(Preview) Nvidia’s Answer to Capital Constraints, Google’s Attrition and Direction, Q&A on AI Writing, Vision Pro, Vibe Coding

Ben and Andrew begin with Nvidia’s announcements of a new funding model for AI infrastructure, including the differences and similarities with railroad expansion 150 years ago, why LLMs were a gift and curse to Nvidia’s business, the pressure on Nvidia coming from Google and Amazon, and the expanded blast radius as Nvidia works to mobilize third party funding. From there: Why the turnover at Google may actually be a good sign for Google’s frontier efforts, and extended thoughts the future of AI-generated output, ideas and substantiation, and Anthropic’s plan to watermark outputs. At the end: A question about the Vision Pro, the obstacles for Starlink Mobile, and a cranky emailer yields a clarifying answer on why Ben is excited about his vibe coded app.

• 0:00Speaker 00

  Hello and welcome to a free preview of Sharp Tech.

• 0:09Speaker 00

  Hello and welcome back to another episode of Sharp Tech. I'm Andrew Sharp and on the other line, Ben Thompson. Ben, how you doing?

• 0:20Speaker 01

  I'm doing okay, Andrew. Kind of feel a little bit in a funk, you know, been some travel going on. It's kind of dreary outside. The brewers are terrible. Trying to figure out what is causing what. But uh, well, it's okay. We're here.

• 0:33Speaker 00

  But here we go.

• 0:34Speaker 01

  We'll make it happen. That's right.

• 0:35Speaker 00

  You know what I feel? I feel FOMO because we were together in Wisconsin last week and I feel like we could have put a call in to Mark Walter to see whether he was interested in selling the Lakers. Sounded like an asset he needed to move pretty quickly. Maybe we would have gotten lucky, could have beaten Kushner to the punch. Alas, here we are, humble podcasters once again.

• 0:59Speaker 01

  Uh, yeah, not to dive into totally random aside, but Josh Kushner, not Jared, Josh Kushner is now one of the owners of the Los Angeles Lakers. Thrive Capital is in a lot of, you know, they're kind of on the, the cutting edge, like the new generation of VC companies are doing very well for themselves. I do think their largest holding is OpenAI. So maybe the real bubble concern now is will anything happen to the Los Angeles Lakers if everything goes sideways? Uh, we'll have to keep an eye on it.

• 1:28Speaker 00

  God willing, that would be one benefit of the bubble bursting. So let's see what happens. For now, Ben, we're going to do all mail on this episode and I'll tell you why, because the last two episodes we've recorded, we've gotten so deep into various conversations that we've hit hardly any mail. So we'll try to remedy that today and we'll start with an article you wrote this week.

• 1:52Speaker 01

  I need to not monologue so much.

• 1:54Speaker 00

  That's right. Be on your Ps and Qs. Let's hit as many of these questions as we can. We'll see. Nvidia announced partnerships this week with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs, and KKR, a super team to establish an independent financing platform or independent financing platforms designed to mobilize over $500 billion of third-party capital to support the build-out of AI infrastructure over time. That's Nvidia's announcement. Part of that plan, as I understand it, involves shifting GPU depreciation risk away from traditional lenders in a bit of innovative financial engineering that I hope you can explain for me because I'm still a little confused what

• 2:39Speaker 00

  the plan is there.

• 2:41Speaker 01

  This is American greatness at play. Invent very expensive thing to spend money on and invent incredibly.

• 2:49Speaker 00

  Convoluted ways to pay for it. Sure. Great. God bless America. So Andrew, in response to the article you wrote about this on Tuesday.

• 2:58Speaker 01

  Is this Andrew in Washington DC?

• 2:59Speaker 00

  This is a different Andrew, although look, this Andrew also has lots of questions about what this actually entails. Andrew asks, can Cuda really generate earnings growth at a rate that outpaces depreciation of the GPUs? I'm being very unscientific about this, but it feels to me like there's an order of magnitude difference in there and not in Cuda's favor. The conclusion of your article on Tuesday carries echoes for me of the resecuritization of mortgage instruments into CDOs and credit default swaps that created the conditions for the subprime loans crisis and the global financial crisis. Do you see any parallels? In seeking to expand the breadth

• 3:42Speaker 00

  of available capital, is Huang creating the preconditions for a subsequent cascading collapse? Perhaps more interestingly, is there a feasible alternative? Or is this just the way the bubble expands? So what do you think, Ben? Take it whatever direction you prefer.

