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Everything Is Fake: Elevating Human Expertise Against AI Workslop in Modern Business
In today's digitally saturated landscape, synthetic content has outpaced human-generated material, raising a critical challenge: how can organizations ensure authenticity and maintain trust when nearly everything consumers and employees interact with may be AI-generated? Recent studies project that by the end of this year, up to 90% of online content may be synthetic. For business leaders, the implications are immediate. This article synthesizes key actionable insights from the "Everyday AI" podcast episode to address the specific business risks and define a practical roadmap for leveraging AI while foregrounding human expertise.
AI Trust Crisis: Data-Backed Impact on Consumer Confidence
A Salesforce "State of the AI Connected Customer" survey revealed that 72% of consumers trust companies less than they did a year ago, correlating directly with the increase of AI-generated material across digital touchpoints. Europol projects the majority of online content will soon be synthetic, obliterating traditional markers of trust and authenticity. Companies face a tangible risk: generic, AI-driven output—referred to in the episode as "work slop"—is silently eroding both consumer trust and revenue, often before any dashboard or KPI reveals the damage.
AI Workslop: Identifying the Business Cost of Generic Automation
AI-generated workslop describes outputs that are technically competent but lack domain expertise and authenticity. The podcast identifies three primary causes:
Education: Only 6% of organizations qualify as AI high performers, according to McKinsey. Most companies deploy AI without foundational knowledge—such as context engineering principles or basic model parameters (top k, top p, temperature).
Cost: AI delivers passable work in seconds at a fraction of human labor costs. This economic incentive leads organizations to accept "good enough" as "good," something rarely tolerated before AI became mainstream.
Accuracy: Day-to-day business deliverables often exist in a gray area, where the difference between competent and excellent can only be discerned by domain specialists.
These factors explain why social media, pitch decks, customer emails, and even resumes have become graveyards of AI workslop, contributing directly to declining trust scores cited in research.
Fraud and Deepfakes: Operational Risks for Business Leaders
AI-enabled fraud is now a top concern, flagged by 72% of business leaders in Experian’s Fraud Forecast. The technical barrier is gone: cloning voices and generating realistic deepfake videos require minimal skill and can be executed at scale in minutes. The risk extends throughout operational processes—vendor proposals, hiring, internal communications, and sales collateral can no longer be verified on sight alone. As detection tools lag behind AI generation, authentic company expertise risks being dismissed or ignored.
Another overlooked issue: the "liar's dividend." Genuine expertise gets lumped with AI-generated spam, exposing companies to false negative assessments and lost opportunities when authenticity cannot be proven beyond doubt.
Elevating Human Expertise: The Practical Cure for AI Workslop
The episode’s central insight is clear: organizations must strategically inject domain expertise into every AI workflow—not as a passive reviewer, but as an active driver. Smith OS reports found that AI content overseen by qualified experts performed four times better than fully automated outputs, directly impacting proposal acceptance rates, client conversion, and engagement metrics.
Generic prompts produce generic outputs. Proprietary reasoning and company-specific decision logic—when properly documented and embedded—transform AI systems from liability to differentiator. For maximum business value, the chain of thought within large language models must be audited and iterated by true subject matter experts, not technical staff or generalized AI champions.
Expert-Driven AI Loops: Operationalizing Authenticity for Business Value
"Human in the loop" is identified as a critical flaw: placing any non-specialist in an AI system adds little value and reinforces workslop. What’s needed are expert-driven loops where domain specialists proactively shape context, review outputs, and maintain professional standards in AI-powered workflows.
Companies should:
Audit all customer-facing content for genericness or unverifiability.
Categorize and flag outputs for expert review.
Capture nuanced, expert-driven reasoning and language for integration into AI.
Design workflows ensuring routine, proactive involvement of domain leaders.
Internal benchmarking and monthly/bimonthly scoping, not annual reviews, help prevent the proliferation of workslop and keep outputs tightly aligned with real business standards.
AI Training Data Quality: The Hidden Threat to Future Business Outputs
With over 90% of online content soon to be synthetic, AI models are increasingly trained on workslop-heavy data, eroding baseline quality over time. The relative quality of publicly available content—especially blog posts and other training materials—has noticeably declined, perpetuating the cycle of bland, competence-without-expertise outputs.
The upcoming challenge: distinguishing "good enough" from "genuinely valuable." Only niche domain experts can reliably draw this line, further emphasizing their critical role in business AI adoption.
