t1 t2 t3 and then take the maximum of them in that ti they will be all be there but then this loop is inside ti it is null homotopecant ti but ti is a subspace of t so it is null homotopecant ti okay therefore every chain has a upper bound so that is sufficient condition for John's lemma John's lemma will tell you that there is a maximal tree a maximal element which contains belonging to tau means it is a tree containing tau 0 all right a maximal element i have got now suppose there is another vertex which is not in this maximal tree maximal t that would mean what because x is connected from that extra vertex you must be having a path coming to this t that would mean that you will get a vertex somewhere such that the next edge from that vertex to this tree the next other edge will be inside the t okay so this is what it means but then you see really seen that this okay what i have done here there will be an edges which will have one end point in t and the other end is not in t from the end point which is not in t you can collapse the whole thing to t contract that means t union s deforms to t or t is a strong deformation detect of s therefore t union s is contractible therefore t union s is a larger tree that is a contradiction to t is maximal therefore there are no more vertices so that completes a proof that every every tree can be contained in a larger tree and the largest tree such that all the vertices of x are there okay in particular there is always such a thing we can start with a single point single point is a tree after all so every connected pseudograph is homotopic to a bouquet of circles so this is the the corollary of whatever you have done finally how how does you get it start the pseudograph take a point any point you want there will be tree containing that that tree i will take the maximal one which will have all the vertices in it all the vertices of x are there since it is a tree i can collapse it okay then g by t g to g by t is homotopic balance what will happen to g by t there will be all those edges which are not in t they would become circles every edge which is in t has become a single point at that single point i will have some vertices some what some circles what are these some circles only for those edges i did say that all the edges are inside t all the vertices are inside t all those edges which are outside t they will become circles now so g by t is a wedge of circles namely one point union now circles the number of circles will be precisely equal to the number of edges which have which we have missed from t okay so this is the gist of this thing we are starting with t not equal to v not a single any single vertex take t to be the maximal okay containing all the vertices a tree containing all the vertices then x to x by t is a homotopic balance all the vertices have been identified to a single vertex because t contains all the vertices okay so all the edges in t and also have become two single point so the edges in x minus t are the ones which survive and how they survive they become circles and two end points of these edges are identified single point therefore x by t is a bouquet of circles therefore what is the conclusion you take a pseudo graph which is connected take any point in it pi 1 of the pseudo graph with any point a is a free group that is the conclusion right so let us the next theorem given a connected pseudo graph pi 1 of x is a tree a free group of sorry pi 1 of x is a free group of rank equal to the number of edges outside any maximal tree in x therefore this number is independent of what we use to by the way the maximal tree may not be unique you can think about that but the number of edges outside because of this will be the same that is the beauty okay so that is something which you are down next time we will use this one to prove a big theorem in in group theory and some more topology later on so that will be the last module in the last lecture for this theory thank you


All I know is you came over, you sat down, and I smelled something pretty dang wonderful.  Jameson, have a good cocktail. Drinking is good for your health. Hello, welcome back to our YouTube channel, I'm Corbin, and you can follow us on Instagram and Twitter. It's all juicy content. It's so juicy. So smooth. And I think we'll also send Patreon or follow our official Twitter account. And today, the reason we have these, we're reacting to a song from the movie Cocktail. Hey! Yes. Do you want with Tom Cruise or he's a bartender? No. This is called Tom He Ho Bandu. Yeah. Tom He Ho Bandu. From the movie Cocktail starring Dapika Parakon. Hey! Hey! And... Sartaj. And Diana Penty. Don't know who that is. Yeah, we don't know who Diana Penty is. Would you like it? Yeah, you do. We probably do. And I'm sure there's still a gazillion actors from India that we haven't been introduced to. Yes, but this is from that movie. It's called Cocktail. Any time I have an excuse to drink, I do it. Especially Jameson, my friends. That's true. But for all of you that said don't drink on camera... Here we go! If you say that, I'll block you. Here we go! I'm just envisioning Sartaj. I know, I know. Do you remember the trailer at all? I do. I do. This is looking like it's the beginning of Spring Break in Miami. Crazy cult movie from the 80s called Spring Break. Yeah, it's a Persian. Yeah, I'm sure it is. The volume is really low for us. Unusually low. If I could feel the words. Yeah. I think it's the audio in the video. It is. She physically is the exact same physical type as this actress. An American actress. The exact same physical type. What are you talking about?   You again. Catchy song. Very catchy song. I wish it was loud. I so love that. Go Sartaj! Go Sartaj! Go Sartaj! Depeke! Depeke! Depeke! Depeke! Your mom. Figure out what's going on. I thought Depeke was maybe his girlfriend. No, it's Sartaj. Depeke's your girl, man.  Depeke. Just because you see a pretty girl dancing doesn't mean you got to give your heart to her, dude. Once again, we have no idea what the ex was doing. I know they're going to be a sister, and they just got three or five. That'd be totally gross. And now they're dancing till the break of dawn.  What? There you go. She's going to push her. I'll show you to reject me. Was the woman who screwed up the map and Sartaj's mind at the end of Sacred Games. So that's why he did that, because he's still mad at that woman for doing the thing on Sacred Games. I'm never going to get past that. I wish I knew the relationship. I wish I knew what was going on. But that song is fun, and for some reason, the volume on the video was really low, so we couldn't turn it on. Usually we're blasting it in here. Yeah, I would have blasted that. It'd be interesting. Probably. Because that'd be one that... It's a party song. It's a party song. It's also funny to see Saig-Fellikon in these types of roles. Because once again, we always go backwards with our introduction. It sure seems to be that way. So like, most people don't know Saig-Fellikon as Sartaj, unless he's been watching his channel for a while. Right. But for us, that's who he is. But he was this guy for the longest time, I believe. He was almost like a pretty boy, and he was like the love interest, and all the kind of stuff. Rom-Coms. But it's just weird for me to see him like this. I know. That's Sartaj. Why is Sartaj getting all playboy-y? I want to find out who that girl is. Yeah. Did the actress from You Again, who plays the antagonist to Kristen Bell, their body types are identical. 2012, probably? Right, right. Of the different cocktails, yeah. Yeah, that's it. Okay. Yeah, that's her. Diana Pente. I don't really know. Yeah, we haven't seen her.  You Again. Yeah, the actress with some that. She's done a lot of different work. And if you haven't seen that movie, by the way, You Again is a fun movie with Kristen Bell and Jamie Lee Curtis. Which one are you talking about? Uh, Joanna. Odette Annabelle. They're very, like, the same height, same body structure. That's not who I was talking about. Okay, okay. But yeah, the song was really fun. I really enjoyed that one. I want to hear it, like, on my phone. Yeah, loud. Yeah. What's it called? That's what, um, I don't know. We just reacted to it. To Mi Ho Bandu. Yeah, maybe you could find it somewhere else and we can actually play it and hear it, like, straight from... Okay, this is it. Nope, this is an ad. But that's alright. There we go. That's what I'm talking about.


