Exploring the frontiers of Technology and AI
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When it comes to AI, Google is a company that you would expect to see at the top.
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And yet over the last six months, they've suffered a bit of a fall from grace.
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Their flagship model, Gemini, which peaked at the end of last year,
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has suffered delay after delay after delay, with companies like Anthropic,
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OpenAIR, and even Meta running complete circles around them.
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I think 15 brand new AI models has dropped since Google's last one has.
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But the bad news doesn't stop there. Google has also recently lost key members
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of staff from their AI team, the most prominent one being Demis Hacibis,
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the AI CEO of DeepMind, who started the whole thing at Google.
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He's had to step away to focus on other things, but they also lost their chief
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AI scientist, Jeff Dean, who, for all intents and purposes, is the godfather of Google.
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He's responsible and the man behind 90% of Google's hit products,
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including the TPUs that power Gemini and power other models like Claude and ChatGPT.
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As well as having his hands in several other of foundational research papers
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that has given birth to modern day AI as we know it.
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That guy also left citing reasons that he can't do the work that he wants to
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work on at Google anymore. And so the public reaction was understandably catastrophic.
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The stock dropped 5% over a matter of hours, losing the company tens of billions of dollars.
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But recently, I think things have started to shift. Now, of course,
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Google in itself, on its own, without all the AI stuff, is a massively successful
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company. They're the third most valuable company in the world. I'm not negating that.
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But if AI is set to be the future, it's important that a company is able to pivot.
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When I look at the likes of Meta, when I look at the likes of Amazon,
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although they're operating in different spheres, they are able to focus and
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spend so much money and time and resources in trying to figure out the AI thing.
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And Google started very much in the same way. They've been arguably the longest
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company in this game spanning back decades and decades.
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And so after this recent shift in staff, time and resources,
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it's important that they're able to get their foot back into the race.
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And we might be starting to see it.
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Just last week, they released Gemini 4 Argon, or at least they announced it.
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And if benchmarks are anything to go by, this model is pretty powerful and can
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compete with the best of the best from Anthropic and OpenAI.
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But they also recently, in fact, today, as I'm recording this,
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released Nano Banana 2.1, their brand new image generation model.
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So we're starting to see more releases from Google.
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And on this episode, I want to dig into what's happened, where Google is at
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right now, and what the future might look like. All of this and more on today's
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episode of In The Loop, brought to you by Qualcomm.
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Now, very quickly, before we get into it, you might be wondering,
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Ejaz, this is a different setup and you're missing a co-host.
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There's a ton of updates that have happened for the show.
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Go and check it out on our video that we posted two days ago.
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It's on our channel and we'll explain everything there. But long story short
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is Limitless is evolving into a new brand refresh called In The Loop,
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but it's very much the same DNA of the show. And we're going to be coming to
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you every single day of the week with the latest and greatest in AI and frontier tech.
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All right. So now setting the stage for Google as a company.
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Now, for the last seven months, Google has basically had their best model underperform
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every single flagship product that Anthropic and OpenArea have put up.
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And I mean that for every single model, including Opus, including Sonnet,
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which are typically the cheaper, less impressive tiers from Anthropic,
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including Sol, including Luna and Terra, which again, are the lowest equivalents
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coming from open air, their cheapest and technically worst models would still
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outperform Google's best.
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And that's weird, because at the end of last year, Gemini was on top and they
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had a meteoric comeback where they kind of had the lead back in the day before GPT came out.
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And then GPT came out and took the world by storm. And Google was criticized for not being able to.
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Stage a comeback and actually catch up with all the research that they've done.
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And then they released Gemini and the world's opinion changed on it.
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It became deeply integrated into every Google product that you could use,
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including Google search.
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And it just became synonymous with using Cloud or ChatGPT. But something changed since then.
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And I don't know whether it's a research problem, or whether it's a staffing
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problem, or whether it's just generally a product company focus thing,
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but they weren't able to ship a competent model.
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Now, speculation behind that is at the same time that they started to announce
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delays, Claude Code hit an absolute exponential run. And this,
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of course, is the flagship product from Anthropic.
