It seems there's a big shake up on the underperforming Gemini side. Before there was Shazeer (already gone) and Vinyals as co-leads, reporting to Hassabis, now Gemini comes under Kavukcuoglu reporting direct to Pichai as SVP of DeepMind.
Hassabis seems to have been pushed aside. He had been CEO of DeepMind, but that position no longer exists and it seems Kavukcuoglu is now leading DeepMind with a title of SVP. Hassabis is now just "Chair" of DeepMind, and has been given the newly created title of Alphabet Chief Scientist. Are these just face-saving titles, or does he still have any real influence over Google/DeepMind's pursuit of AGI?
Shane Legg remains as DeepMind "Chief AGI Scientist", but I wonder if the DeepMind founding mission of creating AGI is really intact, or if he will be next to go. Has DeepMind just become the Gemini division?
I just found out that David Silver, DeepMind's RL-expert, already left in february, to create a startup "Ineffible Intelligence" focusing on RL-based continual learning.
Yeah he seemed too reasonable to me, relative to the fervor. Whoever ends up in charge needs to do a lot of frothing to catch up to the ferver that would justify their valuations and investments
Probably not going to happen, but I'd love to see Isomorphic Labs separate from Alphabet with Hassabis still as CEO. He's too pure minded to be at company like Google.
He won a nobel prize for his work on protein folding while at Google...it seems like they give him a lot of room to explore problems that aren't directly related to serving ads
But it was Google fault that they really cannot productize all that innovation that happened DeepMind. Google probably now fumbled with world models and this exodus will create next giant in that space.
Hopefully not - Isomorphic also have external investors (maybe 10-20% ownership?), who may be willing to buy it outright from Google if they lose interest, and Hassabis is now said to be "leaning into" the role of Isomorphic CEO, which is at least pursuing his goal for scientific advance via AI, if only in one specific (important) direction.
Google's capex is to capture the compute purchases from the other labs. The amount they are spending on Gemini is probably shockingly low...and hence researchers leaving for better watered pastures.
> The amount they are spending on Gemini is probably shockingly low
IDK, haven't Google been putting Gemini front and centre in pretty much all of their products?
I'm seeing Gemini on my slide decks, Gemini on my e-mails, Gemini on my searches, Gemini on my videoconferences, Gemini on my database query console. My impression was they were doing a Google Plus style attempt to marshal all the company's efforts behind one product.
Their capex is primarily in building DCs (and what goes into them), which they fully intend to monetize in order to capture that $500B backlog. In that context, the amount of new capacity that will be given to Deepmind could be surprisingly small, since the ROI of selling pickaxes rather than mining gold is so much better right now.
Not true, they get paid at least a whole level (or two) above what we make. When I was discussing offers a few years ago, the GDM candidates regularly had six figure differentials even though they were the same level.
Maybe this isn't the same as the eight figure comp they'd get at Meta when they did their hiring spree, but no one thinks that's sustainable.
Of course it does matter what other companies are paying, but I wonder how many DeepMind employees actually deserve to be paid more than any other "rank and file" Google employee? What unique skill do they have (esp. relative to what Google are now doing), and what value are they creating?
I don't get the impression that the difference between one company succeeding to build SOTA LLMs, and another struggling, comes down to individual employees - it seems to be more about the organization itself and their ability to manage teams and projects of this type. No doubt there are a few rockstars generating huge value, such as Noam Shazeer had been, but they are the exceptions.
When DeepMind was first created, before Google acquired it, they were famous for the high salaries, especially for the UK, but this was an assemblage of the brightest and best PhDs, expected to be solving challenging research problems along the unknown path to AGI. Many of these original employees may still be there, but it seems their job and value proposition has changed - are they any more capable, or key to, helping Gemini catch up with the competition than some "rank and file" employee familiar with LLMs? And if so, why haven't they done it?
>the fate of the company partially rests on whether deep mind does well
I'm honestly not sure about that. They need a decent LLM to fight off the threat of LLMs replacing search, but I'd say that Gemini 3.6 Flash is more than good enough for that, with a smaller/cheaper model being preferred to a larger one. If you are "searching" for a proof to the Jacobian conjecture, then try Fable, and I doubt Google will miss the advertising revenue if Anthropic manage to sell Terrance Tao a pair of socks.