Okay, so let's look at this satellite anion reacting with this bromocyclohexane. So from what we've been talking about, potentially, you would think that the product that would be formed would be the SN2 product. So what would be the SN2 product? Why don't you guys write that down in your paper right now? So that would be the product that we were just talking about. Remember, in that case, we were reacting it with a primary alkyl halide. This time, we're reacting it with a secondary alkyl halide. Because the secondary alkyl halides have more steric ingredients, more sterics than primaries. That makes sense, right, everybody? Does that make sense right now? Yes. So what? OK, so what you would expect from the SN2 reaction that's not formed would be that product there. Hopefully, you guys all wrote that product. If you didn't, make sure you're being able to by next time. What's actually formed is the E2 product, the elimination product. Why? Because secondary alkyl halides are more sterically hindered. So what you'll find is that instead of just doing the backside attack and letting the leaving group leave, you'll do the acid base reaction. So remember, where did this come from? This came from a terminal alkyne, right? So we can make a terminal alkyne back from it. It's a pretty strong base, remember we were talking about. So when we have that bromine there, that makes that hydrogen even more acidic. So what will happen is that hydrogen will be deprotonated. The electrons there will go into the ring and form a double bond. And those electrons there will bounce out and form the bromide on anion. So what you'll find is the expected SN2 product is informed in this reaction, but the elimination product is formed. And that's, again, due to the fact that this is the secondary alkyl halide as opposed to a primary alkyl halide. Any questions on that one? Wait, the last one. No, that's all right. Oh, OK, sure.


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The Democratic Party primary in Massachusetts is coming up very soon And there's a lot of races to watch in this state But one of them that I am watching very closely is the primary race for the US Senate between incumbent Edmar Key and Joe Kennedy the third now Usually I support the insurgent candidates over the incumbent Democrats But in this instance I actually am backing Edmar Key because he is the more progressive candidate He supports Medicare for all a green new deal pot legalization criminal justice reform and Joe Kennedy even though now He's claiming to support all of these wonderful policies He is a Johnny come lately to all of these policies and Edmar Key has been hitting him nonstop throughout the course of the primary For only supporting them recently since he's choosing to primary Edmar Key. So usually I support the insurgent candidates Not in this instance. I'm backing the incumbent in this instance now having said that I'm not like the biggest fan of Edmar Key in the world And I think that leftist need to pump the brakes on praising Edmar Key because he may be like better than Joe Kennedy But he's not let's not fool ourselves. He's not like a wonderful progressive candidate. He's no Bernie Sanders He's not even a Jeff Merkley because my question is okay. You support Medicare for all green new deal That's phenomenal, but why didn't you endorse the one candidate who wanted to bring this vision to a national stage? Bernie Sanders, I mean Edmar Key did not endorse during the 2020 Democratic Party primary He sat this one out now you can defend him and say well look he had Elizabeth Warren from his own state run against Bernie So maybe he didn't want to ruffle any feathers. Okay, but I mean if you truly care about these policies Why not support Bernie Sanders? And I think that honestly a true leftist should know the difference between Bernie Sanders and Elizabeth Warren But look, I'll give him a pass because someone from his state was running for president even Ayanna Presley Endorsed Elizabeth Warren over Bernie Sanders because she's from the state of Massachusetts And I don't necessarily dislike Ayanna Presley. I think she's one of the better Members of the House of Representatives even if I disagree with her on some issues But then my question is okay. Well, if you didn't support Bernie in 2020, why didn't you support him in 2015? Now you'll ask Mike, but didn't he support him in 2015? No, he actually endorsed Hillary Clinton over Bernie Sanders right when Bernie Sanders was starting to pick up steam He endorsed Hillary Clinton over Bernie in August or excuse me in October of 2015 Yeah, so look Edmar Key. I think that he is He's kind of stretching it a little bit And rewriting history to an extent when he boasts about his progressive bona fides Having said that I do think that you know between him and Joe Kennedy Joe Kennedy is far worse He is a corporate Democrat who doesn't even support the legalization of pot as far as I know unless he flipped on that as well But you know most of the Democratic Party establishment They seem to have backed Joe Kennedy over Edmar Key and that's frustrating because They told us that primary challenges are bad But here they are backing someone who's primarying a Democrat And I'm also frustrated because you have some prominent progressives who are in charge of the Congressional Progressive Caucus like Pramila Jayapal and Mark Pokan endorsing Joe Kennedy over Edmar Key You have Pramila Jayapal doing Medicare for all town halls with Joe Kennedy and that is fine Like I think that as many Medicare for all town halls as you can do you should do them But let's also not kid ourselves and acknowledge when someone is trying to dupe us Acknowledge when someone is just supporting a policy because they want to win an election like we have to be more savvy as Progressives you had Mark Paul can blocking people on Twitter who you know expressed Disappointment over his endorsement of Joe Kennedy over Edmar Key. So look truly There's not very many heroes in this story, but I am rooting for Edmar Key over Joe Kennedy Now what irritates me and makes me like Edmar Key even more is the type of campaign that Joe Kennedy and his team are running I mean, there's no policies except for his fake supportive Medicare for all. There's platitudes You know, it's it's nonsense like Joe Kennedy is basically the heterosexual version of Pete Buddha judge But maybe less substantive than Pete Buddha judge if you could believe it But he is now using Something out of the 2020 playbook, which I kind of expected Joe Kennedy's team is Basically accusing people who support Edmar Key of harassing him and his supporters online and bullying people sound familiar Well, it should because they are trotting out the exact attack that we all knew as the Bernie bro attack So Politico reporter Stephanie Murray explains Joe Kennedy's campaign manager Nick Clements sent a letter to Edmar Key campaign manager John Walsh Requesting a personal and public statement from marquee instructing his followers to immediately end the attacks on Joe's supporters And the threats to Joe and his family's life. She added the email included pages of screenshots of tweets last sentence reads Please note members of the media have been copied on this letter to encourage Accountability now we're gonna read the letter. It's relatively long so bear with me But this is going to be the most inseparable thing you've ever heard like this is what we basically saw When Elizabeth Warren was attacking Bernie Sanders supporters like it's this elevation of well There's just one random comment that said this one thing. So that's going to be conflated to your entire campaign acting the same way It's a culture of this toxic behavior like this is the same shit We saw from the 2020 Democratic Party primary and it's giving me flashbacks And it may be even less substantive than the Bernie bro attack because really all they point to are Not even a dozen examples of people being mean to Joe Kennedy and his family online So the letter reads the following dear John in recent weeks the tone and tenor of the marquee for centered campaign and people associated with it Have reached dangerous levels despite repeated public requests for senator marquee and his campaign to disavow and condemn the vitriol If his supporters particularly online your campaign has responded with silence Ridicule and thinly veiled efforts to actually incite further attacks on Joe Kennedy His family his staff and his supporters this tested endorsement of such negativity has given your supporters Licensed to not only target student fellows destroy campaign materials and vandalized campaign property But to launch death threats against Joe himself and revisit the assassinations of his grandfather and great uncle We understand it is not anyone paid by the marquee campaign making these threats But it is the direct result of the toxic online ecosystem Your campaign has allowed at best and encouraged at worse It has now reached the point where our campaign has to report death threats to capital police on a near daily basis The negativity of the quote marquee verse that is cringe worthy as hell is not random one-off or half-azard It is a pattern of behavior sound familiar one that has grown more vitriolic