• 4:00Speaker 01

  Well, let me take whatever direction I prefer. We may look up an hour later and not got that far through our building. Um, there's like a macro question about sort of AI infrastructure generally. And then there's like a micro question about Nvidia specifically. And both are at play and I think what sort of what happened this week. So at a very high level, and this is where people do reach for the railroad analogy. Like I, I sort of reluctantly linked to it. It is such a good analogy this week, but just because like everyone's sort of talking about it. Um, so I had to sort of cite, look, even Simon Adel brought this up on his call. I've not had anything special.

• 4:40Speaker 00

  Everyone's reading the same book.

• 4:42Speaker 01

  That this is not, well, not just that, but I mean, people have talked about the railroad thing for a few years now. The book just came out this year, which is sort of fuel on the railroad analogy fire. But where the railroad point is interesting is the fundamental issue that happened in, in 1873 is the world ran out of money. Like which, you know, we've talked about the running out of compute. We've talked about running out of power, but the issue at hand here is what happens when you run out of money? And that sounds like an incredible thing to say, given how much money there is in the world. But we talked on this podcast even a year ago, like not that long ago,

• 5:23Speaker 01

  about, well, you can't really call it a bubble when these companies are paying for this out of their free cash flow, right? Like there, you know, what's the spillover?

• 5:32Speaker 00

  Sure.

• 5:33Speaker 01

  That we're worried about.

• 5:34Speaker 00

  What's the risk they're assuming in that scenario?

• 5:36Speaker 01

  That's right. It's like, once we start getting into debt, then we need to have a conversation. And the crazy thing is, we sort of blew through debt in like nine months. Like the, the amount of debt that was raised in the second half of last year and the first half of this year is in the hundreds of millions, probably soon to be approaching like a trillion dollars. And the, it raised by big companies with great balance sheets. And there's just a, or great sort of businesses, I should say. The balance sheets are getting sketchier.

• 6:10Speaker 00

  Money printing businesses. So they're real businesses.

• 6:14Speaker 01

  And, uh, and at some point, just like the, you run, you run out of people willing to give you money.

• 6:21Speaker 00

  Totally. I mean, we talked about this a week ago in Madison where we were discussing like the lending environment will tighten and hyperscalers are going to have to get creative here, which is what we're seeing.

• 6:30Speaker 01

  Good job by us. Yeah. Yeah. Good job by us. Like foreshadowing sort of this announcement. And so, but there, there's still lots of money out there and there is money that traditionally goes to large, long-running infrastructure projects because that money itself is a long-term liability. So sort of the classic example here is the pension fund. The pension fund is that, you know, people, you're paying into your pension over time. Your employer is paying into your pension over time. I actually know a surprising amount of, of the mechanics of this because for one person businesses, actually like pensions are the best possible retirement plan.

• 7:15Speaker 00

  Interesting. Okay.

• 7:16Speaker 01

  So the, um, because you could, you could contribute a much greater amount than a traditional retirement plan before taxes and shift sort of your tax liability window. All these things that go into it. It's actually called the doctor plan because doctors are the most frequent users a lot.

• 7:31Speaker 00

  Okay. Yeah.

• 7:31Speaker 01

  Right. Because what, what happens with the doctor is you're in school for a very long time. So you start making money relatively late, but once you make money, you usually make a, a fairly decent amount of money. And so it's like a catch-up plan where you can put way more money into retirement.

• 7:45Speaker 00

  All the money you weren't saving in your late twenties as you're toiling through That's right. school and residency.

• 7:49Speaker 01

  That's right. So, um, yeah, it's, it's kind of interesting because it's like a hangover from like old school pension plans that aren't really in favor anymore. Anyhow, the, the law, but those are, that's money that it has to be there in the long run, but it doesn't have to be paid out for quite a while. And so these are the sort of investments that want to go into like a toll road is the classic pension investment where you're putting a lot of money to work, but the predictability and understandability of the long-term payback is very clear. And it's going to pay back over a very long time. You're going to make a lot of money in the long run, but you have to have very patient capital because the, and like pensions in theory would have been a

