Meta Is Firing Anyone Who Doesn't Use AI

0:00Eric Siu:Using only 20 % of your business data is like dating someone who only texts emojis. First of all, that's annoying. And second, you're missing a lot of context. But that's how most businesses operate today, using only 20 % of their data. Unless you have HubSpot, where all the emails, call logs, and chat messages turn into insights to grow your business. Because all that data makes all the difference. I would know because I use HubSpot at my company. Learn more at HubSpot.com. So Meta, they cut looks like 700 people this week, but they're planning another 15 ,000 or so. So Meta now requires 65 % of its engineers to write 75 % or more of their code using AI by mid-2026.

0:40Eric Siu:Their scalable machine learning org has a target of 50 to 80 % AI-assisted code. Across Messenger, WhatsApp, and Facebook, 55 % of all code changes must be AI-assisted. 80 % will be mid to senior. Engineers must adopt AI tools such as DevMate and Google Gemini. So every Meta employee now is graded on AI-driven impact and their performance review. It is now a core expectation. If you don't use AI, you don't advance. Meta is the first major tech company to formally tie promotions to AI adoption. I don't know about that, but I do know they're gonna use this as more of an excuse to cut. I would say out of any sector,

1:13Neil Patel:like using AI for marketing, you should, but it's not as black and white, at least from what I've found compared to like coding. For coding, product, design, I would say using AI, especially for coding, is much more black and white than how we would use AI as marketers. And if you look at most of these tech companies, their biggest expense isn't just payroll. It's specifically engineers, product people, and designers within their organization.

1:41Eric Siu:And what Neil's really referring to here when he says it's a little different, it is. And the simple way to look at this is coding is verifiable. It's just like math. You can verify it. Okay. Now, when it comes to my feeling, your feeling about this ad creative or whatever, not so much. Same thing with a copy is it's, it's more, it's not as fluid, which is like the copy you're seeing from AI right now, you need to hate the human in the loop to make it really good. And it needs to work back and forth. You can't just let it do its thing. It hasn't quite worked out that way yet. But I think one, we mentioned this a couple of months ago, the companies that are like blaming AI for cuts, it's the ones that have, you know, overhired during, you know, the, the, the pandemic era and also the, the low interest rates.

2:21Eric Siu:Right. And now that interest rates are higher you know yeah yeah so it affects stock prices too so well luckily pal says he

2:27Neil Patel:doesn't think that we need to raise rates he made that announcement i think a day ago or was a lot in the last week but going back to marketing not being as black and white with ai this morning i'm on a call and it was 8 a.m pst this guy from india is showing me how they're using ai and marketing another one of those okay specifically on social media some of the best content i've ever seen produced by AI for social posts. Not only did they get the content right, quality, they got the timing right. Because if you just crank out content, but you're cranking out content on something that's played out, it's not going to do well.

3:02Neil Patel:So they're looking at what's being talked about in the news world and marketing world and trying to figure out what should I hit on right now that'll appeal. Their engagement is roughly seven to eight times higher than their normal posts. And we went back quite a bit and they've been doing this for over a month. So they go, this is great. Now they're trying to make the case how this is amazing. So the first thing I do is I say, how much revenue were you generating before all of this and how much revenue you're generating right now? They didn't have the answer. So I said, all right, let's get back on a call with an hour.

3:36Neil Patel:So they're like, okay, we'll be able to get the data by then or rough directional. So, you know, They asked their data science person. Their data science person says, well, we kind of traffic it. It's hard to track everything, but it drives less than 1 % of our revenue before we started using AI. And I'm like, okay, now you're getting more engagement. What's the change now? And their sales cycle's not that long because their product sells for well under$100. I think it starts at like$19 or something like that per month. So it's a quick sales cycle at that stuff and they have a free trial. And now they're just like, cool.

4:09Neil Patel:How much revenue are you generating now? Like, well, we haven't really seen an uptick, but we think the branding is going to be great for us. I'm like, but what's the revenue? They're like, our revenue is pretty much roughly flat. And how long has it been out for? How long have they been, the product out, or how long have they been doing this experiment for?

4:25Eric Siu:Both.

4:26Neil Patel:The company, I don't know how old it is. I know it's well more than five years old. So I think it's less than 10 years, but I know it's more than five years old. For this experiment, they've been running it for roughly 30 days. Okay, yeah. So they need a little more time, I think, too. They need a little bit more time, but I actually don't think they need more time to figure out if it's working or not. I think they need more time to adjust their approach. Because if they tell me a visitor comes to the website and people typically buy that day because it's a free trial, no credit card. And then if you want to end up purchasing, then it's, I believe it's$19 a month USD.