and unwieldy by the day It began before Joe entered this race when one of the senator's top political advisers launched a personal attack on Joe's family online and despite initial condemnation the campaign chose to reward him afterwards It continues with direct interventions from top marquee campaign aides sharing offensive content and disparaging Kennedy volunteers Doing nothing but supporting their chosen candidate the death threats and assassination References are beyond the pale But it is petty and personal attacks on our supporters that we find the most disheartening Reasonable minds can disagree about who to support in this race between two progressive candidates who undoubtedly share deep political values But no matter what side you fall on we should be able to agree that those who volunteer their time talent energy and passion to help drive the Democratic process should be commended and celebrated not disparaged ridiculed or attacked in the age of Donald Trump sound familiar and the existential threat he poses to our democracy Honesty and authentic political participation is more sacred than ever when this came up in our last debate The senator claimed no knowledge of it for all of your reference I have included a sample of what Joe and our team have faced online for months First the death threats and allusions to murdered family members And then they show a screenshot of a tweet where Alex Thompson shows off a button from the Kennedy campaign saying he's going full Kennedy in the final month with these vintage buttons and They then point out a comment where somebody responds saying he went full Kennedy Rip thoughts and prayers That is ruthless, but it's hilarious They then point to another tweet saying he should go all out copying Gramps There's another tweet of President Kennedy getting shot Somebody then tagged Lee Harvey Oswald saying we could really use you right about now another person says I think we should help him out if he wants to go full Kennedy heading to the dealer plaza right now in Reference to an ad released by Ed Marquis someone said Ed Marquis murdered the entire Kennedy dynasty with just one line of this ad Pun intended your move Lee Harvey Oswald another person said might as well call Ed Marquis Lee Harvey Oswald because he just killed a Kennedy damn the letter then continues second the nasty of course and Personal attacks on Joe and our team done by supporters often directly connected to your campaign your fellows volunteers and organizers Then they show a picture of a dinosaur saying fuck Joe Kennedy.  We know that supporters get passionate We know that in the heat of an election things get sharp But we also know that at the end of the day the buck stops with the candidate and his or her campaign We both have the opportunity and the responsibility to set the tone for our supporters to follow Unfortunately, your campaign has chosen to do the opposite I am hoping cooler heads prevail here and we can agree that this has gone too far as you celebrate and promote your social media Following and Ed Markey for Senate campaign materials. You must acknowledge the influence you can have on those who follow you We are requesting a personal and public statement from Senator Markey himself Instructing his followers to immediately end the attacks on Joe supporters the threats to Joe and his family's life and the destruction of Kennedy From Massachusetts campaign materials and property Please note members of the media have been copied on this letter to encourage accountability Sincerely Nick Clemens now the fact that they see seed the media in this message It tells you everything that you need to know about this.  It's a manufactured outrage campaign Sharing some people joking about him getting assassinated that is not tantamount to Ed Markey promoting this It's not tantamount to a death threat. It's just memes in poor taste, right? But you're going to find this about any campaign, right? You could point out weirdos or randos and say look at what your supporters are doing But that doesn't mean that the candidate is responsible for that like this is the same exact thing We saw with the Bernie bromit They were pointing to snake emojis that Bernie Sanders supporters were sending to Elizabeth Warren as evidence of quote-unquote Harassment and Elizabeth Warren even admitted that the snake emojis in part were the reason why she refused to endorse Bernie Sanders So either this is an attempt to manufacture outrage to help you get elected or Democrats are just so fucking fragile That they can't even take random people on Twitter getting zero attention Criticizing them. I mean give it up give it up But my favorite part about this story my favorite part about all of this fellow outrage is the response to one of these criticisms Oh, excuse me one of the harassment that we've seen by Joe Kennedy's wife So in response to the particular comment where someone said he went full Kennedy rip thoughts and prayers to his young widow She actually responded to that and her comment honestly made me laugh This young widow would be me and this is what Ed Markey's campaign condoms. Oh my god.   You have no policies All you do is you talk about how mean the other side is to you and when you're done with the primaries Then you point to how mean the Republican is how about rather than pointing out how mean everyone is to you Rather than pretending to be the victim when you actually have power and wealth Why don't you offer to do something for the people who you want to represent? Actually promote policies that you support now to Joe Kennedy's credit He actually has been making a bit of a pivot to the left in supporting Medicare for all But the difference here is that Ed Markey is saying you're only supporting these because you're primarying me And I supported these before you now again You can argue the extent to which Ed Markey actually supports these policies But he supports them a hell of a lot more than Joe Kennedy does and being the sponsor of the Green New Deal At least Ed Markey is more serious about climate change than someone like Joe Kennedy But I mean this is all that Democrats have when they are running primary campaigns They cannot beat the leftists on policy. So they have to basically cry and say you're mean to me My feelings are hurt.  You're looking for of course I'm disappointed that as someone I have known for years you are choosing to end this campaign with crocodile tears Senator Markey has condemned all vile and hateful speech Surrounding this race and you know it the Markey campaign has put up with non-stop Kennedy campaign supported harassment including a pickup truck spewing negative attacks literally every day at our campaign events attended by families and young people We don't whine or complain about your ridiculous campaign supported stunts because we are focused on our campaigns positive message and supporters if Reporters would like photographic proof of Kennedy campaign attacks and harassment they can get in touch with me instead of more Disingenuous political stunts the Kennedy campaign should join ours in closing out the race Discussing solutions to the real injustices people face Medicare for all the Green New Deal racial and economic justice as senator Markey Has done since day one and oh, yeah almost forgot. He then tagged the campaign manager for Joe Kennedy So I think that's a pretty good response. I mean he made fun of them for their crocodile tears You know basically poked fun at them for pearl clutching and I think that that's what you have to do Like he did say of course we condemn these violent attacks But rather than saying that you're kind of like tacitly admitting guilt Just say why don't you condemn the violent attacks that your supporters have been lobbing against us first? And then we'll follow your lead be a leader in this instance Like do something like that because what they want you to do is admit guilt when you have to deny that narrative like reject The narrative altogether because it is just that it's a narrative.  You goddamn insufferable hacks But this is exactly what you know Democrats do because they have nothing, you know as this person said they have no substance Now I do have to criticize this person who is the campaign manager of Ed Markey for boasting about the fact that he's a fan of Bane capitals Deval Patrick in his Twitter profile How could you support a progressive like Ed Markey and be a fan of Deval Patrick in the same breath like? Democrats are just very weird. This is what happens when you have a big 10 party, right? But look this story is laughable like the fact that Joe Kennedy's campaign Actually published this and thought that they would get support from people or sympathy like it's embarrassing like you should feel ashamed of yourself Like this campaign manager for Joe Kennedy Shouldn't be able to get a job in politics again because you did this and before we end this I do want to add one more criticism of Joe Kennedy That they can use like you have my permission to use this in your email your next campaign to add whatever you want because I'm really butthurt that I was not used in the burning bro smears because I Sent a lot of snake emojis and rat emojis to people to judge and Elizabeth Warren.