• 8:33Speaker 01

  good match for say railroads, right? Because the, the problem with the railroad is you build it and you might not really get your money back for 30 years. But, and this is a beautiful symmetry because I think this Nvidia deal is symmetric with the Google equity issuance in which I wrote about Berkshire Hathaway and their shift from seized candies, a very high margin business, using that cash flow to get into BNSF railroads, which is a lower margin business, but the absolute cash that's thrown off is very high. Right. And, and the analogy there is, is, you know, to what extent is Google making, making the same shift? And I think that's a very pertinent point to this

• 9:15Speaker 01

  Nvidia thing, which we can sort of circle back around to. So you have this long patient capital that is a very good alignment for long-running investments. And so what you had in this post by Jensen Huang is trying to make the case that actually you're all thinking about AI wrong. It's not a short-term investment. It's actually a long-term investment. And the, and, and, you know, if you put Nvidia GPUs in, they run for a very long time and longer than you think. And we make them better with Cuda over time. And this is sort of building on the hyperscalers

• 9:56Speaker 01

  argument, which is, look, the data shells, like the, the actual buildings, those are 30-year investments and we're only buying GPUs right when we need them. So they're kind of aligned, but a little not aligned, uh, in that, in that regard. And so the case that's being made here is this is a long-run investment that deserves long-run capital. And if you zoom out, it's like, yeah, because all the short-run capital's been used up. And, and, and so that's sort of the case being made here. Now, is the case valid?

• 10:27Speaker 00

  Yeah.

• 10:27Speaker 01

  Is, uh, is.

• 10:28Speaker 00

  Well, and the lenders, their concern.

• 10:30Speaker 01

  Ben in Madison wants to email and say.

• 10:33Speaker 00

  Do we buy it?

• 10:34Speaker 01

  Hi guys. Is this case valid?

• 10:36Speaker 00

  Well, in terms of the invalidity or potential invalidity, one of the concerns is that the GPUs that any of these companies, any of these infrastructure companies are buying from Nvidia burn out before the patient capital can realize the upside.

• 10:51Speaker 01

  Or not just that, but Nvidia comes out with new GPUs that makes your old GPUs obsolete.

• 10:55Speaker 00

  Exactly. Right. And so Nvidia is trying to guard against that risk, correct? And try to allay some of those concerns.

• 11:03Speaker 01

  Well, Nvidia is trying to do a lot of things. Most importantly, preserve their competitive position and their margins. So it's kind of an interesting point, a big, a talking point that Jensen Huang raised, and that was repeated on the CoreWeave earnings call. And I don't think it was an accident that these happened back to back. It's like Jensen Huang comes out and makes this case, then Corey comes out in their earnings and says, "We have A100 chips that we are contracting out at a higher rate than before."

• 11:30Speaker 00

  And they're working great.

• 11:31Speaker 01

  Which I, well, which I think is, I think that's absolutely believable. I've like, it better be, they said it in their earnings, right?

• 11:39Speaker 00

  Yeah.

• 11:40Speaker 01

  It's a little more, and it makes sense. Like, like compute is in such demand, there's already installed compute, even if that compute is six years, I think the A100.

• 11:49Speaker 00

  Yeah, it's a previous generation, for anybody who's not clear, but still being utilized.

• 11:54Speaker 01

  So it's a little, okay. Right. So, so on the surface, it's a great case. It's like, look, people are out there saying GPUs only last two to three years. Actually, here's an example of a chip that is six years, signing contracts right now.

• 12:06Speaker 00

  Still turns up the demand.

• 12:07Speaker 01

  Those contracts are worth more than what the contracts were previously. They're actually increasing in value. And by the way, these are fully depreciated assets. They're like, all the cash they're earning is pure profit. This is a long-term asset. And on the surface, it's a pretty good argument. There's just a couple of problems.

• 12:26Speaker 00

  Okay.

• 12:27Speaker 01

  So, problem number one. A big shift that has happened in the last couple of generations has been a shift to water cooling. So you have, which requires entirely new kinds of data centers. You like, so you, you can't just take in like your GB200s, or the upcoming Vera Rubin, and slot it into the old data center.

• 12:50Speaker 00

  Mm-hmm.

• 12:51Speaker 01

  Because they actually need water cooling, and these requires entirely new ways of putting servers together. Like, Facebook had this entire, like, they love the whole open source or open like data center thing. They had this concept, they could manufacture data centers very rapidly, and it was like this two-story sort of thing. I think it was two-story or whatever. But it all, it all depended on passive cooling. So one question I have about the A100 case, and I think some H, I think the H generation might also be, be air-cooled, not water-cooled, or maybe it was half and half.