More to the point, DeepMind seems to have become a product division charged with building LLMs, not a blue sky research institute chasing AGI. To the extent that continued LLM improvement is important to Google, the relevant question is how much do you need to pay for a competent ML/LLM developer?
>you sound like an MBA
Well, no - techie here.
I wasn't sure if you were suggesting that DeepMind should pay more just because people developing LLMs at other companies are paid more, or because these are elite DeepMind researchers and are objectively worth more than other Google developers. My point being that it seems they are no longer being used as elite researchers - they are LLM developers. Does Meta need to pay FAANG salaries to employees that have been repurposed as data labellers?
this is roughly how i've been reading their approach, but i wonder when/if this changes. what actually makes them start caring about having the most capable model? maybe nothing. i suppose they could be happy in a world where they (a) are still the agents' preferred search index and (b) own or produce a good percentage of the hardware that anthropic and openai run on.
> i suppose they could be happy in a world where they (a) are still the agents' preferred search index and (b) own or produce a good percentage of the hardware that anthropic and openai run on.
If the goal is to maximize share price (which it is) this is probably the safest approach. Why do they have to keep chasing developing the best model when they can
a) charge everyone for the cloud infra
b) have a "good enough" experience for normal consumers
Their current setup will be worth trillions. Already is. Let anthropic get paid for the most expensive queries while they get a bill from Google for their cloud/TPU use.
GCP grew 82% YOY with 24B revenue and improving margins. So GCP became a 100B business. Anyone doubts it's gonna double in less than 5 years?
Gemini just needs to be good enough for normal folks who want personal agents for everyday use. I don't think Google has to compete with Anthropic on making the best agentic programming model.
CEO of <thing> at Google (not Alphabet) was always an informal title. There is no CEO of Cloud, CEO of YouTube, or CEO of DeepMind within Google internally -- it's always been an SVP role.
Roughly two years ago suddenly Hassabis was heavily promoted on all Google YouTube channels.
It was to fight ChatGPT and promote Gemini.
I think the guy does a very poor job or is simply not the right guy to appear as public figure for Gemini.
At least he tried. He is a man for everything that is not filmed.
Google doesn’t really have a person to give Gemini or AI a human face. And that is only consequential because Google never had any public person with any charisma like Jobs, Zuck, Altman.
Inevitably, this will lead to Sundar Pichai replacement. I see nothing less. The sooner the better. I can't even tell what Google's focus is, is there any even?
> and has been given the newly created title of Alphabet Chief Scientist. Are these just face-saving titles, or does he still have any real influence over Google/DeepMind's pursuit of AGI?
Alphabet Chief Scientist doesnt sound like a demotion / lack of influence to me but who knows. We're all just speculating here.
I think it's more than that. I made prediction in earlier 2024 that the main players of AI will stick with transformers while second class players will want to transcend it. The difference is admittedly a bit subtle but ai researchers would get it. I wrote it with mamba in mind back then, but google was still trying to come up with the 'next transformers' and one that can remember using weights and all that stuffs. You can say the same abt lecun's and ilya's now.
My main reasoning was that transformers was the lightning in a bottle and the best work is in extending it instead of transcending it, which requires you to capture another lightning . Which to me appears to miss the assignment. OpenAI, Antrophic, they understand this intimately. Google on the other hand, fell victim to their own ambition.
If your goal is purely commercial, or time critical, then a product-based approach of squeezing all the juice out of LLMs makes sense.
If your goal is truly human-level AGI then this is more of an open-ended research endeavor, and timelines are hard to predict. Arguably we have only "captured lightning in a bottle" once in the last decade - the original 2017 attention paper - and so the timeline for a "few more Transformer-level breakthroughs" might more realistically be estimated in decades rather than years. You could argue that the application of RL to LLMs as a training method was a second "lightning in a bottle" but I don't think it changes the expected timeline of such discoveries by much.
The time criticality seems to have become a huge factor for those pursuing LLMs, and certainly for OpenAI and Anthropic, who regard it as a race.
It seems absurdly obvious (though many would disagree!) that LLMs alone are not going to achieve human-level intelligence and cognitive performance (using a slightly broader term there to include things like creativity, for those that might not consider that as part of intelligence).
If you compare a Transformer to a brain, then the best parallel is that a Transformer is functionally similar - in being a prediction engine - to part of our cortex, but of course that means ignoring the other half our cortex - the feedback paths that enable continual learning, which in turn supports creativity.
