Social media’s unregulated evolution over the past decade holds a lot of lessons that apply directly to AI companies and technologies.

Oh, how the mighty have fallen. A decade ago, social media was celebrated for sparking democratic uprisings in the Arab world and beyond. Now front pages are splashed with stories of social platforms’ role in misinformation, business conspiracymalfeasance, and risks to mental health. In a 2022 survey, Americans blamed social media for the coarsening of our political discourse, the spread of misinformation, and the increase in partisan polarization.

Today, tech’s darling is artificial intelligence. Like social media, it has the potential to change the world in many ways, some favourable to democracy. But at the same time, it has the potential to do incredible damage to society.

There is a lot we can learn about social media’s unregulated evolution over the past decade that directly applies to AI companies and technologies. These lessons can help us avoid making the same mistakes with AI that we did with social media.

In particular, five fundamental attributes of social media have harmed society. AI also has those attributes. Note that they are not intrinsically evil. They are all double-edged swords, with the potential to do either good or ill. The danger comes from who wields the sword, and in what direction it is swung. This has been true for social media, and it will similarly hold true for AI. In both cases, the solution lies in limits on the technology’s use.

#1: Advertising

The role advertising plays in the internet arose more by accident than anything else. When commercialization first came to the internet, there was no easy way for users to make micropayments to do things like viewing a web page. Moreover, users were accustomed to free access and wouldn’t accept subscription models for services. Advertising was the obvious business model, if never the best one. And it’s the model that social media also relies on, which leads it to prioritize engagement over anything else.

Both Google and Facebook believe that AI will help them keep their stranglehold on an 11-figure online ad market (yep, 11 figures), and the tech giants that are traditionally less dependent on advertising, like Microsoft and Amazon, believe that AI will help them seize a bigger piece of that market.

Big Tech needs something to persuade advertisers to keep spending on their platforms. Despite bombastic claims about the effectiveness of targeted marketing, researchers have long struggled to demonstrate where and when online ads really have an impact. When major brands like Uber and Procter & Gamble recently slashed their digital ad spending by the hundreds of millions, they proclaimed that it made no dent at all in their sales.

AI-powered ads, industry leaders say, will be much better. Google assures you that AI can tweak your ad copy in response to what users search for, and that its AI algorithms will configure your campaigns to maximize success. Amazon wants you to use its image generation AI to make your toaster product pages look cooler. And IBM is confident its Watson AI will make your ads better.

These techniques border on the manipulative, but the biggest risk to users comes from advertising within AI chatbots. Just as Google and Meta embed ads in your search results and feeds, AI companies will be pressured to embed ads in conversations. And because those conversations will be relational and human-like, they could be more damaging. While many of us have gotten pretty good at scrolling past the ads in Amazon and Google results pages, it will be much harder to determine whether an AI chatbot is mentioning a product because it’s a good answer to your question or because the AI developer got a kickback from the manufacturer.

#2: Surveillance

Social media’s reliance on advertising as the primary way to monetize websites led to personalization, which led to ever-increasing surveillance. To convince advertisers that social platforms can tweak ads to be maximally appealing to individual people, the platforms must demonstrate that they can collect as much information about those people as possible.

It’s hard to exaggerate how much spying is going on. A recent analysis by Consumer Reports about Facebook—just Facebook—showed that every user has more than 2,200 different companies spying on their web activities on its behalf.

AI-powered platforms that are supported by advertisers will face all the same perverse and powerful market incentives that social platforms do. It’s easy to imagine that a chatbot operator could charge a premium if it were able to claim that its chatbot could target users on the basis of their location, preference data, or past chat history and persuade them to buy products.

The possibility of manipulation is only going to get greater as we rely on AI for personal services. One of the promises of generative AI is the prospect of creating a personal digital assistant advanced enough to act as your advocate with others and as a butler to you. This requires more intimacy than you have with your search engine, email provider, cloud storage system, or phone. You’re going to want it with you constantly, and to most effectively work on your behalf, it will need to know everything about you. It will act as a friend, and you are likely to treat it as such, mistakenly trusting its discretion.

Even if you choose not to willingly acquaint an AI assistant with your lifestyle and preferences, AI technology may make it easier for companies to learn about you. Early demonstrations illustrate how chatbots can be used to surreptitiously extract personal data by asking you mundane questions. And with chatbots increasingly being integrated with everything from customer service systems to basic search interfaces on websites, exposure to this kind of inferential data harvesting may become unavoidable.

#3: Virality

Social media allows any user to express any idea with the potential for instantaneous global reach. A great public speaker standing on a soapbox can spread ideas to maybe a few hundred people on a good night. A kid with the right amount of snark on Facebook can reach a few hundred million people within a few minutes.

A decade ago, technologists hoped this sort of virality would bring people together and guarantee access to suppressed truths. But as a structural matter, it is in a social network’s interest to show you the things you are most likely to click on and share, and the things that will keep you on the platform.