Action Roadmap: Building Trust and ROI in AI-Centric Organizations
To address the everything-is-fake dilemma while maximizing AI’s productivity advantage, companies must:
Audit all outputs for genericness and unverifiability.
Systematically identify domain experts for each business area.
Capture authentic reasoning and language from those experts for routine integration.
Build AI workflows that require regular input and oversight from subject matter specialists.
This approach not only lowers the risk of workslop, fraud, and misattributed expertise but also restores consumer trust and improves business outcomes—demonstrably, as noted by performance metrics cited throughout the episode. Context engineering reframed as a trust strategy delivers productivity while maintaining authenticity, making it a non-negotiable requirement for organizations aiming to thrive in the AI era.
Conclusion: Specific Steps to Combat the "Everything Is Fake" Crisis
By moving from human-in-the-loop to expert-driven loops, companies can proactively address the synthetic content surge and maintain genuine industry leadership. This strategy is not only a productivity improvement but a trust imperative—ensuring the brand’s distinctive expertise is never lost to AI workslop or deepfake confusion.
For those seeking a more detailed blueprint and practical tools, explore dedicated communities and resources focused on advanced AI adoption and authentic context engineering.
Topics Covered in This Episode:
- AI Trust Crisis and Consumer Skepticism
- Deepfakes, Fraud, and AI-generated Content
- Workslop: Rise of Generic AI Outputs
- Human Expertise vs. Fully Automated AI
- AI Content Detection and Liar’s Dividend
- Elevating Human Oversight in AI Workflows
- Expert-Driven Loops vs. Human-in-the-Loop
- Auditing Business Outputs for AI Workslop
- Domain Expertise in AI Context Engineering
- Roadmap to Fight AI Workslop with Humans
Episode Transcript
Jordan Wilson [00:00:16]:
Everything is fake. The text you read on that landing page, that amazing photo you saw on Instagram, the viral video on Twitter, and all of those comments. That customer service rep you talked to last week that was surprisingly chipper and actually helpful. There's a good chance all of it was fake or at least AI. And I think we're about to cross over into a very dangerous place where the majority of our day to day interactions will be engaging with some type of media or medium that's either partially AI generated or completely AI. Yes. There's the fast emerging threat of deep fakes and fraud that's not gonna go away. And while I think there's a whole other episode to be done on the impacts of the everything is fake disease that has on us as individuals, there's probably a more important ship that you have to write right now.
Jordan Wilson [00:01:10]:
That's how your company actually uses AI but remains human. See, I'm someone that encourages AI use, like, all day every day, but I know most people and most companies don't take the proper care in elevating human expertise while using AI. And that's led to this onslaught of work slop in 2026 or the never ending barrage of generic sounding, uninspiring outputs that your company may be rubber stamping. So how can you fight back against the year of the fake? How can your company resonate in a sea full of mundane work slop? And how can you make sure the expertise of your brightest people doesn't get drowned out by identifying all of your business processes? Well, we're gonna talk about that on today's episode of Everyday AI in our start here series. Everything is fake and how your company can leverage human expertise and fight the AI work slot. So here's the big picture. Right now, there is an AI trust crisis. It's already here.
Jordan Wilson [00:02:12]:
So a recent study from Europol, or sorry, Europol projects that up to 90% of online content may be synthetically generated by the end of this year, and that means that trust is essentially gone. A new Salesforce state of the AI connected customer survey said that 72% of consumers trust companies less than they did a year ago. So if almost everything that we see or read online is gonna be AI and people don't trust companies, What's the answer? Well, it's elevating your human expertise while still using AI, and that's what we're gonna talk about on today's show. So if you stick around for the next twenty or so minutes, here's what you're gonna learn. You're gonna learn why defaulting to everything is AI generated is now the sound professional discipline and not cynicism. You're gonna learn how AI works workslop is silently destroying trust and revenue before any dashboard shows it, and you're gonna learn what the most AI native companies do to stay human, authentic, and competitive at scale. Alright. Let's get into it.