• 13:19Speaker 00

  Okay.

• 13:20Speaker 01

  The reason those are staying in place is because there's no replacement for them. You have data centers that are built for a particular assumption around cooling. New GPUs don't fit that assumption, so that data center is actually stranded.

• 13:36Speaker 00

  They're stuck with the A100s for life because of the way the data center was built.

• 13:41Speaker 01

  That's right. So I, so on one hand, in a compute scarce environment, absolutely they can keep selling them. But that is not, the A100 is not representative of what your expectation should be for GPUs going forward.

• 13:56Speaker 00

  And not necessarily dispositive as to the question in terms of whether this will still have utility in a market.

• 14:02Speaker 01

  That's right. It's not, this doesn't undo it. The fact of the matter is, A100s are being sold for more than they were before because compute is scarce. But that gets to the next question. The available compute today is a function of decisions that were made pre, like 2024 and before, right? Like, because it takes like two years to bring these online. And of course, back then, the market was, for the record, freaking out about capex. And everyone who spent money on capex were right. Actually, no, they were wrong. They were wrong because they didn't spend enough on capex. They should have, they should have spent more in 2024. But the, but so everyone has these signals. Everyone's

• 14:44Speaker 01

  talking about our demand exceeds our supply, but everyone's getting the same signal at the same time. So a reasonable concern from the market is, okay, if one company was getting this signal, then yes, they can invest appropriately. If 10 companies are getting this signal, and they invest, do we overshoot? This is how the boom-bust cycle happens. Is everyone's getting the same signal, that doesn't mean the signal is a 10x signal. It might be a 5x signal, but 10 companies invest, so you end up with double the capacity that you need.

• 15:20Speaker 00

  Right.

• 15:21Speaker 01

  So that's like, so that's one of the, another concern, where the supply-demand environment right now is not necessarily representative of the supply-demand environment in two years, or three years, or in 30 years, however long you want these long-running assets to sort of be, be, be considered on.

• 15:35Speaker 00

  And that scenario would involve several companies bowing out of some of these infrastructure build-outs and the race to the frontier. Is that right?

• 15:44Speaker 01

  Well, what it would, what would entail is, your A100s are not going to be getting contracts if there's a gazillion GB200s available.

• 15:53Speaker 00

  Mm, okay.

• 15:54Speaker 01

  Right? They are, they are available as a function of there not being compute. If there is an abundant amount of compute, the old compute's going to get retired very quickly.

• 16:03Speaker 00

  Yeah.

• 16:04Speaker 01

  So, so, which again, I'm not saying the argument being put forward is wrong. There's a lot of weight being hung on these A100 contracts that I'm just saying are not necessarily going to be long-term representative.

• 16:19Speaker 00

  Hmm.

• 16:20Speaker 01

  Now, the pushback is, we are so short on compute, we've barely scratched the surface of what these things can do. Actually, it's not just that in two years, we're not going to have a surplus, we're still going to be in shortage. And by the way, that might be true. Like, like the extent to which the possibilities are barely being tapped as far as AI, particularly once we get to purely autonomous sort of functionality, like where you don't need to have the human in the loop. Like, there is, the bull case is not insane. And it's not like a railroad, this is the distinction from the article, where the railroad article, like, there's just no way to accelerate the revenue generation potential of a railroad.

• 17:02Speaker 00

  Yeah.

• 17:03Speaker 01

  Like, you gotta actually.

• 17:04Speaker 00

  It's closer to a toll road.

• 17:05Speaker 01

  That's right. You have to actually build it across a brutal terrain, which takes a very long time. Then you actually have to develop the land that you got for it. The land has to like build up like productive functions such that it starts using, like, it's just, in the physical world, things are slow.

• 17:23Speaker 00

  Yeah.

• 17:24Speaker 01

  That's right. And even then, the number of, say you instantly had total saturation all over the railroad, you can only run so many trains. You have to build trains. Like, so it's not, whereas AI, the scalability capability, if this stuff starts working, you have all the benefits of any digital good, right? What's the idea of software? You write software once, it's instantly, infinitely duplicatable, and it can be used sort of everywhere. And there's aspects of that to AI, particularly when you think about the concept of AI improving itself, AI writing its own programs, AI being like set loose on a company and creating agents on its own that figure out all the functions of it.