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And at that time, AI coding was kind of this mysterious, weird thing.
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Companies didn't really know whether they should focus on automating coding
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or not, whether they should focus on automating research or other such things.
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And Claude was the very first instance where it became clear coding and AI are a natural fit.
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And that is where the leading edge is going to be figured out.
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OpenAI figured this out about a month after Claude Code launched and we shifted
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everything to build their own competitor called Codex. And since then,
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they've been able to catch up. And you could argue that's why they've been able
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to build much better models that stay on par with Claude.
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And now it's Anthropic versus OpenAI, because they're using these models to
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build the next better versions of themselves, something called recursive self improving.
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Google, People unfortunately watched all of this happen and thought.
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I think we can wait a bit. We can try and figure out all these other types of
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products that we've already committed to on our product roadmap and we can come back to coding.
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And that ended up costing them pretty greatly, which is a shame because Google
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has some of the biggest distribution on earth. You're seeing on the screen here,
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their market share value is around $4.1 trillion, making them I think the third
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most valuable company in the world.
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And they have around 3 billion users across all their different products.
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It's very important to state that there is no other company like Google that
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has such a large product surface area across both general consumers,
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so the public like you and I,
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and enterprise customers that use their products pretty aggressively to document
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stuff on Google Docs to create slides or to manage their databases via Google Sheets.
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So pretty much wherever you are on the internet, whether you're searching for
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something, whether you're trying to produce something, whether you're trying to analyze something,
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there's always a very competitive google alternative which is why then when
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ai exploded onto the scene you would expect them to be able to pull off a similar
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stunt and they actually were able to with gemini.
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But they haven't been able to keep up but don't take my word for it i mean look
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at the screen when we look at the artificial analysis index which is a really
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good general benchmark to try and test the intelligence of the latest model
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from these frontier companies
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you see clawed opus 5.5 which was released about a week and a half ago score
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58 you've got gpt6 astra scoring a very close 53.
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And then you have Gemini 3.1 Pro, which was released back in February,
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which in AI years, funny I say
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years, which in AI timeline is years and years and years worth of time.
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So you wonder why hasn't Google been able to ship an effective product?
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Well, the answer is, I think there is a work culture problem because starting
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August 5th, but actually even before that, they started to bleed a lot of very
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important talent. And we covered this on previous episodes.
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We mentioned that John Jumper, who shared a Nobel Prize with Demisus for building
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AlphaFold, which is basically their protein sequencing DNA AI model, had left to join OpenAI.
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And then Jeff Dean, who I mentioned earlier on in the intro,
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who is kind of like the chief scientist and grandfather at Google for a lot
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of the other products that aren't just AI related, just an absolute invaluable
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person at the company, left to start his own AI lab.
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I think it's called Discovery Loop, which is focused on automating research,
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first starting in AI, and then other parts of research in physics and science.
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That is exactly what the Frontier AI labs that are leading this entire field
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are doing and focusing on.
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So my question then becomes, why on earth did a loyalist like Jeff Dean feel
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the need to leave when he could have easily built this at Google?
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I think there's a work culture problem going on. So it led to this massive exodus.
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Demis' service had to step down and focus on something else.
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And so what we're left with is a completely bare team that needs to figure out
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a completely new strategy.
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But everything I'm saying, I admit, sounds very Doomer-esque for a company that
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is the third most valuable in the world and is making so much money.
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Because when you look at the stats, right, they have over 1 billion in AI mode
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for search past a billion in AI overviews, which have reached around 2.5 billion.
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And then when you look at the amount of tokens that they process for their AI
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products or across all of their products, it's 22 billion tokens a minute.
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They've also reported 82% growth
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in Google Cloud, which is their AI CapEx infrastructure business where
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they're spinning up TPUs, which is their own version of NVIDIA's GPUs,
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and using it to facilitate selling training and inference costs to other AI
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labs or firms that want to train and raise their own models.
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And again, putting all of that aside, they have a humongous enterprise business,
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8 million Gemini enterprise paid seats on their AI products alone.