Of course people will probably respond "you don't need flapping wings to fly", but if you want to fly you do need SOME way of doing it, so brain comparisons are still valuable... If you look at our brain architecture and identify all the components and connections that have no equivalent in a Transformer, and if the goal is human-level capability, then you do need to understand what each of those brain components achieve functionally, and have SOME way of providing that functionality in your LLM+ or whatever you call it. LLMs' lack of any functional equivalent to our cortex's feedback paths - lack of continual learning - has been recognized as one major functional deficit, but there are probably half a dozen others too, reflecting the multiple "Transformer level" breakthoughs that Hassabis notes are needed.
While you don't need flapping wings to fly, you do need to invent the airplane, and even after 100+ years of airplane advances we've yet to build an airplane even remotely as capable as what some birds and insects are able to achieve.
Maybe 2017 was the Wright Bros moment where humans first learnt to do some of what our brains can do.
Yeah, a bit surprised that the top comments aren't discussing losing Dean, he's been a figurehead for the company for decades. It's like Apple losing Ive in a way.
EDIT: "figurehead" - that's all I meant. A notable, public figure from the company who is credited with having a significant impact on its evolution. I'm not making a judgement call on his departure, or Ive's, being good or bad.
That said... just give it a couple years, they'll be back in a lucrative aquihire.
Losing Ive was probably a good thing for Apple. I often feel that after a few years these big guys have done what they could do and somebody else can take it from there. So far even losing Jobs didn’t hurt Apple as it looks.
The position that I have rather often read on the internet is: Jonathan Ive did very good work at Apple as long as there was a counterpart who could steer his creative vision. This counterpart was of course Steve Jobs. When Steve Jobs died, there wasn't such a counterpart anymore, so Ive's work for Apple got much worse.
I think this is true more often than many people want to admit. One of the most famous examples being Lennon and McCartney. Even though they couldn't stand each other near the end that tension helped produce their best music. They never did anything comparable as solo musicians.
Jony Ive wanted to chop down all the trees at De Anza Community College for something like 11 million dollars for an Apple event. There's a lot of ways he was demanding in the wrong ways for Apple
Man, I'd never heard that story, so I verified it. Turns out it was about two dozen trees and 25 million dollars, just to put up a big tent for an Apple launch event. (Yes, the trees were removed.) What a tool, he just lost my respect.
> Ahead of the event, Ive pushed CEO Tim Cook to remove two dozen trees from the De Anza College campus next to the Flint Center for the Performing Arts to erect an extravagant white tent for the hands-on area.
> So far even losing Jobs didn’t hurt Apple as it looks.
Who knows? Pure speculation? You can also say if Jobs was still around they could have 10x-ed it even further?
Apple car could have been a thing? Apple could have been way ahead and actually competing in AI and data centers? Who knows what else Jobs could have came up with?
Alphabet is only a holding company. I wonder what kind of authority he has over scientists at Google, Waymo, Deepmind, etc. Probably close to none, so a huge demotion in everything but in title and compensation.
To me the most significant part is that he's no longer in charge of DeepMind, the company he created, other than this "Chair" title which sounds meaningless.
When DeepMind allowed Google to buy them, it obviously had some major immediate positives - access to compute and money - but it seems it should have been obvious that the agreement was too good to be true, that they would be allowed to continue independently on their blue sky research mission to create AGI without any external interference or pressure to create product.
It seems that Hassabis and his DM co-founders eventually realized the mistake and tried to take DM private again starting c.2018, but of course this failed.
Now Hassabis has lost control of DeepMind altogether, and it seems to me, as a total outsider, that this is the end of the DeepMind mission to create AGI, at least the Hassabis/Legg definition of AGI as human-level general intelligence, capable of creativity and scientific discovery. Hassabis had always, until very recently, said that he believed it would take a number of additional "Transformer-level" breakthroughs to achieve this type of human-level AGI, while still seeing an LLM as one component of if (which to me seems an admission that the goal has failed - a true human level AGI should be able to learn language, etc, using it's own continual learning mechanisms).
It seems that DeepMind has now fully become the Google Gemini (LLM) division, trying to create a me-too product.