• 18:07Speaker 01

  Like, again, none of that stuff quite works now, but it's working pretty well, and it's accelerating unbelievably rapidly. I think we have an emailer in here saying that I'm a Luddite because I took too long to like vibe code, which I'll push back on in a little bit. But it speaks to the point that I'm sorry, like, my six months was too slow for you. And it's kind of like a valid point, right?

• 18:27Speaker 00

  Yeah.

• 18:28Speaker 01

  So like, like, the, the speed with which this is moving is a very real thing. But it's not a slam dunk case at all. And also, there's a real tension in bringing more supply to market will depress prices.

• 18:43Speaker 00

  Hmm.

• 18:44Speaker 01

  Now, you can argue the demand's so high that the prices will still go up because demand will accelerate more than supply.

• 18:51Speaker 00

  Yep.

• 18:52Speaker 01

  But they're not going to go up as much as if you did bring more supply to market. And this is sort of in, it's just a math function. Like, the, the price depends on how much supply you have. It also depends how much demand you have. The bet is that demand is going to not just increase faster than supply, but increase even more such that prices, it doesn't matter how much Nvidia produces, price is going to go up. And maybe that will be the case. But it's not, but the, the other question is just this timing question. This gets back to the, the, the amount of capital in the market. In the long run, you can't be funding stuff with debt forever. At some point, you need to actually make money, and that money gets cycled back into buying new stuff.

• 19:37Speaker 00

  Yeah.

• 19:38Speaker 01

  And that, I'm sure, is going to happen. But like we talk about with stock picking, it's not enough to be right, it's about timing.

• 19:46Speaker 00

  Yeah.

• 19:47Speaker 01

  And the big question with this, these capital issues is, I believe this stuff will pay for itself. The question is, will it pay for itself in time to avoid like an air pocket where we run out of money?

• 20:00Speaker 00

  A whole bunch of bag holders. Sure.

• 20:02Speaker 01

  That's right.

• 20:03Speaker 00

  Well, and one other question before we move on. There's an element of this that read to me, reading your article, as sort of a defensive move from Nvidia, as Google brings all this infrastructure online, and you've got two dominant AI players, and as everybody becomes more cost-sensitive, there's going to be an increasingly urgent push to get on TPUs as opposed to Nvidia chips. So Nvidia wants to facilitate building out with Nvidia hardware and Nvidia software. Does that make sense? Did I read that correctly?

• 20:39Speaker 01

  Yeah. So that, that gets the micro question, like the Nvidia-specific question. And this is a question, by the way, we've been talking about for, for a few years now. I think it was GTC 2024, so it was about 15 months after ChatGPT had come out, when Nvidia is truly like a stock of flame.

• 20:59Speaker 00

  A stride of the world. Yep.

• 21:01Speaker 01

  That was the GTC where Jensen Huang was at the San Jose Convention or like Conventions or like Arena, like the hockey arena.

• 21:07Speaker 00

  Yep.

• 21:08Speaker 01

  And it's like a rock star thing, right?

• 21:10Speaker 00

  I think he may have also signed someone's boobs at that GTC.

• 21:14Speaker 01

  That that was act, no, that I think that was in Taiwan that that happened.

• 21:17Speaker 00

  Okay.

• 21:18Speaker 01

  But I might be, I might be wrong.

• 21:20Speaker 00

  Either way, same era Nvidia.

• 21:22Speaker 01

  Yes.

• 21:22Speaker 00

  Just owning the universe at that point.

• 21:24Speaker 01

  And I remember that was kind of a boring keynote in a way that Nvidia's GTCT notes were not boring, but because before ChatGPT, they knew they had this incredible computing capability, this sort of like this, you know, highly parallel, what are the things you can do for it? CUDA lets you program it more easily. And I, I wrote an update years ago where someone's like, how can Nvidia announce all this stuff? Why can these keynotes be so cool? Especially because they do a key, like Nvidia loves doing keynotes. They do keynotes like every six months or actually less if you include CES and things like that. They do Jensen's up on stage like every three to four months.

• 22:01Speaker 00

  That's true.