From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. With Watson 1.0, IBM deviated from the Silicon Valley mantra, fail fast, as it took nearly a decade for the company to pivot off of its original vision. In our view, the opposite dynamic is in play today with Watson 2.0, i.e. Watson X. IBM's deep research in AI and learnings from its previous mistakes have positioned the company to be a major player in the generative AI era. Specifically, in our opinion, IBM has a leading technological foundation, a robust and rapidly evolving AI stack, a strong hybrid cloud position thanks to Red Hat, a rapidly evolving ecosystem and a consulting organization with deep domain expertise to apply AI in industry-specific use cases. Hello and welcome to this week's theCUBE Research Insights, powered by ETR. In this Breaking Analysis, we share our takeaways and perspectives from a recent trip to one of IBM's research headquarters. And to do so, we collaborate with analyst friends in theCUBE Collective, Sanjeev Mohan, Tony Baer and Merv Adrian. We'll also share some relevant ETR spending data to frame the conversation. Now this past week, about 50 top analysts spent the day at the Thomas J. Watson Research Center in Yorktown Heights, New York. It is a beautiful facility in an American gem of core scientific research. It is the headquarters of IBM's research. The innovations from this facility over the decades have spanned mathematics, physical science, computer science and other domains that have resulted in semiconductor breakthroughs, software innovations, supercomputing. It's a main spring of IBM's quantum computing research and has spawned several Nobel Prize winners. The event was headlined by IBM's head of research, Dario Gill, along with Rob Thomas, head of software and go-to-market. As I say, about 50 analysts attended for a full day of briefings on GNI infrastructure, semiconductor advancements and quantum computing. Now for today's breaking analysis, I'm going to set the stage with some spending data, my perspectives, and then we'll cut to the conversation with Sanjeev, Tony and Merv, which took place during and immediately after the event. First, I want to review the top level macro picture in enterprise tech spending. And there seemed to be some confusion amongst the analysts about the actual data. Now if you follow this program, you've seen this ETR data format previously, it plots spending momentum on the vertical axis using a proprietary metric called net score. That reflects the net percent of customers in the survey, which is around an end of 1700 IT decision makers, that spend more within a sector. The horizontal axis represents the penetration of us within a sector, within the survey. That's called pervasion. So it's a proxy for market presence. So that red dotted line at 40% represents a highly elevated level of momentum. That's on the Y axis. Here's the story that this picture is telling us. During the pandemic, as you can see, the squiggly lines there show this, spending on ML and AI peaked. As we exited the isolation economy, we saw momentum decelerate on AI spending. And then one month before chat GPT was announced, the sector bottom and then the trend line was reset upward. Now because the top line IT budgets are not growing dramatically, a majority of AI initiatives, we estimate probably around two thirds, are funded by taking money from other sector buckets. Now we're highlighting cloud in this graphic and you can see it's downward trajectory as cloud optimization kicked in the last several quarters. But other sectors face the same headwinds. It's striking to see the momentum that companies get from rapid feature acceleration and quality announcements in gen AI. Here's a chart from the August ETR survey that shows which players products are getting traction in gen AI. The blue bars indicate the then current actual usage and the yellow is planned usage. And you can see Microsoft and open AI are clear leaders with Google next with Vertex AI and then IBM Watson X AI. Other likely includes platforms like Anthropic. Now the point is Watson X is clearly in the mix and IBM's focus is on hybrid cloud with Red Hat leading the story and AI. Now with Watson X. So this is just three months after survey was taken three months after IBM's Watson X announcement at its think conference. And the same timeframe that 90 days really about roughly for Google Vertex AI from the point in which they announced that in May. And the point is the pace of innovation is fast and adoption is following pretty close to GA. Spending is happening. AWS bedrock just went GA last month and we expect measurable impacts in the fourth quarter. Now here's a similar graphic with the why is spending velocity in the X representative of market presence for specific AI platforms. Now that table insert that informs the placement of the dots those two columns essentially net score and then the end in the survey. Let's start in the upper right. Notice the positions of open AI and Microsoft. They have net scores of 78% and 67% respectively. And it supports the narrative. These are off the charts and it supports the narrative that these companies are winning in the market and gen AI very clearly, very strongly above that 40% magic line. Now the cloud players as well are top of mind as is anthropic and Databricks with its machine learning and data science heritage. Now while IBM is showing much lower on both dimensions look where the company was in April of this year just one month prior to its announcement of Watson X which again was in May. They went from a negative net net score that again that means spending was flat to down for a higher percentage of customers than it was up. Okay, so they went from a negative net score to a strongly trending upward movement to 15.8%. That is meaningful and their net score surpassed Oracle in the last survey. Now interestingly when you dig into the data one of the reasons for that uptick is the percentage of new customers. These are new logos that are adding Watson or new customers to the Watson X platform jump from 3.6% in July. So it was relatively low single digits in July of 2022 and it jumped to 13% so mid single digits in the October 2023 survey. Now the other really notable is the churn dropped from about 16% in July of 2022 to 7% over that same time period. Now remember legacy Watson 1.0 is still in these figures so they're working off some of the managed decline there and transitioning the base to Watson X. The point is quality announcements with strong features are coming and they're coming fast and furious and they're catalyst for Gen AI platform adoption. We're going to talk about that in the analyst round table. So with that as background let's go into that and these are seven areas that we're going to focus on. The first one, digging into the top takeaways we're going to look at the Watson X stack and we're going to go from silicon all the way up to SAS and comment there and then we evaluate the impact on two ecosystems the analytics partners, remember Tony, Sanjeev and Merv, hardcore data and analytics folks so we're going to focus on that and we're going to look at the Gen AI partners. We're going to go all the way up the stack. We'll look at any surprises from the event and then we're going to finish with commentary on the roadmap and top challenges that IBM must overcome in the market. Now one area we don't dive into too extensively in the conversation because it was mostly NDA is quantum computing. Now I'm not deep into quantum but I'll say it's starting to feel like it's getting more real and IBM is betting big on quantum is taking a very long-term view of the technology. My take is conventional computing is going to be here for decades. The big question that we think about here at the Cube Research is how existing computing models they keep getting better and better people figure out better mathematics and better physics with existing silicon and innovators are finding new ways to solve problems irrespective of quantum. So the crossover point is really fuzzy right now but it's something that we're going to really start paying closer attention. Okay, with that, let's take a look at the analyst discussion. In this episode, we're partnering with our friends in the broader Cube collective community, Sanjeev Mohan, the founder of Sanjeev Mohan creator of the It Depends podcast and Tony Baer who's with DB Insight and a longtime friend of the Cube. Tony's part of a group of outstanding data analysts that we refer to as the data gang and of course, Merv Adrian, also former Gartner analyst and now an independent analyst with the company he formed IT market strategy. Merv had to catch a plane. So we prerecorded his thoughts and we'll work them into the conversation. And we're here at the Thomas J. Watson research facility. It's in Yorktown Heights, New York, a beautiful facility. I'm Dave Vellante with the Cube Research, formerly Wikibon and we're going to share our thoughts and opinions on IBM's progress in the area of AI, data, research, maybe a little bit on quantum and its ability to commercialize and monetize its impressive research capabilities. And guys, as you know, IBM launched its Watson export portfolio in May of 2023 at its think conference and continues to evolve the platform with things like Watson X AI, Watson X data, leveraging open standards like Apache iceberg, they're partnering with key players and they're evolving its roadmap into governance and beyond. So let's just get right into it and start, guys, what are your top takeaways, Sanjeev, kick it off and then we'll go to Tony. Thank you so much for having us. It is absolutely wonderful to be at such a prestigious location. My key, I have so many takeaways from what we saw today, but I'll stick to only one. My main one is the speed at which IBM is moving is breathtaking. Watson X was only announced in the month of May 2023 by June, Watson X.AI and Watson X.Data had already gone GA. In September, code assistant went GA. And today in November 2023, Watson X.Governance has