In the early days of DeepMind, before Google, before LLMs, I remember a David Silver slide deck titled "Reward is all you need", referring to RL rewards, which I never agreed with (although Rich Sutton might), but does at least reflect the independent thinking at DM, and of course RL not only gave rise to AlphaGo, but has now become central to the continued improvement of LLMs. However, notably David Silver also left DeepMind earlier this year, to found his own startup focusing on RL-based continual learning, presumably feeling that there was no longer a place for that type of research/pursuit at DeepMind.
Still, LLMs seem to be a destructive enough force on their own that perhaps it should be seen as a positive if research towards more powerful AGI appears to have had a major setback.
As for the "Alphabet Chief Scientist" title, it seems somewhat irrelevant, as least as far as Google's pursuit of true AGI. Hassabis is the face of beneficial AI, having been Knighted and awarded a Nobel Prize for his work, and it would be a horrendous PR move for Alphabet not to at least appear to be treating him with respect, even if in fact this does reflect him being pushed aside.
> The insight driving the program, Naga said, is that the limiting factor for AV development is no longer the underlying technology. “The bottleneck is data,” he said. “[Companies like Waymo] need to go around and collect the data, collect different scenarios. You may be able to say: in San Francisco, ‘At this school intersection, I want some data at this time of day so I can train my models.’ The problem for all these companies is access to that data, because they don’t have the capital to deploy the cars and go collect all this information.”
You can’t be the CTO of Uber wanting to do AVs, and get the data collection requirement shockingly wrong.
Waymo is able to deploy with less (but targeted and high quality) data collection by having world class simulation capabilities. Not that they haven't collected huge amounts of data as it's no doubt important (I've heard their onboard storage is transferred and emptied every few days), it's just not a bottleneck. They have the most efficient operation in the AV industry.
The best example of why data collection isn’t the bottleneck is Tesla. They boast about billions of miles of data, yet they’re struggling to put out fully autonomous vehicles.
> When they want data about a school intersection in SF at a certain time of day, they just... synthetically generate it and simulate
I think it's more about detecting changes to the world. You need boots on the ground, so to speak, to see that new speed limit sign or the new lane paint. The Waymo vehicle can no doubt react to changes in the world when it encounters them, relaying them back to the mothership, but it's better to know about them in advance.
Most AVs, definitely Waymo vehicles, are self mapping. They can detect environment changes and relay it to the entire fleet. That's because they map using the same vehicles as the fleet.
I'd be surprised if this is a thing outside the biggest US (and European, for that matter) cities, judging from Google StreetView there are lots of streets in US cities/towns with almost no paint lines at all.
Do you mean in the API? I live in an European country and I don't think I ever saw an asphalt road without paint lines. This varies a lot between countries though.
Small country side roads routinely lack a central line in Sweden. Even smaller roads can lack the side lines too. And I'm talking asphalt roads here still. The same happens on many residential streets in towns and cities.
But sure, it would be rare to have a large road or street without markings. But most roads aren't large. Most travelled kilometers happen on large roads, but that is not the same thing as most roads. And many individual journeys would involve at least a little bit of small roads at the beginning, end or both.
And of course, if they are covered with snow and ice during the winter you can't see the markings anyway.
Many American roads don't have lines. Residential roads, parking lots, many business driveways have limited markings.
Then there's roads with just the center line markers with no road should markings.
Then there's a whole class of roads of lines over "demarked" old lines that weren't demarked well, or lines fading that should've been painted a long time ago.
I'm surprised you've never seen a non-perfect road?
Here in Bucharest there are quite a lot of big boulevards that do not have them, either because they haven’t been repainted over in a long time or because they laid new asphalt without bothering to repaint the lines (this happens a lot, unfortunately, and is very frustrating).
That's true, but the human can do a much better job planning for the journey if they know what to expect along the way.
One example, from the end of the journey: knowing in advance where the actual entrance to the business is, or the specific curb cut that leads to the residence, makes it easier and far less error prone to decide exactly where the journey should end. Even humans have a hard time figuring out the right access point for a business or residence. This is a job for an offline process, fed by as many data sources as possible.
Yeah I'm not so sure this CTO is on the mark here, but to be fair, I do think some of this IRL long tail/edge case data is important for Waymo. The simulation software is super interesting to me - the real world can be so chaotic, and even if they could generate every possible real life case, there needs to be validation on whether the Waymo driver is responding in the optimal way. They certainly haven't solved this problem, you can see some of their growing pains in all of these articles - floods in Austin, more and more interactions with emergency vehicles that first responders seem to believe are getting worse, etc.