Jordan Wilson [00:03:24]:
Welcome to everyday AI and our start here series. This, if you're new, well, it's the essential podcast series to both learn the basics of AI, but if you are a regular to double down your knowledge. So, after doing 700 plus episodes of Everyday AI, I didn't have a good answer when people always said like, hey, Jordan. You have a lot of information out there. Where do I start? Well, you start here with the start here series. I think it's best if you listen to all of these in order. They're shorter episodes, usually about twenty five to thirty minutes. But if you wanna catch up on all of them in order, I suggest you go to starthereseries.com.
Jordan Wilson [00:04:01]:
That is going to give you free access to our inner circle community and in our start here series, space. You can go and listen to all of the episodes in order, read about them. There's a playlist that we keep updated there and everything else. So if you missed our last episode, we went over the AI labor shift, when it will happen, and what it means for jobs. So let's talk about why everything is fake and why I think it's just actually one of the biggest problems that most people don't know that we're fighting against. So again, when I tell you this, I'm gonna try to say this is some, you know, oh, like, bragging about something. In many instances, because I am drowning in AI every day, I feel that I pick up on certain trends and topics maybe a little bit for the before the average, you know, business user. And what I've seen over the last six months is this onslaught of work slot.
Jordan Wilson [00:04:58]:
Right? And I don't know if the average everyday business consumer has realized this. Maybe you have. If you spend any time on, you know, LinkedIn, Twitter, you've probably already seen this. So I think it's already impacted the written word, but I think it's gonna impact everything. Right? So as vibe coding, becomes very commonplace, it's gonna impact, you know, all of the apps we use. There's gonna be app slop. There's gonna be video slop. Right? But, essentially, I think the smart business consumer needs to realize that this is just the future, and there are things that we can do to fight against it.
Jordan Wilson [00:05:36]:
So let's start here. Right now, no one really knows when or if something is AI generated, which is maybe a good thing. Right? If you're thinking, oh, well, I can use AI at scale. Yes. You can. So when something is done correctly, it is very hard, to tell the difference between, you know, an expert who is using AI correctly at scale versus a team of maybe 20 humans who aren't using AI. Right? As a former, you you know, journalist, I was a a a a Pulitzer, fellow. Right? I won the ACP story of the year.
Jordan Wilson [00:06:14]:
So I was a pretty decent journalist way back in the day. Pretty good at writing. Right? I I I think writing, we've already far surpassed what the average large language model can do, but there's a big gap there. Because when I say that, a lot of people won't believe me. They're gonna be like, no. What comes out of, you know, chat g p t or Gemini or Claude or, you know, Copilot, whatever is is kinda garbage. No. It's it's not.
Jordan Wilson [00:06:35]:
It's better than what award winning writers like myself can do if you know what you're doing. But that's a big if, and that is elevating the human expertise. Right? Because if someone goes in and doesn't put a lot of care, they don't understand the basics of context engineering or understand how large language models work, they're not gonna be able to produce something that's economically valuable. They're gonna produce work slop. So that is the, generic output that you get from, you know, just trying to either take the shortest way out or try to get the quickest output. But right now, humans can't tell the difference between AI content that's produced at by an expert and actual, you know, experts, created good human only AI content. In a study from Boringa last year show that people claiming to confidently be able to spot AI images only scored about 30% accuracy. So, yeah, even when people are like, oh, I'm sure that this is or is not an AI image.
Jordan Wilson [00:07:33]:
No. Also self reported confidence in AI detection is rising every year while actual accuracy keeps just, declining. And this is not something that your team can just solve with better instincts or better tools. So what do I mean by this? The quality of what AI can produce. I keep I've I've been saying this over and over the last couple of months. The quality and the the the just scale that we've seen in the last three months has far surpassed what we got the three years prior. And that's what I think has gotten us to this crisis of, well, everything's fake because you can't tell. Right? If someone knows what they're doing, in, you know, Nano Banana Pro or, you know, c dance for video generation, VO for, you right? All of these platforms.
Jordan Wilson [00:08:23]:
If you're using the best platform for image editing, video editing, text generation, web, right, for software engineering, agentic coding. The output ultimately, if you have an expert driving it, is pretty much indistinguishable, which is crazy to say that even video. Right? We never thought. We thought, you know, back when we in the early Dolly one, Dolly two days, you would have said, oh, you you know, AI video is twenty years away. Right? But here we are, you know, five years after that point in time, and, well, no. It's already, you know, gotten to the point of human level again if you have an expert human driving it. So when everything is fake, you can't look over fraud. That's only one concern, but I think you have to understand this.