been announced. This collaboration between the research and the business is something that we have never seen before. And I feel that IBM has literally shrunk the time between what the research and what they commercialize. Thank you for that. Tony, what's your main takeaway or takeaways? I'll basically follow that up, which is that, and I think one thing that, and I never really correlated this until I actually got to this facility where it was basically about a dozen years ago, it was right after Jeopardy, where we saw the original Watson, maybe we can call it now Watson 1.0, it was kind of like showcasing what Watson 1.0 did on this huge national, huge TV game show. But I think the thing that really hit me over the head today, and I think what Sanjeev is talking about is a good result of that, is that IBM learned plenty of lessons from the first Watson, which is one, let's not build something so unique and so complicated, we need to get product out to market, we need to make this easy to use, we need to basically integrate parts. And in many cases, they're actually building on what they built before. A lot of the Watson X portfolio comes from a lot of the Watson tools. So I think they really have learned there. And I will take the liberty of saying, why don't we call this, why don't we call Watson 2.0? Yeah, thank you for that, Tony. Let's go to Merv and get his take that we prerecorded. Yeah, play Merv and let's come back and we'll talk a little bit about my takeaways and then we'll move on. Well, the biggest thing I think is that IBM is an overnight sensation after 20 years of working on AI technology.  They did a lot of pure science for many, many years. We all know how commercially successful that was, but now they focused on a deliverable set of technologies that create and deploy and optimize things that are being designed to solve real-world business problems for some of the largest companies in the world who needed desperately. Okay, for me, I think what Tony referred to as Watson 2.0, I mean, I think that this company has the term embarrassment of riches, it's got a lot of riches. And I think to your guy's point, I think they got it right this time. I mean, in terms of the original Watson, trying to make it do things that it wasn't necessarily intended to do, but they built up a lot of expertise and a lot of learnings and they're really compressing that time to innovation, which is critical in this day and age, obviously. But now let's think about Watson X and look at it from the stack standpoint, from chips and infrastructure. You think about foundation models and LLMs the whole machine learning platform that IBM has. And of course they got data and analytics all the way up to apps and SaaS. And let's start with Merv. Let's listen to how Merv thinks about the Watson X stack. Well, it's been a while since we've looked at the value of a truly deep vertically integrated manufacturer here. And to your point, all the way from the Silicon up through the models, through the data collection, through the data extraction from assets that aren't really used today to the creation of commercially viable models that are not oversized the way the open source projects are but are tuned and sized appropriately for performance and very importantly indemnifiable. And inside a development environment that knows how to use all of that, deploy it, version it, provide the apps associated with it and then on top of all that, a consulting organization that's actively engaged in delivering that technology package for specific business problems for users today. That's all of the pieces coming together at the same time not at varying stages of maturity or preparation but everything we saw today is being done somewhere right now by a customer's paying. All right, thank you. Thank you, Merv. Tony, give us your thoughts on the stack. Well, for one thing, IBM has obviously thought from the hardware level up to essentially, I don't want to call it the virtualization layer but essentially it's the open shift layer, the orchestration layer. And that's actually a very unique basically asset that they have. And then at the top that essentially that's where essentially what I'll call for lack of a better term the application layer, which would basically be Watson X and all the data lives. And so I think from that standpoint, it's a fairly comprehensive vision. It includes pieces that are in various stages of being filled out. And I think also it gives you IBM and we'll talk about this in a bit an opportunity to put a lot of pieces together. I think where I think that essentially that IBM is really differentiated from let's say like AWS or Google for that matter is that AWS and Google will not make any bones about the fact that they're going to know their customers, applications or their customers, vertical businesses. I mean, yes, Google and AWS, I believe have basically some horizontally call center automation options that use AI and now using generative AI. And I just have a really good feeling and it's nothing that I can really point to at the moment. But if you look at IBM's history of close engagement with customers, it's very significant systems integration, consulting business, 160,000 people in that business. They know what they're very familiar with, with customer business processes. And so what I'm really hoping, I think what IBM really has the chance to do compared to a lot of, let's say like, it's either rivals or frenemies in the hyperscaler world, is to take AI and go further in terms of verticalization, in terms of let's say matching up, let's say the right foundation models with let's say some prescriptive types of data sets. I think IBM has a lot of potential there. So I think that to me is the thing that really sticks out. Yeah, I mean, IBM's always had pretty significant data chops. And what is relatively due is the impact of Red Hat as a linchpin. When you think about arbitration as mantra of hybrid cloud and AI, OpenShift containers and Linux are going to be everywhere. Linux is going to follow the data to the edge and obviously containerizing workloads enables a hybrid cloud. But Sanjeev, anything you'd add to that? Yes, I wanna say that if you look at the layers of IBM's AI stat, the layers are not any different from what others do, but there are some certain nuances that make them different. For example, at the hardware layer, they can run on NVIDIA, they can run on Intel, AMD, even Google TPUs. But then the layer above, we talked about OpenShift as being the layer above that and also Linux, but they also have Ansible that they have inherited from Red Hat. So that Ansible Automation Platform is another key component of that. And then as you go up the stack, they've got the whole governance layer, they have their own databases, they have their own data integration, master data management, so they have all of that. They have the SDKs, the APIs, and then at the very top, they have the Assistant, which allows them to do code generation. So that's how they think of the entire stack. Okay, thank you for that. Let's talk about the impact of the overall ecosystem. I mean, IBM's ecosystem spans from, and it's stack obviously spans semiconductor partnerships to hyperscalers, SAS, ISVs. And of course, it's internal consulting partnerships and external partnerships with consulting organizations. Sanjeev, why don't you kick this off? How do you think about the evolution of IBM's ecosystem? So they have had partnerships with major cloud providers with SAP, Salesforce, Adobe. They're actually working very closely. We learned today a lot about Adobe Firefly and how much that collaboration is taking place. But what really excites me is their partnership with Samsung. And the reason I say that is because IBM is rethinking its entire AI first chip, which is currently at five nanometers, it's going to even two nanometers, and they have two partnerships. Samsung is the one that has a foundry and builds their chips, but now they are also very closely partnering with Japan, a company called Radius. And they're developing a two nanometer chip. So I see that's a very exciting part of their partnership. Yeah, you know, I'm glad you brought that up because when IBM essentially jettisoned its foundry, you know, spun it off the microelectronics division. People thought, oh, they're getting out of the semiconductor business far from it. I mean, they changed the business model, leveraging external foundries. Samsung, obviously the number two foundry in the world behind TSM, but very rapidly with IBM's partnership, moving along that curve, I think significantly driving innovation. Tony, what would you add to the ecosystem conversation? Yeah, well, a couple of things. I'm going to first continue on Sanji's thoughts with Samsung, which is that, you know, with IBM, you basically, you know, very much see this as a classic licensing play. And the fact is that there is a ready market out there because the fact is NVIDIA can't supply everybody. And so we are seeing kind of a race here.  Google has its TPUs. The interesting one, Microsoft just partnered with Oracle to piggyback on Oracle's NVIDIA superclusters. So basically, you know, that doesn't necessarily, I wouldn't classify that as going to a second source. The fact is that all of the big, you know, let's say like hyperscalers are essentially, you know, have huge appetite, you know, for these types of chips. And in turn, for anybody else to get their hands on these chips, the best we can do at this point is look for some sort of secondary aftermarket.  