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That's not even including some of their Google Cloud enterprise products.
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So for all intents and purposes, is Google is an absolute cash-generating machine,
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and I'm not negating that. They have one of the largest cash balances on Earth for any company.
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But they've also been spending very aggressively to try and acquire and build
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as much compute data centers to pay for the expensive capex costs that they're taking.
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But there's a bit of a mismatch. Google right now, as they're positioned,
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is focused to be a really good
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hyperscaler or infrastructure provider, akin to maybe a neocloud that builds
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AI data centers or builds the chips, supplies them then to AI labs that can
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use them for inference costs or training their own AI models.
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And that is all fair and game. But Google at its core DNA is not just a research lab.
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They're this perfect hybrid of a research lab and a product company.
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That's why they've been able to become one of the biggest companies on Earth.
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And that's why people love Google as a company and as a business.
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They haven't been able to translate this for AI yet.
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And you can argue that if they maintain this kind of product or working culture,
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then they're going to end up just remaining a hyperscaler or a neocloud,
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whilst companies that actually build the net new products like AI models,
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like AI agents that we've spoken about on previous episodes,
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will end up benefiting from the economic opportunity that just explodes over
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the next couple of decades.
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So with that said, now has never been a more important time for Google to shift
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gears and try and figure out an alternative strategy to get back in the AI race.
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I personally want to see Google win because they have such a huge advantage.
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They're not just a singular monolithic company.
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They have fingers in every different pie that you could think of when it comes to the internet.
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They're the most vertically integrated company that doesn't exist for any other
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AI lab. When you think of OpenAI and Anthropic, yes, fantastic models,
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but they lack the distribution. They don't have the billions of users.
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When you look at Meta, they have all the billions of users, but they don't have a bleeding edge model.
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Google, with all their wealth, resources, and expertise, should be able to pull
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off a miracle and have the best vertically integrated AI product.
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They have all the data to kind of mold that into the best consumer experience
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or enterprise experience depending what kind of a product they want to build.
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But the result of what we've actually seen is very fractured teams being divvied
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up, very specific resources that don't really make sense.
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This team gets compute, but the other team doesn't get compute.
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Or another team gets way more compute, but it's spent on a product that maybe
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may not have as much impact on millions of users versus a couple hundred thousand.
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It's just this weird thing. And then they're selling compute to competitors
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when they probably should be using it to train their own model just to make
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cash in the near term so that it might look good on a quarterly earnings report
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versus trying to play the longer game.
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And so the question then becomes, okay, what moves will Google make under this
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new realm of losing half the AI talent and trying to figure out a new structure and approach?
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Well, we might see it with a brand new model. They announced Gemini 4 Argon,
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which as the Google post says, delivers frontier performance in complex workflows
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across real world software engineering, knowledge work, and cybersecurity defense.
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Now, of course, The first thing that I looked at that Sundar Pichai,
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the CEO, highlighted was their benchmark evaluations.
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Now, if you're watching the show, if you've watched it for a while,
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you know we're not big benchmark guys, but it gives us an idea of what this model might be.
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And so the first thing that draws my eye is, okay, it's beating GPT-6 Astra
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and Claude Fable 5.1, which are each respective company's flagship model model.
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Pretty well on a number of different things. But then you have to look at what that thing is.
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Now on the left here top, it's knowledge work talking about VALS index automation
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bench knowledge work is basically if you think about 70 to 80% of the tasks that are required for a
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software based job, and I'm not talking about software engineering, but I'm talking about
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anyone that has a nine to five that uses a computer that uses slack and a bunch
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of these other different CRM tools or whatever that might be,
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you might need to access a spreadsheet or write up a strategy document or type
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messages to different workers in Slack or customer support, this model
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excels across any other frontier competitor model, which is fantastic,
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which honestly applies to a much larger percentage of the world than some of
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these other benchmarks and what they test for.
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But moving on to agentic coding, which is kind of like the creme de la creme
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of benchmark tests, it excels in DeepSuite version 1.1, which is effectively
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the AI agent version of coding.