Tesla on the other hand has billions of miles of data, yet because there is a limit to camera-only techniques, that data isn't that useful is it? They have no ground truth data to evaluate their camera system on, which is why sometimes you see those Teslas driving around with lidar rigs mounted on them. Going camera-only is just asking for trouble.
I agree real world data is important for Waymo. I didn't mean to say it wasn't, so I've edited my comment to reflect that. It's just that data is not some magic bullet to achieve self driving like Tesla and others suggest.
Of course, Waymo still has much more room for improvement. But it's much more efficient to supplement less but higher quality IRL data with large amounts of synthetic data, than to run a million data collection vehicles 24x7 because most IRL data is boring and useless.
Waymo said 6 years ago they simulate 20 million miles every single day [1]. Clearly, it's working for them given their scale of deployment right now.
Although most of the real-world data is probably boring, collecting more of it likely makes discovering rare edge cases more likely. But since they happen rarely, I imagine that after discovering them, they would then need to figure out how to simulate them.
The biggest difference, is Uber has vehicles around the world. So there's more data from countries with different rules from the US. Signage is definitely different between the US and Europe.
I.. am amused by the confidence on display, but I can't say that I am not concerned that people are confidently stating that real world data is not useful, because it can be just simulated. One would think that, by now at least, we know that simulation is at best an imperfect copy.
And I don't like the idea of even more data being harvested and used.. I just find the dismissal.. odd.
Parent's post noted that it is not a bottleneck, because it can be readily simulated ( and thus not useful ). I am not sure if QED is too much in this case, but I stand by my amusement. Or are you arguing that real world data is somehow less useful than simulated data? It is very confusing. I would accuse of nitpicking, but I just noticed you are the parent:D You can certainly speak for yourself.
Again, I’m not suggesting this. Bottleneck has a specific meaning. It means Waymo is limited by not having the ability to collect data. Well, clearly that’s not true because Waymo already has a reasonably scaled deployment across a dozen cities that no one else has and can handle millions of scenarios.
Real world data is absolutely required, but more of it doesn’t give you magical self driving ability as Uber’s CTO suggests. If it were the case, you’d see Tesla achieve fully autonomous driving years ago.
> The best example of why data collection isn’t the bottleneck is Tesla. They boast about billions of miles of data, yet they’re struggling to put out fully autonomous vehicles.
Well, TBF, the tesla data was complete garbage with earlier vehicles. They had cheap and somewhat bad cameras in the earlier vehicles that was only somewhat recently updated. And even then, I don't think Tesla is at the end of their hardware journey. I think they don't think that either, which is why they've gone to a subscription only model for self driving vehicles.
Waymo, on the other hand, has gathered less data, but more high quality data. They do the expensive mapping of a city which is a big part of why their vehicles have early on been able to do some pretty impressive feats. The drawback is getting that high quality data takes a lot of time and resources.
> And even then, I don't think Tesla is at the end of their hardware journey.
I dunno about that. Tesla seems completely adrift, pretending to pivot with random forays into humanoid robotics or whatever, to the point that I wouldn't be surprised if they exited the consumer vehicle space altogether within the next decade. They have no answer for Chinese competitors.
I recently watched some videos related to the production of cybercab, which has now started public testing. They’ve still done some great engineering, to the point that the car is now assembled like a matchbox car. All the drive components are contained in a single package for a FWD configuration that the body just drops down on. The car now has no controls besides the screen and door pulls. The materials are all lower cost and they even found a way to skip painting the cars. All of this should help them cut costs significantly.
As far as the self driving, they may be far off still, it’s hard for me to get a read on that and this vehicle is a bet that they will be able to achieve it - right down to the braille in the cabin, so maybe that’s why they still fail. The thing I will say is that despite the PR disaster that the CEO is, which gives us that feeling that the company has lost its mind, it seems they are still quietly doing some advanced engineering.
Didn't they need the data from the 200 million miles or so from actual driving before they could get to the generative model though? Data isn't everything, as you point out with Telsa (mainly because they decided to forego using lidar it would seem), but it is pretty fundamental.