Where I see IBM playing here is that, you know, licensing, working with partners like Samsung, it becomes a key part of the supply chain for these advanced AI chips. The other thing also is that, you know, as we start looking at, you know, generative AI, the fact is that it's going to require lots of different types of foundation models, which themselves, you know, there may be a need to have different types of chips, you know, that are specialized with different classes of foundation models. So I see huge potential in that. The one other partnership I want to focus on is AWS, because I think IBM, you know, and actually was at, he's here exactly, you know, almost exactly five years ago. I saw Arvin Krishna at that time before he became, you know, because CEO. And basically, he outlined IBM's hybrid cloud strategy. Until that point, IBM was trying to say it's IBM cloud. And IBM was at that point saying, you know, something we realized where the market has gone, we still very much will have our cloud, but we also realized that our customers are going to be using other clouds that have already scaled. And so in the past about year or so, you know, year, year and a half, IBM is really, you know, very much ramped up its relationship with AWS. And it's to the point where I want to see a lot of these Watson X, you know, SaaS services, which are initially premiering, you know, both on-prem or in IBM cloud, you know, or hybrid cloud and OpenShift, I should say, I really want to see that go to AWS. And I think there's a very strong potential. It's going to be a really strong route to market for IBM. Thank you for that. And then, you know, you think about some of the advantages that, that IBM has in terms of its, you know, leadership and quantum. Now, quantum is still, you know, years away from really having a measurable market impact, but it's also building a quantum ecosystem as well. And, you know, a lot of times people will use the CUDA, Nvidia, you know, CUDA analogy, you've got to have a programming environment that will run applications and IBM is well on the path there. But as I say, it's many, many years away, but it could be a linchpin of IBM's, you know, future of business volume. Let's hear from Merv. Give us your, go ahead. Yeah, so I just want to add, since we were talking about the model, foundation models, specifically, another very exciting partnership I liked what I saw today is between IBM and Huggingface. So, and it's a bidirectional, very tight integration. You can go into IBM Studio, which is the front end, and you can either run a native model from IBM called Granite, which it's fully trained by IBM and they've published all the data sources, all the training, optimization, everything is out there in the public. Or you can pick a Huggingface model from its collection of models. Vice versa, you can go to Huggingface and you can select Granite. So that's a very good integration they've done. Great call out. And I tweeted during the session today, some ETR data on Huggingface adoption and like many MLAI platforms, it was very high momentum during the pandemic. And then it started to wane. And then month after chat, GPT was announced, it started on a new trajectory and you're seeing Huggingface is obviously one of those partners you want to do business with. Let's hear from Merv. Merv, give us your take on the ecosystem, please. Partnerships is something that IBM has historically been very good at and one of the most intriguing moments of discussion of that today was the story about their semiconductor factory, the work they've been doing with Samsung, the work they're doing with New York State, building a new fab plant for the new generation of hardware that's gonna be used for a bunch of this stuff. They're partnering with almost everybody. And I say almost advisedly because it was notable that we heard Google's name mentioned very, very rarely today and also Oracle's name mentioned very, very rarely. But we heard AWS, we heard Microsoft, we heard SAP. I guess the usual suspects with a couple of notable, at least for the moment, omissions, they said it was work in progress. So it's not clear whether that just hasn't closed the loop yet, but right now those guys are not in sight. So that was a little interesting to me. Okay, thank you, Merv. All right, Tony, let's start with you here on the next sort of series of questions. Is there anything you heard today that surprised you? I mean, personally, I was struck by Dario Gill's commentary on the steps that IBM has taken to actually turn research into commercial opportunities. And this is something that IBM, I think, struggled mightily with in the 2010s, but he had very specific details, organizational details, the process, the mindset, collaboration, silo busting across multiple research organizations. And then Rob Thomas' commentary on focus where the product folks do nothing but product. They're not doing marketing. They're not doing go-to-market, which used to be kind of one-third, one-third, one-third. They're focused on product. So that was both surprising and refreshing to me, but Tony, anything that you surprised you? A couple of things that I'll just continue along that line for a moment, which is that I think a lot of it just, this comes from the top. And IBM for the first time in many generations has a product person at the top, you know, Arvin Krishnam. And so I think with him, you basically have seen kind of like a back-to-basics with IBM, which is like, let's go back to, let's look at what we do best and become more of a product company and become more focused. I think out of that, of course, you know, what Dario was talking about was that, each of the labs used to be their own fiefdoms. They each had their own specialty. So you go to Toronto for this, Alamedin for that, you know, and Watson here in New York, Tyler Heights for something else. And now it's that they are now putting together, kind of, it's almost like, you know, in a way like almost like an ERP of research, which is that they now are operating basically from the same sheet of music. They may still have their specialties, but at least there's now basically more of a transparency, you know, more, you know, more, I guess what we call inter-process communication. So I would agree with you. That same, which I think was very exceptional. The thing is I said, I think a lot of the stems from the fact that they've had a change of leadership that is basically much more, you know, basically, you know, feet to the ground. But the other thing that really just sort of, you know, stirred my, you know, that really surprised me was the take up, the ramp up of Watson X. This product, I mean, it was only announced in May. And yet, when we saw the logo, all those logos out there, this is unusual for any new IBM product line, even as it was just going to early release, but there was that much appetite, you know, in the customer base. And the thing is, I'm not going to describe it totally this, but I'll give this, you know, this sort of reflects, you know, kind of what we were just talking about with, you know, with research and with focus, which is that, I said, think. I've been thinking in previous years and it was just all over the place. Think, you know, in Orlando was very focused on, and they stayed focused on two messages or two themes. One was hybrid cloud, and the other is AI and they stuck to it.  And so I think that to me, as I said, the rapid ramp up of logos to Watson X, just surprised the pants off. And the data confirms that if you go back to my tweets on 11.9 the day that we are actually here at IBM, I shared some ETR data in terms of Watson's spending momentum on Watson platform compared to a year ago. It's still nowhere near where the big cloud players are, but they made a substantial meaningful move. Now, one quarter doesn't a trend make, but it's something that we're watching closely. Let's bring in Merv and get his take. Anything, Merv, surprise you. Change in attitude was a bit of a surprise and it was very notable at the very, very beginning of the day when Rob Thomas spoke specifically to Dario, software organization talking to the research organization about how they jointly made this decision to invest in a technology when it wasn't necessarily all that exciting to a lot of people. And of course they'd had their issues with how well Watson had gone. So they made that commitment, but also Rob made this very specific decision to have the people working in the software organization work on delivering product, not owning marketing anymore. And that was controversial and he's not entirely sure yet how successful it will be, but it's a renewed focus by IBM on commercial delivery, which frankly has been absent. We've heard an awful lot of stuff that sounded really good as data sciences, IT research, but there is an enormous amount of focus right now on stuff their customers can use for solving the business problems. And it's a convergence of trends in the marketplace. They could be uniquely well positioned for this moment. And that's pretty exciting. It's the first time I'm as enthusiastic about them as I've been for a while. Okay, thank you, Merv. Sanjeev, let's go to you. Any surprises that you'd care to share? Yeah, I am delighted to see this new IBM, it's refreshing. I feel for the first time in a very long time there's a sense of urgency, there's a sense of focus. And also what I was quite amused to see is how IBM is leveraging a lot of their acquisitions from the past. So I'd be very honest. In the past, when I used to see IBM, there used to be a file net group or cast iron group, all kinds of, and they never talked to each other. But today it was quite interesting to see that when you use the AI code assistant or something and the code, oh wow, the lights went out. The lights just went off. Turn the lights on. Yeah.  