Jordan Wilson [00:09:10]:
So I am speaking to this from a couple of different ways. For consumers, I think today's show may be helpful that you need to change your mindset and assume literally everything is fake. Everything is AI. Right? I've gotten a lot more comments recently, right, that, you you know, this podcast or myself, AI, no. I've literally had a cold for off and on for, like, three months. Yeah. Right? But I've gotten all these comments recently. It's like, oh, no.
Jordan Wilson [00:09:39]:
This is this is no. It's, I don't think AI can, quite ramble on like I do, not yet or, yet simulate my stuffy nose or horsey throat. Right? So no. If if you're, listening on the podcast, this is not AI. I'm a real human doing this live and unedited, unscripted. My voice has just been gone off and on for three months. But I think it's a good thing for consumers to have. Right? When you were having that conversation with the someone over the phone.
Jordan Wilson [00:10:09]:
Right? When you're watching something or, you know, probably the biggest thing that no one's talking about, just the right agentic AI, right, with all these, you you know, the open claw variants out there. Probably anything you read online, even discourse. Right? People debating things on, you know, Twitter, LinkedIn, Reddit, Quora. It's probably mostly going to be bot driven, I would say, within a couple of months, and people aren't going to understand that. So, yes, I think this is important for consumers to understand that pretty much anything you interact with will probably be fake. But I think it's also as business leaders, I think it's important to understand. Right? That that email you got from a client, is it real or is it not? Right? You you know, pretty pretty soon. Right? I'm I'm getting this all the time.
Jordan Wilson [00:10:55]:
I'm getting emails from AI agents. Sometimes they disclose it, sometimes they don't. I've gotten video pitches from, you know, clearly things that are AI, and people think that I maybe I can't tell the difference. Right? So, yes, this does lead to problems on the consumer side and on the business decision maker side, but also fraud. Right? Company wide, I think you have to be, paying attention to this. So, in the Experian future of fraud forecast that came out this year, seventy two percent of business leaders identified AI enabled fraud as a top operational challenge. I think maybe two years ago, I think most people understood that AI was was a huge threat. Right? Because what you could do with with deepfakes, things like that, voice cloning, obviously, a huge concern.
Jordan Wilson [00:11:44]:
But now the barrier to do this, the technical barrier and the technical know how is essentially zero. You know, anyone with that can read within 30 minutes could probably figure out how to do this at scale. Voice cloning, you know, AI videos that look very real. It's it's very easy. Right? These fraud tools are now free. They don't require any technical skill, then they allow complete autonomy, and and and and an enemy as well. So anything that you is important for your day to day business operations. So vendor proposals, you know, job candidates, executive communications, they can no longer just be verified by site alone.
Jordan Wilson [00:12:27]:
I think it's important that we come to that realization that, you know, these these routines that we've gone through throughout the years. Oh, checking email. Right? Hiring. Oh, yes. Looking at these resumes. Right? Probably, we've already seen this over the past two years. Now you assume every single resume is written by, you know, Chad GPT or Claude or Gemini or something. Right? But the same thing with videos, as well.
Jordan Wilson [00:12:49]:
Audio, you know, interviews, similar. There's a flip side to that, to this deep fake issue. Right? And this is, again, maybe good conversation for another, episode where we can go deeper on this. But even proof of things that actually happen, I think that's gonna be hard to prove. Right? That's called the liar's dividend. That's when real data and real expertise gets lumped in with AI spam. Right? Kind of how, you know, people have been saying, oh, Jordan, you're, you know, because my voice has been going in and out, and my audio is a little weird because of that. People are saying, oh, no.
Jordan Wilson [00:13:25]:
You're AI. Right? No. That's the liar's dividend. But I think it's important to realize and understand as well. And and because of AI content detection, right, which is not real. Right? There's obviously things that are a little bit better and a little more reliable, you know, on the video, in the image side, you know, audio as well in terms of invisible, watermarks in some type of media. But in written media, it's there's no such thing. Right? So when you have AI content detection, you know, coming out and say, hey.
Jordan Wilson [00:13:53]:
You know, your company's proposal, you know, couldn't be accepted for this RFP because it was AI generated. Right? Well, no. You know, and that's a problem because I think that your company's real expertise can actually be dismissed as AI generated, just because there's zero recourse or proof. And that's led to this trust crisis that if you don't know it's happening right now, it is. Right? So like I said, that Salesforce study that said that 72% of people trust companies less this year than they did last year, that is a drastic drop. Right. And that has led to, I think, or is the result of maybe work slop. Right.