• 22:01Speaker 01

  How does he talk about so many new things? And the reason is that it's all the same thing. Everything is just parallel computing on using CUDA. And they're just making all these libraries where they're just changing a few things, but they're all the same thing. And so, but the, and the reason they were doing that is they're trying to, they're throwing everything against the wall. At what's every possible application of parallel computing? Let's make a library for it and see if we can find and get the next, a market spinning around this beyond gaming and beyond like Bitcoin money.

• 22:33Speaker 00

  Funny you say that because there was before ChatGPT launched. I remember a GTC that you covered. I don't know whether I was working with Sir Tekkery at that point, but it just seemed like Jensen was throwing all kinds of crazy ideas at the wall to see what sticks. And it was like, cool. It was like imagining the future. It's great that he's got all these ideas. I don't know how much any of this will actually be real, but he's clearly thinking about where we're going to be and how we're going to be computing 10 years from now. And then ChatGPT blows up maybe nine months later. And it's like, oh, okay. So this is it. And Nvidia is in the catbird seat.

• 23:13Speaker 01

  So, but the weird thing about large language models is they were obviously incredible for Nvidia. That's why their stock went to the moon. They have been on and off the most valuable company in the world. It was also very bad for Nvidia. And the reason it was bad for Nvidia is that the, the play with CUDA is to build a developer ecosystem on top of CUDA.

• 23:38Speaker 00

  Mm hmm.

• 23:38Speaker 01

  But CUDA only works on Nvidia GPUs. So you get CUDA for free. It's easier to use and it's a tremendous investment. Like Nvidia almost went under trying to build CUDA at a time when no one understood what they were doing or why they're wasting money on it. And that's why like Jensen Huang will get bristly, particularly when people like question, like they're like rent seeking or profit, whoever it's like, no, they earned their spot. Fair, absolutely. Like, like it, it shouldn't be forgotten. Like they, they have earned every dollar they've gotten through 25 years of taking massive risks.

• 24:15Speaker 00

  And the stock bottomed out several times along the way.

• 24:19Speaker 01

  It bottomed out in October 2022.

• 24:21Speaker 00

  Right.

• 24:21Speaker 01

  I wrote an article like three weeks before ChatGPT came out, like tracing their bottoming out history and their, their search for what was next.

• 24:30Speaker 00

  Nvidia in the valley. I remember it well.

• 24:33Speaker 01

  Nvidia in the valley. So there is a, um, so, but so go back to this GTC. So I wrote an article at the time called Nvidia Waves and Moats. And what was interesting about that GTC was number one, it was very boring. Like all the cool stuff kind of got scrubbed out. Now Jensen Huang has brought that stuff back. So the last few GTCs, like he's more talking about other things. Now it comes across as, oh, you're, you're still looking for something beyond the LM. Cause the problem with the LM is it shifts the developer platform far above where Nvidia sits. All the activity is happening on top of LLMs. You're like, it, it's not, and so no one who's writing an AI application today is using CUDA.

• 25:18Speaker 00

  Yeah.

• 25:19Speaker 01

  Now, some people are, if you're like training your own model and, and, and you're doing some like low level things or non, non LLM things, but the vast majority of the energy and all the money and the ecosystem is far removed from CUDA. They have no idea and don't need to know or care what chips their application are running on. They're just on the OpenAI API or they're on the Anthropic API.

• 25:44Speaker 00

  Yeah.

• 25:44Speaker 01

  Or they're on like using Bedrock and Amazon and it's sitting on Trainium and they're using a Chinese open source model. It's totally abstracted away. And this is why LLMs were bad for Nvidia. Now again, all the money they made along the way is worth it, but they had, their moat has been tremendously diminished.

• 26:06Speaker 00

  Hmm.

• 26:07Speaker 01

  CUDA is still a moat if you need to do stuff that requires CUDA.

• 26:10Speaker 00

  Right.

• 26:11Speaker 01

  But the vast majority of stuff and energy doesn't require CUDA.

• 26:14Speaker 00

  All right. And that is the end of the free preview. If you'd like to hear more from Ben and I, there are links to subscribe in the show notes, or you can also go to sharp tech.fm. Either option will get you access to a personalized feed that has all the shows we do every week. Plus lots more great content from Sir Tekkery and the Sir Tekkery Plus bundle. Check it out. And if you've got feedback, please email us at email at sharp tech.fm.