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There's two types or different ways of testing an AI model and how good it is at coding.
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There's the monolithic version where you just ask an AI model itself to try
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and figure out a complex coding task.
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And then there's a version where you can allow that AI to spin up multiple versions
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of itself and do what is known as agentic reasoning, which is work between multiple
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versions of itself to try and figure out a solution to the problem.
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DeepSuite is exactly that. And when you look at.
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Fable 5.1 and gemini 6.1 astra very impressive and then across other things
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like science math long context computer use it does a very good job especially
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when it comes to multimodal reasoning so
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overall it sounds like this has been a major upgrade for google in general and
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if i pull up this chart over here we look at the artificial analysis index which
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i mentioned earlier google had been trading behind by like 25 points
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they have now jumped up to 53 which matches gpt6 astra So for all intents and
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purposes, this is a good model, but I can't give you a genuine review because.
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I haven't been able to get my hands on it. And that's because they haven't actually
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released it to the public for fear of causing a cybersecurity incident.
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Now, this has kind of become a recurring case across every AI lab that releases
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a new model, they train this model, and then they realize, oh,
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this thing could be incredibly misaligned, and could break out of a sandbox
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and cause a lot of disruption.
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So let me sandbox and test it with cybersecurity defenders first,
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let them get their hands on it first.
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I think the very first model that did this was Claude Mythos Preview back at
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the start of the year, which is ironic because we now have a bunch of open source
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models which are as capable as that.
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And we haven't quite seen any of them cause AI disruptions, at least any that
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we have been aware of. But the point still stands. We want to build and release
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AI effectively and safely, which I'm fully on board with.
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So Google is undergoing a process that I think was self-volunteered by the government,
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which I would guess lasts about one to maybe two weeks.
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So we should expect to see the public release sometime around a week from now.
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But some insiders as reported by Bloomberg has already got their hands on it.
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And listen, there's mixed reviews.
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One person goes, it does less well when employees actually put it to work.
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The model struggles to handle certain coding tasks. And this has been my whole
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thing around Google and Gemini models. And this is just a personal take,
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you might have a different one.
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But once you get your hand on the model and actually end up using it,
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it is a stark and very different experience to what the benchmarks actually
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indicate to you. So the benchmarks look very impressive in theory.
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But when you actually end up using it, you realize I'll just continue using
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GPT or continue using claw. Now I'll reserve my judgment until I actually get my hands on Gemini 4.
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For all intents and purposes, I'm excited to see Google come back in a very meaningful way.
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And especially in coding where they've honestly lagged behind for every other
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model iteration that they've had over the past year. So I'm remaining cautiously
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optimistic that they've been able to figure something out. All of this and more
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on today's episode of In The Loop brought to you by Qualcomm.
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But that's not all that they're releasing. They also released a brand new version
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of their image generation model, which is Nano Banana 2.1.
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Now, this is brand new. It released literally a few hours ago.
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So I'm not gonna sit here and pretend like I have access to a bunch of different
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demos because I haven't tried the thing out itself.
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But from the visuals and examples that we've seen so far, let me tell you, social media is cooked.
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These things are looking incredibly realistic. I'm pretty certain that there's
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no ladybug that has ever looked like this or creature or insect that has ever looked like this.
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Weird pink lucid neon bug would be kind of cool if it existed,
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but you know the humans are looking more human the reflections are looking way
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more accurate everyone has all their fingers in all the images there's no extra
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limbs or appendages added which is fantastic these models are getting really
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good and google has been in the game for multi-modality
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and by that i mean good image generation models
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great video models and fantastic world models i believe genie 3 we haven't seen
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an iteration since then, but they were leading world model front as well.
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The point is, Google is perfectly positioned to be putting out some of these
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products, and they've been slow or delayed to release these products over the last six months.
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But we're starting to see a comeback since then, and finally,
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a shift in product and work culture.
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Now, when I step back, and I think, okay, listen, from what we've seen,
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from the evidence over the last year, six months, and these products that have
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just released over the last week, how do we feel about Google?