As it happens, this often means outrageous, lurid, and triggering content. Researchers have found that content expressing maximal animosity toward political opponents gets the most engagement on Facebook and Twitter. And this incentive for outrage drives and rewards misinformation.

As Jonathan Swift once wrote, “Falsehood flies, and the Truth comes limping after it.” Academics seem to have proved this in the case of social media; people are more likely to share false information—perhaps because it seems more novel and surprising. And unfortunately, this kind of viral misinformation has been pervasive.

AI has the potential to supercharge the problem because it makes content production and propagation easier, faster, and more automatic. Generative AI tools can fabricate unending numbers of falsehoods about any individual or theme, some of which go viral. And those lies could be propelled by social accounts controlled by AI bots, which can share and launder the original misinformation at any scale.

Remarkably powerful AI text generators and autonomous agents are already starting to make their presence felt in social media. In July, researchers at Indiana University revealed a botnet of more than 1,100 Twitter accounts that appeared to be operated using ChatGPT.

AI will help reinforce viral content that emerges from social media. It will be able to create websites and web content, user reviews, and smartphone apps. It will be able to simulate thousands, or even millions, of fake personas to give the mistaken impression that an idea, or a political position, or use of a product, is more common than it really is. What we might perceive to be vibrant political debate could be bots talking to bots. And these capabilities won’t be available just to those with money and power; the AI tools necessary for all of this will be easily available to us all.

#4: Lock-in

Social media companies spend a lot of effort making it hard for you to leave their platforms. It’s not just that you’ll miss out on conversations with your friends. They make it hard for you to take your saved data—connections, posts, photos—and port it to another platform. Every moment you invest in sharing a memory, reaching out to an acquaintance, or curating your follows on a social platform adds a brick to the wall you’d have to climb over to go to another platform.

This concept of lock-in isn’t unique to social media. Microsoft cultivated proprietary document formats for years to keep you using its flagship Office product. Your music service or e-book reader makes it hard for you to take the content you purchased to a rival service or reader. And if you switch from an iPhone to an Android device, your friends might mock you for sending text messages in green bubbles. But social media takes this to a new level. No matter how bad it is, it’s very hard to leave Facebook if all your friends are there. Coordinating everyone to leave for a new platform is impossibly hard, so no one does.

Similarly, companies creating AI-powered personal digital assistants will make it hard for users to transfer that personalization to another AI. If AI personal assistants succeed in becoming massively useful time-savers, it will be because they know the ins and outs of your life as well as a good human assistant; would you want to give that up to make a fresh start on another company’s service? In extreme examples, some people have formed close, perhaps even familial, bonds with AI chatbots. If you think of your AI as a friend or therapist, that can be a powerful form of lock-in.

Lock-in is an important concern because it results in products and services that are less responsive to customer demand. The harder it is for you to switch to a competitor, the more poorly a company can treat you. Absent any way to force interoperability, AI companies have less incentive to innovate in features or compete on price, and fewer qualms about engaging in surveillance or other bad behaviours.

#5: Monopolization

Social platforms often start off as great products, truly useful and revelatory for their consumers, before they eventually start monetizing and exploiting those users for the benefit of their business customers. Then the platforms claw back the value for themselves, turning their products into truly miserable experiences for everyone. This is a cycle that Cory Doctorow has powerfully written about and traced through the history of Facebook, Twitter, and more recently TikTok.

The reason for these outcomes is structural. The network effects of tech platforms push a few firms to become dominant, and lock-in ensures their continued dominance. The incentives in the tech sector are so spectacularly, blindingly powerful that they have enabled six megacorporation’s (Amazon, Apple, Google, Facebook parent Meta, Microsoft, and Nvidia) to command a trillion dollars each of market value—or more. These firms use their wealth to block any meaningful legislation that would curtail their power. And they sometimes collude with each other to grow yet fatter.

This cycle is clearly starting to repeat itself in AI. Look no further than the industry poster child OpenAI, whose leading offering, ChatGPT, continues to set marks for uptake and usage. Within a year of the product’s launch, OpenAI’s valuation had skyrocketed to about $90 billion.

OpenAI once seemed like an “open” alternative to the megacorps—a common carrier for AI services with a socially oriented nonprofit mission. But the Sam Altman firing-and-rehiring debacle at the end of 2023, and Microsoft’s central role in restoring Altman to the CEO seat, simply illustrated how venture funding from the familiar ranks of the tech elite pervades and controls corporate AI. In January 2024, OpenAI took a big step toward monetization of this user base by introducing its GPT Store, wherein one OpenAI customer can charge another for the use of its custom versions of OpenAI software; OpenAI, of course, collects revenue from both parties. This sets in motion the very cycle Doctorow warns about.