5:01Neil Patel:Their sales cycle is really short. It's self-service. So time isn't the issue from a tracking perspective. I think time's the issue from they need to continue the experiment and try to figure out how to drive conversions. But this is a prime example when a marketer is using AI and they're looking at the top of funnel numbers of engagement and be like, look at the increased engagement. Seven, 8x, this is amazing. We're doing such a great job. But they're not really looking at the full funnel and understanding that, yes, you're doing more. And by the way, not only are they getting seven to eight times more engagement per post, they're cranking out three to four times the amount of volume per day as well.

5:39Neil Patel:So they increase the quantity and they're getting more engagement. So total views are drastically higher, but they're not seeing a revenue increase because the issue with a lot of the content, even though it's related to marketing, like let's say if you talk about chat GPT ads or, you know, GEO, or I think that one of the articles was related to chat GPT ads because they just made an announcement that they had a hundred million dollars. it's cool and all, but if it doesn't drive revenue, who cares? And I think that's a big part markers get wrong with AI. They look at only top of funnel metrics, and I'm not saying you should ignore them, but you need to look at the full stack.

6:14Eric Siu:Yeah, and by the way, let's, so I actually, I had a YouTube video that went out talking about how my costs with Anthropoc tokens are skyrocketing, right? And people couldn't fathom why am I spending so much? Well, to Neil's point, if you're leading a company, even if you're a marketer of a company, you should be able to figure out how much return on investment you're driving with your AI effort. So I'll kind of explain my return on investment just from my, even with my bad short-term memory, I'll tell you right now. So$7 ,200 in spend, okay, in the last 31 days or so, we recouped 500 grand already in costs that I got to cut with my org just by making a little CFO thing and going back and forth with it and just being very precise on what I needed to do.

6:52Eric Siu:And then it also providing benchmark numbers. So that's one. Okay. So that's one. The second one is when we got the single brain website up without meaning to wait another month, the moment I got that website up in less than 24 hours, three leads from it. Right. And I'm just, I'm just, this is my short-term memory right now. I'm just remembering what we've done recently from it too. I went through our, I was looking at through ClickFlow, which is our, our AEO software, SEO software. Right. And we had massive drop-off from our signup page for whatever reason. It, it pulled in our mix panel, our Stripe data.

7:22Eric Siu:I looked at everything and it pulled benchmark data. And it's like, Hey, you should fix this. I'm like, Hey, actually QA, our product go through the entire flow itself and tell me what you think is wrong and where to drop off is. Right. I took that and I sent it over to the engineer. He thought some of the data was wrong. He came up with his ideas, but then from there, we actually got the fix done. You know what I mean? But these are things that actually drive more higher conversion rates and more monthly recurring revenue that I can point to. I like this, all this, but by the way, I like making all this content, these dot charts and things like that makes me feel good, right?

7:52Eric Siu:But it's mostly theater if you can't prove the revenue side.

7:54Neil Patel:I totally agree with you. One use case we've been loving using AI for in marketing is for conversion rate optimization, but not the standard way, like a marketer saying, hey, go look at my pricing page and tell me what changes to make our product page. We've been using AI to look at the numbers because we're global and most companies get transactions from different cities or countries. For this example, let's use countries. And credit card processors work differently in every single country. Like Adyen supposedly is better at taking your money in Europe than for corporations and certain other payment processors.

8:33Neil Patel:Or for example, in Brazil, PayPal doesn't work as well as, I think they have this payment system called Beletos. So we had AI analyze which countries have abnormally high drop-off rates from credit card, pricing page to credit card, credit card to checkout. And then we broke down the steps to see which areas are they dropping off. One could be pricing because you're not pricing in local currencies. So then we started looking at that. But AI helped us analyze and elicit in order every single country from the credit card page, like someone putting in a credit card, clicking submit and it not going through to that had the worst conversion rates.

9:13Neil Patel:I think Columbia was number one for us. I'm actually 99 % sure Columbia was worse for us. And then it recommended, here are the payment processors that work best for these regions. And I'm like, cool, run an experiment where we test out the payment processors for every single one of these countries with its recommended solutions to maximize their conversion rate.

9:31Eric Siu:So are people doing the experiments for you? Or are the agents signing up for all the things for you?

9:36Neil Patel:The credit card processing, we have humans sign up for that. That is really hard to do with agents because it requires a lot of personal and financial details and banking details that we personally don't feel comfortable to give to agents. But the agents are analyzing which ones have the biggest drop-off and then providing solution options. And then we go and call and we negotiate the rates because you can get better rates than whatever the agents tell you or AI is telling you. And then we go and test it out.

10:05Eric Siu:And that's massive, by the way. Like that's the type of stuff that generates, when you're talking about at scale, we're talking about millions and millions of dollars.