Jordan Wilson [00:14:41]:
I think consumers are trusting companies less. Number one, if you are putting out low effort content at scale, which I think so many, so many people are, and that has led to, Workslot. But number two, if if if you're not putting a a human face, if you're not putting imperfections out in the world. That's one of the reasons all along I've wanted to right? Before I even started everyday AI more than three years ago, I said, I'm not gonna edit this thing. I said, I'm gonna I'm gonna I'm gonna go on. I'm gonna do this thing live, because I knew at the time, I'm like, in a couple of years, you know, this is all gonna be AI generated. So I wanna get my imperfections, my my stuttering, my sniffing, all of it. Like, that's that's human.
Jordan Wilson [00:15:30]:
That's authenticity. And I think that's one of the reasons why, you know, this this podcast has, you know, kind of grown in popularity over the years be because it's real. It's authentic. And I think that brands need to understand that too because of the trust crisis. Right? So even good example. Three major dictionaries named AI Slop, their word of the year in 2025. Right? And Work Slop, I think, is going to, trend in that direction. If you haven't heard of Workslot before, right, that's just AI output that's technically competent, but it carries, like, nodes or domain expertise.
Jordan Wilson [00:16:02]:
It just sounds like bland. Right? And and social media is already a graveyard of enterprise Workslot. Most companies don't even realize that they are actually contributing to this. And here's three reasons. I think that companies are still falling into the work slop trap. And again, we're gonna get to how you can overcome this, with human expertise. But the three reasons are education, cost, and accuracy. Alright.
Jordan Wilson [00:16:31]:
So education. So, recent McKinsey study said that only 6% of companies qualify, as AI high performers generating meaningful business impact. What that is is everyone's using AI, but how many people out there have actually learned, have actually taken courses, are actually taught. Right? We use large language models every day. Does anyone know top k? Right? Top p temperature. Right? Not that those things are super important, but you should at least know what those things are, right, for the things that we're using every single day. This is the equivalent. This is the, like, twenty ten equivalent of, you know, sitting in front of a computer as most knowledge workers do in 2010 and not knowing what an email was or not knowing, I don't know what a URL was.
Jordan Wilson [00:17:26]:
It's like, no. You know what a URL is. That's the thing you put in the bar. You type it and you click the button. Right? It's it's it's how it operates and how it works. Anyways, education is the first reason. Number two, cost. The economics of AI are irresistible and undeniable.
Jordan Wilson [00:17:42]:
AI generates, like, quality usually or passable outputs in seconds at a fraction of the human cost. And then accuracy, I think, is the third reason that we have so much work slop because so much of the day to day deliverables fall into a gray area. Right? I think good enough passes for good. Alright. Let me say that again. And how many times, if you're being honest with yourself, if you've just blindly used AI output and you've looked at something, how many times have you said good enough? Right. Whereas before AI, when, when, when you were doing it manually, I don't think good enough would ever pass for good. Now it does in an age of AI.
Jordan Wilson [00:18:26]:
Right. Where you're like, oh, that's good enough. So we've talked about so far why, work slop is on the rise. The impacts, maybe personally on consumers, fraud, deep fake, but here's the cure. It's elevating human expertise inside of your AI setup. You need strategic oversight from experts. So Smith OS, reports found that AI content with human strategic oversight performs more than four times better than fully automated outputs out of AI. Right? This is what makes decision makers click yes or no on that proposal.
Jordan Wilson [00:19:18]:
Four times higher, a four times better result when you properly put in human expertise, when you properly elevate those domain experts at the right place at the right time in your AI workflows, which unfortunately isn't being done. Right? Because I think when AI made content creation nearly free, I think don't that instantly elevated domain knowledge to be the competitive moat overnight. And I'm talking about basic text to text, large language models all the way through fully autonomous, you know, multi agent orchestration, everything it's elevating the right domain expert at the right time in the process. Because the winners right now, those that are getting the most out of it are pouring so much human expertise into their AI that the outputs are unmistakably theirs alone. Right? And if you listen in our start here series, we talked about a little bit this in context, in the context engineering episode. But I think it's worth maybe repeating one or two things from that. It's all about making sure that you have the expert lined up with whoever's building whatever AI flows. Here's what I mean by that.