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I stand by what I said earlier, which is they are a fantastic company that has
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so many different ways of making money. I think Google...
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For most companies, has an incredible privilege of having years and years of
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runway ahead of them. But it's only years.
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And a lot can change in those years. And we've seen the likes of Anthropic and
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OpenAI increase their ARR pretty stupendously over the last 12 months.
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We've seen Anthropic go from their expected forecast for this year was 10 billion,
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and they've recently reportedly hit 100.
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And OpenAI has also recently hit 70 billion ARR as well, the fastest growing
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companies that we've ever seen.
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If they continue that trajectory, and that's a big if, they should be able to
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catch up to Google's ARR in roughly about two and a half years.
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So Google has roughly that amount of time to figure their AI strategy out.
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And it's not just Google that's alone on this. You can point to companies like
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Apple that have been very slow because they can afford to take the time.
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What I applaud Google for is that they have taken the time to test out different products in public.
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And maybe they've fallen flat on their face sometimes, but they can build and
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iterate from that. So it's going to come down to the team, it's going to come
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down with how they spend their resources, and how they shift in reaction to
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losing half of their most important AI talent ever. Jeff Dean.
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Sucks to see you go, dude. And the final point I'll make is don't forget,
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Google is, again, the only major company that can vertically integrate the entire AI stack.
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They have the TPUs, so the GPU equivalent of training and inferencing AI.
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Then they have the AI models themselves.
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Then they have the distribution across billions and billions of users across,
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so many different interfaces, Google Maps, Google Search, Google Ads.
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They can just apply AI and change it overnight.
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So I still think the ball is very much in Google's court and I think they need
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to start acting much quicker than what we're seeing and what gives me hope is
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for the longest time for those of you especially who have watched this show I,
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have personally been bearish on a company like Meta and I never thought that
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they'd be able to pull it around and,
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they surprised me and they've come out with one of my favorite products this
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year in the form of Metamuse, a personal AI agent I think Google can pull off
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a similar stunt but it remains to be seen whether they actually want to and
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whether they will actually commit resources to be able to do that.
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That's basically it. That's my take on the whole Google thing.
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I wanted to give an update on Google pretty desperately because I miss it and
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I'm still very bullish on the company, but I've been struggling to try and figure
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out how they're able to turn things around. I've been waiting for a flagship
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model and maybe that's come in the form of Gemini 4 Argon.
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I can't wait to get my hands on it. If you're listening to this and you are
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from Google, please, I would love to play around with it and give my honest review.
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But that is that. It has been a crazy couple of weeks, guys.
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This is a brand new version of the show.
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And the idea here is that we keep the core DNA of everything that we've done
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here at Limitless. And we bring the best, most insightful takes and thoughts
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on all the top AI trends and news that is happening week in, week out.
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So if you enjoyed this episode and you aren't subscribed or you haven't left
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a comment, please do both of those things. In fact, do me a favor and even turn
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on notifications as well. It helps us out massively in terms of getting to a
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wider reach and audience and sharing as many opinions as we can.
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If you have a take that disagrees with me, I would love to hear from you genuinely.
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Like I am always trying to pick holes in my arguments and in my takes and in my opinions.
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But at the end of the day, I'm the same as you. I'm trying to figure all of
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this out in real time. So I actually learn a lot from the comments that you guys give.
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And if there's anything that you can teach me or if there's any alternative
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perspectives that you might have that I might have missed that compliment some
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of the stuff that I've talked about, let me know.
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If there is something or a different angle about Google that I've missed, I want to hear about it.
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You can DM me or you can leave a comment. I don't really care.
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I'll read anything. Finally, if you think a friend of yours or someone in your
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network might benefit from an episode like this, please copy the link and share it with them.
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I want to get this to as many people as we can. We are also on Spotify,
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Apple Music, and pretty much wherever you can listen and hear a show or podcast.
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And that is it. Thank you so much for listening to In The Loop brought to you
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by Qualcomm. I'll see you on the next one. See you guys.