This is a real time, baby. Real time. Yeah, so when, so now what I saw that I found very interesting is the fact that as soon as AI does a recommendation, let's say you say AI write me a COBOL program to do certain tasks and the code assistant does that. Or if you say that, okay, now I want you to translate into Java, it gives you recommendations. Those recommendations can then be made actionable through and super, like I said earlier, you can put it in Astana. Which is another one of their acquisitions to do application performance monitoring. You can use IBM's data band acquisition to do data observability. So all these pieces are now sort of coming together. And I thought that was very, very good. Nice. Okay, let's press on in the dark here. Merv, how would you describe IBM's data and analytics ecosystem? You know, let's talk about the positives and negatives, the challenges. You know, we're talking here about the data catalog, the databases, which of course is Merv's wheelhouse, integration tools, governance, data ops. Let's hear from Merv. Yeah, what's been sorely missing besides their openness story, which is admirable and their willingness to work in a hybrid cloud environment which is also admirable, but neither is particularly unique. And of course they've been good with structured data and even some unstructured data. One of the most impressive things I saw today was watching one of these models extract data from a photograph of a form that was filled out by hand. And the demo was extraordinary because they didn't just show us the magic that happened at the end. They also showed us, here's the data stream that the system extracted from this document and fed to the model for analysis. And you could see that there's stuff going on that people aren't talking about, which is what do we do with that? How do we get that data out of those things? It reminds me of Steve Martin's old thing about get an ad. You can make a million dollars and pay no taxes. It's easy, let me tell you how. First, get a million dollars. Well, hang on, let's go back to that part. That's the hard part. How do we get this data not suitable for consumption into a form that's consumable by these models? We've made the models good. Now, how do we give them the data? This is a really core competency and not very many people are talking about it. So that's one. The other one is, I think the amount of time and effort that IBM has put into trust, into governance, into PII recommendation, all that stuff, recognition, excuse me, all that stuff. And the fact that as a result of all that effort, they are willing to indemnify their customers, to offer them models that they are indemnifying. I haven't heard that word in any of the other conversations I'm having about this technology. And you think about who their customers are and the fact that those customers are going to have to sit down in front of regulators and not just say, yes, we've got explainable AI, but in fact, give them a report that says in the words those regulators want to hear, how they got to the decision to lend Donald Trump $50 million more than his property was worth. You know, that's a big question. Okay, Sanjeev, what's your take on IBM's analytics ecosystem? So their ecosystem consists of some databases like DB2, consists of the governance pieces that I've talked about a little bit. It consists of their, I didn't see any BI stuff from them though, maybe I missed that part. But I think they, like I said earlier, they're leveraging all their existing pieces. Their lake house is very interesting because they're using Iceberg. So Watson X dot data is their lake house. They are relying a lot on the cloud pack for data. So this is what we saw today is actually the next iteration of cloud pack for data. Yeah, okay, thank you. And Tony, why don't you close out this section? Yeah, I'm not quite sure that basically we saw the next iteration of cloud pack for data. I think that right now, and this is what I've been on IBM's case about this, is that they really need to bring Watson X dot data and cloud back for data together. I didn't really hear that today. And that's actually one of the things that's on my wish list. I really do want to see that. But yeah, I think I was very, I mean, I did a whole lake house study of lake house market ecosystem, back in Q1 of this year. And I basically, one of my predictions was that IBM was going to support Iceberg. Iceberg was as it has become essentially, kind of the de facto standard open table format for the lake house. And so my prediction was vindicated there. And I think, and in fact, as they've actually implemented Iceberg with a few interesting little twists in terms of being able to locally cache data and be able to do certain things in a very distributed fashion. So I think IBM has actually done some, taking some of their innate capabilities and kind of put them in good use with Iceberg. They're not just any other Iceberg implementation. That being said, as I said, I do want to see some real product convergence with cloud pack for data, which is not just data warehouse. I mean, cloud pack for data also included basically kind of like AI ops tools, also included analytics tools, actually kind of subsumed a lot of like some of the old Cognos analytics, as a matter of fact, you know, taking that to the cloud. So that's so much, I mean, I didn't see that today. It's something that I do still want to see from IBM. Yeah, so we hear it with the, it depends on the podcast and the cube after dark. The cube after dark. It's a belated Halloween special. Okay, the lights came back. So there you go. Great, listen. Audio is the most important thing is as you guys know, we turn these into podcasts and that's where we get a lot of listens. But let's talk a couple more questions for you guys. Let's talk about the external ecosystem of partners, specifically gen AI partners like hugging face. We talked about a little bit, the use of LangChain and so forth. So let's get into the thoughts on IBM's partner strategy as relates to, you know, gen AI and just broadly that category. Sanjay, Sanjay, if you first. Yeah, by the way, you know, LangChain and a lot of these partnerships are very interesting. The thought that crossed my mind was that the space is moving so fast that I wonder how fresh these partnerships remain. Like for example, you know, OpenAI for instance, introduced techniques to bring data in the context size increased a lot. The context size of OpenAI this week went from 32K to 128K. Dranit's size is 32K, which was actually the largest when they announced it. But now they're not. Some of the LangChain, Lama index capabilities are now being brought into the model. So it's a complete moving target in my mind. So it's great that they have these partnerships and they should, but it's literally tomorrow they would need to have new partnerships that they don't even know exists today. Great, thank you. Tony, your take. Yeah, it's really all about foundation miles and having is, you know, I mean, right now we're kind of, we need to go through a Cambrian explosion of foundation miles, which at some point it will rationalize out. But the fact is, is that, you know, what we found is that general purpose, you know, you know, large language models, LLMs are just not going to be even efficient way to solve most business problems. And that in the long run, we also start to need to look at the carbon footprint of all this. And so a lot of this is going to be models that do, you know, through the School of Hard Knocks experience we're going to find out, you know, learn which models are going to be more suited for which classes of problems and that work with which, you know, types of data sets. And there probably will be some vertical industry ones. That being said, I don't, I mean, and this is not to criticize IBM. I think everybody is trying to partner with anybody and everybody. The actual makeup of them, you know, of the partnerships between different hyperscalers and IBM and others will vary a bit. And in terms of like the, you know, for instance like Oracle Invest and Co here, Google and Amazon are trying to compete with the BBFF, you know, with Anthropic. And of course, you know, Microsoft's kind of a tight, you know, bear hug or bear hug with open AI, which I'm not sure maybe goes the other way around in terms of, you know, who's really driving that relationship. So qualitatively, I don't see a lot of difference in the partnerships. It's not a criticism. We're still at early stages.  Again, this is just going to keep, you know, changing. I think what's kind of interesting, of course, open AI, I just had its developer conference and part of that is they laid down the gauntlet. We're going to start to really, you know, underpriced because essentially we have the volume. And so it's going to be a very interesting blogging there. But I think in the long run, it's going to be won by the long tail, which is basically, which is like, you know, finding the right foundation now. And then at that point, we will see some, some consolidation. And I think this is where I think IBM with its vertical industry expertise and it's consulting, you know, it's, you know, where it's on the ground with clients can then get, you know, prescriptive. And I think that's really where the value of the partnerships it's going to be. It's not going to be that IBM is partnered with Huggingface, just like Amazon is, just like Google is.  