Jordan Wilson [00:20:52]:
I think so many times in so many organizations that I talk to, I say, okay, you have this great, you know, scheduled AI flow. You know, it's great. It's hands off. Cool. Great. Who set it up? It's always usually someone technical. Right? Usually, maybe companies have an AI champion or two. Maybe it's someone in IT.
Jordan Wilson [00:21:13]:
I think I see it more on the IT side for, you you know, Windows Copilot enterprise organizations. But, usually, it's someone who's technical or an AI champion, but who's actually benefiting from it? Who's going in and, you know, copying and pasting and updating it? It's usually not that person. Right? I would venture in larger organizations. I would say less than 10% of the output that ultimately gets used, in a deliverable, in an end artifact is coming from someone with domain expertise, with very little or zero input in the front end or the back end. And that's where it's the most important. It's usually a technical person who's setting it up or an AI champion or something, you know, oh, we found this on the Internet. This look good. Let's plug it in, and it's good enough.
Jordan Wilson [00:22:04]:
But the winners are the ones that are pouring more human expertise. Because like we talked about in our context engineering episode, generic prompts produce generic outputs in your proprietary data, your first person or first company reasoning decision logic that changes everything. And you need to start if you haven't already. You need to document why your company makes decisions, not just what decisions it makes. In the same way, I talk so much, and I know you guys are probably annoyed at me saying this. You need to spend more time looking at the chain of thought in a large language model, than you do on your front end context engineering or whatever you do on the back end, copying, pasting, producing a document. Right? You need to be able to iterate multiple times. That is how you properly insert your company's domain expertise.
Jordan Wilson [00:22:58]:
It's through the proper context engineering process. And if you took our, you know, prime prop polished PPP course, inside our inner circle community, you already know this. But that's where you insert your domain expertise is going through how a certain large language model, will tackle a certain issue, and you have to be using the right model, the right feature, the right mode, for the right problem. Right? And that might look a little different depending on what sector you're working in. But the chain of thought in sitting down, right, even if you're, you know, if you have a certain setup or, hey, someone from IT or, I don't know, someone from marketing has to set up a certain thing, well, they need to be sitting down with the actual domain expert looking at the chain of thought and saying, hey. According to our SOP, according to our skills that we set up. Right? This is what the input is on the context engineering side. This is what the output is.
Jordan Wilson [00:23:51]:
But more importantly, let's walk through the chain of thought, and let's see how the model tackled this problem. Right? And documenting the why, that is one of the most powerful things that companies that are winning right now, that's why. And that's just context engineering reframed as a trust strategy, not just for productivity improvement. Is that going to make your company more productive doing that? Absolutely. Right? But that is ultimately going to create more trust in the end. That is going to be that that process right there. Right? Proper prop proper context engineering using the right model, you you know, education, training, all that. But having your domain expert be involved in the auditing and the iteration, looking at the chain of thought, that what is going to ultimately lead to less work slap, more trust, and more ROI for your company.
Jordan Wilson [00:24:51]:
And this is again, why I will continue to call out how bad human in the loop is. Right? I've been saying this, sorry, way before everyone else about how human in the loop is bad. It's one of the worst things I think that has happened in or around the AI industry, aside from just an overall general lack of education, training, and, basic knowledge. Human in the loop is bad. Human in the loop leads to work slot. Human in the loop is going to, continue to contribute to the everything is fake dilemma, and it is going to be bad. We need, as again, if you are a long time listener on this podcast, you know, maybe it's the first time listening. Well, let me introduce this to you.
Jordan Wilson [00:25:39]:
Expert driven loops are what we need. Human in the loop means that you can just put any human passively into any AI system, and that's some sort of guardrail. That's garbage. That's failure. That is work slot. You need expert driven loops. That means the domain, expertise, the domain expert driving the loop. Right? And what does that mean? Usually, when I talk when anyone talks about a a loop, we're talking about an agentic or semi agentic or AI powered workflow.
Jordan Wilson [00:26:14]:
Something that happens in your company repeatedly, right, over and over again. That's that's a loop. You need an expert driving it proactively. Right? Because Gartner right now predicts that companies that replaced human agents with generic AI will be forced to rehire by 2028. And I think human in the loop is just a passive checkpoint, but expert driven loops mean experts are actively shaping the context and reviewing against professional standards. Alright. This is why I think it's so important, and we talked about this in previous episodes, to have internal company benchmarks and internal scoping that you are doing not yearly, not quarterly, but probably monthly or bimonthly. So work slop is only going to get worse and more prevalent.