> Waymo might very well be missing specific kinds of data (e.g more incidents/accidents, near-collisions etc)
Accidents and near-collisions are exactly the kind of scenarios perfect for simulation. You don't test them out in the real world and risk injuries/deaths. You need to have confidence they're handled before you deploy.
I find the idea of learning from simulated data so unintuitive. How can you radically improve your model with just your model? I take it people do it, so it must work, but i just don’t understand it at all.
Well there's a world simulation model and then the driving model.
You can imagine improving i.e. a specialized math model (problem in, theorem out) with a normal LLM that knows lots of problems and theorems generally.
I think people are skipping over the fact that Google has had cars driving around taking photos for 20 years. I imagine that was used to build the world model in the first place.
>Mapping out every intersection, sign, and signal
Before our Waymo Driver begins operating in a new area, we first map the territory with incredible detail, from lane markers to stop signs to curbs and crosswalks. Then, instead of relying solely on external data such as GPS which can lose signal strength, the Waymo Driver uses these highly detailed custom maps, matched with real-time sensor data and artificial intelligence (AI) to determine its exact road location at all times.
That AI part is doing a lot of heavy lifting. They're using real data. We already know synthetic data is dangerous. Explains a lot of if you think it's more reliant on that than real data.
Mapping and simulation have very different purposes. Doesn’t look like you’re familiar with the basics of AV technology. Explains a lot why you’re confused about how real world and simulated data is used.
This is fascinating. I feel like this is converging into the concept of a traditional "IDE". So much of your setup reminds me of IDEs indexing, doing static analysis, building ASTs, etc. before a developer starts writing code.
> Before Waymo deploys in a new city, it deploys a huge fleet of cars that spend months of driving completely supervised, presumably to construct a detailed LIDAR map of the city.
Not entirely true. From their recent "road trips" last year, the trend is they just deploy less than 10 cars in a city for a few weeks (3-4 weeks from what I recall) for mapping and validating. Then they come back after a few months to setup infrastructure for ride hailing (depot, charging, maintenance, etc.) and start service.
Waymo drives 4 million miles every week (500k+ miles each day). Vast majority of those collisions are when Waymos were stationary (they don’t redact narrative in crash reports like Tesla does, so you know what happened). That is an incredible safety record.
We all know Tesla likes to play smoke and mirrors game with vehicle numbers — they have 300+ "robotaxis" but only 7 of them are unsupervised [1], and they shut down when it rains [2].
So let's use a metric that unequivocally shows who is 'winning'. I'm confident Waymo will have more paid rides per week than Tesla at the start of 2027 (I'll give you 2028 if you want). No other metric indicates scale better than passenger trips. If you have more robotaxis or you are in more cities, it will show up in the trip count.
I'll give $1000 to a charity of your choosing if Tesla beats Waymo in this metric. Fully unsupervised trips only, does not include trips with a safety driver or a monitor in a passenger seat, none of the usual games they like to play.
Pay close attention to the wording: "The Waymo Driver ... remains in control of driving". That means it applies the controls needed to go from point A to point B on its own. However, it does not choose point A and point B on its own: a human chooses them. That's autonomous path planning, but not autonomous navigation, and certainly not "fully autonomous" anything.
Waymo prevaricates about the "influence" the human operator has on the path taken by the Waymo Driver [1] but it is clear there are situations that the Waymo Driver cannot choose point A and point B on its own, at least safely, otherwise Waymo would not be paying for humans to do it. They'd let the system do it on its own. It can't. It's not "fully autonomous".
We can play with words and accept whatever terminological obfuscation Waymo wants to impose in order to pimp its wares, or we can accept that current systems have limitations, and choose to understand the real SOTA over marketing.
_____________
[1] Fleet response can influence the Waymo Driver's path, whether indirectly through indicating lane closures, explicitly requesting the AV use a particular lane, or, in the most complex scenarios, explicitly proposing a path for the vehicle to consider idib.
> Tesla is executing the strategy that most quickly scales to 100% of the population.
So, uh… where is this “scale” then? This “strategy” has been bandied about for better part of a decade. Why are they still in a tiny geofence in Austin with chase cars?
Waymo is doing it right now. Half a million rides every week, expansion to a dozen new cities. Tesla does a few hundred in a tiny area.
Scale is assessed by looking at concrete numbers, not by “strategies” that haven’t materialized for a decade.
The bigger deal is the departure of Jeff and Sanjay, rather than Demis moving into a different role.