10:13Neil Patel:Correct. Percentage-wise, we ran the numbers. if it was half of our best country. So if our best country is at 5%, I'm making up a number, and we can get every country to operate at least at 2.5%, we would make, call it an extra 4-ish percent in revenue, which doesn't seem like a lot, but 4 % is a lot of money, and most of it drops to the bottom line. And then you do a little 4 % here, 2 % there, and then 3%. Yeah, next thing you know,

10:39Eric Siu:you've heard of how to grow your business by 10 plus percent. Yeah, and this is not even CRO now. it's just business optimization that's what it is yeah but i look at this as cro as well because you're optimizing rates right at each conversion rate right yeah i'm optimizing conversion rate

10:53Neil Patel:but yeah some of it is finance some of it is cro but the reason i consider it marketing is just like how you give credit card options on a checkout you have apple pay google pay which we've tested out for b2b apple pay and google pay does not boost conversions as much as people think for B2C, adding Apple Pay and Google Pay, massive.

11:14Eric Siu:So look, this stuff is all very exciting and we like to talk about new tools and all that. And maybe we will talk about some of these tools, but I think when push comes to shove, if you're working on a company right now, just keep in mind when you're going to your leader or even your boss or the CEO of the company, you have to talk about what revenue impact you're driving with this thing. That's what we're getting at.

11:34Neil Patel:All right, so I wanted to take a moment to tell you about my podcast co-host, Neil's agency called NP Digital.

11:40Eric Siu:And they work with a whole host of global companies or a global organization. Also, Neil has SEO tools such as Ubersuggest and Answer to Public. All you have to do is go to npdigital.com to learn more. And we'll see on the other side. So Stripe has now made it they've provisioned agents to be able to give people access to services directly from the command line interface. So for example, now if you're in a CLI, like using a terminal or something, you can say, hey, Stripe, I want you to add post hogs, right? You can basically, you can launch anything that you want, okay? So if you want to, here's how I'll break it down.

12:16Eric Siu:The Stripe co-founder said this. Vibe coding menu gen was exhilarating and fun escapade as a local demo, but a bit of a painful slog as he played real app. Building a modern app is a bit like assembling Ikea furniture or future. These are all services, docs, API keys, configurations, dev and production deployments, team and security features, rate limits and pricing tiers. So what Stripe is doing now is when it comes to issues, they or these apps, they'll help you provision all of these. So you can do it very quickly. And guess who takes the, who's the toe booth here? Stripe. So Stripe will be in the middle.

12:48Eric Siu:They'll provision all these things for you and they'll take a piece of the, every transaction. It's a great model, but that's like, they're so fast to get ahead of this. Like they're like, oh yeah, we're going to be out there with working with Meta and I think Shopify as well. So you can buy directly from ads. so they're going to be the toe booth there, right? Yes. Here, we're going to be the toe booth here. Like, oh, you guys need your agent's provision. Don't worry, we'll help your agents find this stuff.

13:09Neil Patel:You still need payment processing. I don't see that going away anytime soon. You know what domain I bought this morning, Neil? API for SEO. You know how there's data for SEO? I was like, oh, API for SEO.

13:18Eric Siu:I was like on my Peloton bike. And I told my agent to buy it. I was like, hey, you have the credit card. Go buy API for SEO. It's like, I got it. And then once I was done writing, I checked it. It's like, I got it.

13:28Neil Patel:That's much better than getting MCPs for SEO.

13:31Eric Siu:Yeah. Yeah, I think MCPs will have their uses, but I think they're like a bit overhyped. I think APIs are where it's at because these agents need to find these things. I don't know if you saw this, Neil. I don't know if I put this in here, but if you look at Supabase, they've grown from like 1 million accounts, like 4.5 million. A lot of that's agents, right? And I think there's another one that just grew very quickly. It's because of agent discovery. So I'm telling you guys, we've been talking about this on the podcast. We're going to be marketing to agents.

13:57Neil Patel:Yeah. And I don't think it's hard for at least SEOs to adapt because you've been marketing to robots for a very long time.

14:03Eric Siu:Like the Google bot, you know, I don't even know what the Bing bot is called.

14:07Neil Patel:But we've been dealing with algorithms all the time from Instagram changes out their algorithm or Facebook or Reddit, although Reddit is more black and white, but we've been having to deal with this for such a long time. I think SEOs are some of the most best suited people to optimize for bots. Cause in this new world, you got to just keep playing the game of pleasing them. Or they complain.

14:30Eric Siu:Yes. They're either the most adaptable, either the most adaptable dynamic people that I know, or they are the biggest whiners. I actually don't think they're separate.

14:40Neil Patel:I think majority of SEOs are the biggest adapters and they're great at adapting, but at the same time, a good subset of the ones, even though they're great at adapting, love whining.