Jordan Wilson [00:27:07]:
And I think this is important because we are gonna have AI slop heavy training data. This is something I think, people very much overlook. So, right, if we go back, to one of our initial stats from Europol that projected that up to 90% of online content may be synthetically generated by the end of this year. And I would say it's probably gonna be more than 90% if I'm being honest. But what does that create? I think that creates trust in AI that we probably shouldn't have, because even if you just looked at the relatively quality like, the relative quality, I don't know if there's a a a standard for this or a benchmark. If not, I hope someone can create it. Right? But if you looked at the relative quality of something that was produced on the Internet in '20, I don't know, we'll say pre, AI. Right? Because even before chat g b t, you know, AI slop was a huge problem.
Jordan Wilson [00:28:11]:
Right? The early GPT technologies that, predated, Chad GPT were, you know, already out there polluting the Internet with garbage, with AI work slot before anyone knew Chad GPT existed. But I think now, right, the average piece of, I don't know, we'll just say blog posts. Right? Because that's a lot of what goes into training data. The average quality of of blog posts, I would say, has gone down by two to three x very easily over the last ten years, and it's because of AI. Right? So that just obviously creates this regurgitated cycle of poor or poorer quality source materials. So, I do think that even more so in the future, yes, the training, at the companies is getting better. Right? The reinforcement learning with human feedback and other, scaling technologies at the big AI frontier labs helps with this, you know, helps to make sure that the training data is, higher quality and the training process is better. Yet still, it is hard for certain people to understand the differences.
Jordan Wilson [00:29:21]:
Right? It's a lot of time, takes a niche domain expert to be able to differentiate between something that's good enough versus something that is actually humanly good. Right? And I do think that this creates a future where not only WorkSlop is more prevalent, but the baseline of what you would get out of a large language model with decent context engineering is probably gonna be, well, a little sloppy, sloppier than it maybe was a year ago. So here's what you need to do. Here is the road map, alright, on how to get over the everything is fake dilemma and how to properly leverage your company's human expertise to fight AI work slot. You need to audit every customer facing or potential client facing output and flag anything that's generic or unverifiable. So that's anything that's currently in production, anything on your website, anything on your pitch decks, anything, you know, in your emails, anything. Right? And anything in draft version that maybe didn't make it to publication. Then you need to identify your strongest experts in those areas.
Jordan Wilson [00:30:29]:
You should first, you know, flag anything and then categorize all those things. Then you need to identify your strongest expert and capture how they would actually say this. Right? Let's say, I don't know, it's a it's a sales page for a certain product or service that's you just started selling, and it sounds kinda generic. It was like, well, this sounds like a lot of nothing. Right? Get your get your people in there and have them tear this apart. You know, they're probably looking at it and be like, look at this garbage the marketing department put out. Right? I can say that. I've I've been in marketing for a while.
Jordan Wilson [00:31:04]:
Right? They're they they maybe don't like it. Right? Or maybe it's a company that is producing this for you. Right? A lot of companies use third party agencies. You need to get you audit everything. You need to identify your strongest people and capture how they would actually, solve that or describe it. And then you need to build AI systems that bring those people in routinely. It's not a one time thing. It's not a, you you know, wash your hands once and you're done.
Jordan Wilson [00:31:32]:
You you need to make sure that you build in that domain expertise in your AI operations. That is an expert driven loop, not just having some random human say, yeah. This is good. That's work slop, and it's crushing your company. Alright. That's a wrap for this episode in the start here series. Everything is fake and how your company can leverage human expertise and fight AI works out. I hope this one was helpful.
Jordan Wilson [00:32:03]:
If so, let me know. Well, let me know by going to starthereseries.com. That is going to give you free access to our inner circle community. Yeah. You're not gonna literally find it anywhere else, FYI. Even if you try to find it, you're not gonna find it. That's gonna give you free access to our start here series. You can go read and listen to all of the episodes in order.
Jordan Wilson [00:32:25]:
We have a Spotify playlist, inside, the community as well. So that's it for today. I hope this is helpful, and thanks for tuning in. Hope to see you back tomorrow and every day for more everyday AI. Thanks, y'all.