So a couple of comments there. So you look at the spending date, open AI and Microsoft by far the most spending momentum. Anthropic also is pretty high momentum, not as prominent, not nearly as prominent. Certainly Google with Vertex AI is showing. And then one that's not showing up in the spending data yet is Lama 2 from Meta. But my, by my sources, the indications are to your point, Tony, about the industry specificity and the domain specificity, perhaps as much as 50% or more of the downloads of Lama 2 are going on-prem or at least the organizations that have major, major data center presence. You know, Bedrock from Amazon just went GA, you mentioned coherent Oracle, they've got momentum, but right now it's really, it's open AI and Anthropic are the two big ones. And of course, Bedrock has a lot of momentum, but it's more a suite of optionality. Let's get Herv's take here and then we'll wrap up. I think this is going back to IBM doing what IBM does best. They identify technologies early and where appropriate, they invest even by the vendors involved. And then they deploy it as part of their stack. And generally speaking, they're not closing that when they do it. The world is turning to Ansible for deployment of things. Red Hat acquiring that was a very smart move. Their work with HuggingFace is really interesting and they'll be able to allow their customers to play with stuff, but none of HuggingFace's models are indemnifiable just yet, but the ones that IBM partners with them on and deliver will be. And so it's a little bit of the best of both worlds, I think IBM knows how to work an ecosystem, but they also know how to take stuff that's open that they have invested in, worked on themselves and released to market. They know how to take that and make it appropriate for industrial grade use in the most mission-critical applications. Last question, and it's kind of two-part question. So we'll combine them, part A, part B. First is, what do you think about IBM's roadmap? And the second part is, what do you see as IBM? So let's end with its key challenges. So first the roadmap and then its key challenges. Well, the roadmap was largely missing in action today, unfortunately, I think we got to see a good roadmap up to the present and maybe six months out, at least on the software side. Wasn't much talked about beyond the first half of next year. And I would have liked to know a little bit more about that. Now that may in part be derivative of the fact that they're going to be guided by the actual deployment experiences. A lot of this is gonna be, what did they ask us to build? Now we've delivered it. And hey, by the way, that's resellable. We've got a model for this business process that we developed for one customer. It won't be available only to that customer. That bodes very well. Prospects for the future. IBM has had a relevance problem for a long time. A lot of commercial buyers are looking to the exciting hot young checks on the market, right? As opposed to the old stalwarts. On the other hand though, IBM's customers are the largest industrial and governmental organizations in the world with very intractable problems. And they have a deliverable here that is well suited to those requirements. And that is and will be a lucrative market. We've been looking at transformation and we've been looking at migration and we've been looking at evolution of the technical debt of large organizations for a long time. And lots of people have taken a run at this. IBM is in a position to service the largest piles of technical debt in the world to the advantage of their customers. And we may be on the verge of one of the largest waves of migration and transformation of legacy software assets because they're not liabilities. They are performing assets. But the transformation of those into a new environment at a higher rate of speed and within demnification. That sounds pretty good to a lot of Wall Street firms who have thousands of bespoke applications that they wrote 30 years ago. That could be a dramatic wave that could be as significant as when C arrived on Wall Street. Okay, last question. Can I say something a bit contrary and far reaching? So Dave and Tony, the way I see this partnership, this whole ecosystem developing is if you look at what a data break state, when they started, what did they do? They only did Apache Spark. That's all. And from Apache Spark, they then went into all other ends and nooks and crannies or the flow. Yeah, and they just built out the entire ecosystem. Snowflake started by building a better cloud native a data warehouse and then they added container services and Snowpark, Python and all of that. So now they're very similar. IBM we are seeing is taking their industry partner or industry focus and the fact that they have 160,000 strong support and now that they have their own foundation models and they've built up the rest of the ecosystem or they're building it. I feel OpenAI, we are going to see the same thing. OpenAI is going to say, this is my imagination, that we started with foundation models and now we're going to build out the ETL version of AI and the analytics version and we're going to have yet another player in our space that does not exist yet and that will be OpenAI. Well, I can certainly see where they might go into like horizontal tools, but again, I think where IBM is going to have the big advantages in the verticals. Everyone will have their strengths. Read Ben Thompson's assessment of OpenAI's launch. It was actually really well done and pretty interesting. Last question, we got a wrap guy so they're going to kick us out of here. Two-part question. What do you think of IBM's roadmap from what you saw today, part A and part B? What do you see as IBM's key challenges? Let's actually start with Merv and then we'll go to Tony and then Sanjeev, you bring us home. Merv, let's get your tank. Okay, Tony, bring it on. I think the big challenge is putting all the pieces together. I mean, I was impressed with the roadmap and then basically you gave me kind of the clue that I think that IBM is going to get a lot more vertical. I mean, it gave me that clue. I can't say one way or the other, but it gave me a really strong hint that that was going to go that way. And so that was really what kind of impressed me is the new kind of potential there. But again, I think what's really important on the roadmap is that IBM has a lot of assets. And as I said, the obvious example that just sticks out is basically, you know, it was with CloudPak for data and Watson X sub-table. I'll throw another one out there, which is looking at governance. I am waiting for the point where somebody only cracks basically how to essentially converge model governance, AI governance, data governance. The fact is that two are just, they're intertwined. Because thing is like, if a model is going, if it looks like a model is going off, the question is, was the model fine, but the data was starting to drift or vice versa or some combination of both. And from that standpoint, I'm really interested to see what IBM is going to do with their Manta acquisition. And I think there's going to be doing a briefing on this next week. It supplies, it provides a very key source of data lineage, which if you're looking at, you know, tracking models and tracking data, tracking the lineage of both, this is going to be a key ingredient. I want to see that integrated into Watson X dot governance.  That is one of the big blockers to invest in this space. Well, thank you for that. And then Sanjeev, you bring us home now. All right, so first of all, I don't think IBM shared with us a very clear roadmap. So some of this is just my own learning from today. Vector databases were not mentioned, but when I asked around, I was told they're coming. So that's on the roadmap. Once one statement from Rob Thomas that really stood out for me, he categorically said, our models do not hallucinate. And I think what he means is that because these models are so curated for certain use cases that they are very confident in their model's quality of answers. However, models are based on probability, so they will hallucinate if you expand the use cases. And I think IBM will discover that as their models get deployed more and more, that hallucination problems will show up. The last thing that I wanna say is that I was asking about PowerPC, there was so much talk about their own chips and nobody wanted to even talk about PowerPC, the chipset they used to have. So it's all about the open chips from commercial parties. But I promise you a lot of their expertise that they're using today in AI chips is coming from power. Power is actually quite a difference. You know, the way it does memory management and it's just look at power just didn't have the volume that x86 had in our course arm. Well, we took the tour of Watson lab and looked and so the quantum machine. Someone actually asked, have you taken that experience from mainframes and applied it to doing all this, this ultra cooling with the quantum and the intercept unqualifiedly? Yes. No doubt we are kind of back to the mainframe future that pendulum swings. And to me it'll just close the big challenge is really awareness of the scope of IBM's capabilities. And really it's ability to execute on translating that research and development into the income statement. I wanna see that cycle get compressed and at the same time really be productive and measurable. Any vendor that's listening to theirs are watching this video and they're thinking that we also are going to go to market with our own AI story. I'm sorry, it's too late.  If that's how you lead, IBM has an advantage because it has a whole stack and it has hide it for decades. So the future belongs to the organizations of vendors that have the complete story to tell, not just, you know, I also do generative AI. Everybody's doing generative AI today. You know, I got to say Sanjeev, Tony and Merv, they become great friends of the cube collective and we're really grateful for their collaboration. It was a little challenge with the lights and with Merv having to leave early. Really appreciate these guys spending some time and contributing to the cube and our community. Tony, by the way, just wrote a great piece on SiliconANGLE reminding us that there's other AI beyond gen AI. So check that out. Okay, that's it for now. I want to thank Alex Meyerson and Ken Schiffman. Alex is on production and manages the podcast. Ken Schiffman also does production. Kristen Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob, oh, it's our editor in chief over siliconangle.com. And remember, all these episodes are available as podcasts wherever you listen. Just search, breaking analysis podcast. They publish each week, wikibon.com and siliconangle.com. You can email me at david.volante at siliconangle.com or DM me at dvolante or comment on my LinkedIn post. And by all means, check out ETR.ai, the best survey data in the enterprise tech business. And also check out the cubeai.com, the cubeai.com. It is now out for public consumption. The cubeai.com, check it out. It is essentially ask the cube. Love your feedback. This is Dave Vellante of the cube, research insights powered by ETR. Thanks for watching and we'll see you next time on breaking analysis.


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