In the middle of this spiral of exploitation, little or no regard is paid to externalities visited upon the greater public—people who aren’t even using the platforms. Even after society has wrestled with their ill effects for years, the monopolistic social networks have virtually no incentive to control their products’ environmental impact, tendency to spread misinformation, or pernicious effects on mental health. And the government has applied virtually no regulation toward those ends.

Likewise, few or no guardrails are in place to limit the potential negative impact of AI. Facial recognition software that amounts to racial profiling, simulated public opinions supercharged by chatbots, fake videos in political ads—all of it persists in a legal grey area. Even clear violators of campaign advertising law might, some think, be let off the hook if they simply do it with AI.

Mitigating the risks

The risks that AI poses to society are strikingly familiar, but there is one big difference: it’s not too late. This time, we know it’s all coming. Fresh off our experience with the harms wrought by social media, we have all the warning we should need to avoid the same mistakes.

The biggest mistake we made with social media was leaving it as an unregulated space. Even now—after all the studies and revelations of social media’s negative effects on kids and mental health, after Cambridge Analytica, after the exposure of Russian intervention in our politics, after everything else—social media in the US remains largely an unregulated “weapon of mass destruction.” Congress will take millions of dollars in contributions from Big Tech, and legislators will even invest millions of their own dollars with those firms, but passing laws that limit or penalize their behaviour seems to be a bridge too far.

We can’t afford to do the same thing with AI, because the stakes are even higher. The harm social media can do stems from how it affects our communication. AI will affect us in the same ways and many more besides. If Big Tech’s trajectory is any signal, AI tools will increasingly be involved in how we learn and how we express our thoughts. But these tools will also influence how we schedule our daily activities, how we design products, how we write laws, and even how we diagnose diseases. The expansive role of these technologies in our daily lives gives for-profit corporations opportunities to exert control over more aspects of society, and that exposes us to the risks arising from their incentives and decisions.

The good news is that we have a whole category of tools to modulate the risk that corporate actions pose for our lives, starting with regulation. Regulations can come in the form of restrictions on activity, such as limitations on what kinds of businesses and products are allowed to incorporate AI tools. They can come in the form of transparency rules, requiring disclosure of what data sets are used to train AI models or what new preproduction-phase models are being trained. And they can come in the form of oversight and accountability requirements, allowing for civil penalties in cases where companies disregard the rules.

The single biggest point of leverage governments have when it comes to tech companies is antitrust law. Despite what many lobbyists want you to think, one of the primary roles of regulation is to preserve competition—not to make life harder for businesses. It is not inevitable for OpenAI to become another Meta, an 800-pound gorilla whose user base and reach are several times those of its competitors. In addition to strengthening and enforcing antitrust law, we can introduce regulation that supports competition-enabling standards specific to the technology sector, such as data portability and device interoperability. This is another core strategy for resisting monopoly and corporate control.

Additionally, governments can enforce existing regulations on advertising. Just as the US regulates what media can and cannot host advertisements for sensitive products like cigarettes, and just as many other jurisdictions exercise strict control over the time and manner of politically sensitive advertising, so too could the US limit the engagement between AI providers and advertisers.

Lastly, we should recognize that developing and providing AI tools does not have to be the sovereign domain of corporations. We, the people and our government, can do this too. The proliferation of open-source AI development in 2023, successful to an extent that startled corporate players, is proof of this. And we can go further, calling on our government to build public-option AI tools developed with political oversight and accountability under our democratic system, where the dictatorship of the profit motive does not apply.

Which of these solutions is most practical, most important, or most urgently needed is up for debate. We should have a vibrant societal dialogue about whether and how to use each of these tools. There are lots of paths to a good outcome.

The problem is that this isn’t happening now, particularly in the US. And with a looming presidential election, conflict spreading alarmingly across Asia and Europe, and a global climate crisis, it’s easy to imagine that we won’t get our arms around AI any faster than we have (not) with social media. But it’s not too late. These are still the early years for practical consumer AI applications. We must and can do better.



Nathan E. Sanders is a data scientist and an affiliate with the Berkman Klein Center at Harvard University. Bruce Schneier is a security technologist and a fellow and lecturer at the Harvard Kennedy School.

Sourced from MIT Technology Review



& archive page

By Webb Wright 

If you’re considering launching a new AI-centered brand or product, you may want to go beyond simply adding ‘AI’ to the end of the name.

The AI Gold Rush is in full swing and brands of all stripes are rushing to establish their particular niches in this hugely profitable and increasingly crowded industry. New AI-centered brands, departments and products are cropping up by the day, each requiring a name that is, ideally, both memorable and unique.

“Every single company, whether a candy bar manufacturer or a software company, seemingly has to show that it is doing something to leverage AI,” says Jonathan Bell, founder and CEO of Want Branding. “And that often requires some kind of adjacent brand, which, of course, then needs a name.”

Several brands, as you may have noticed, have simply taken to adding ‘AI’ (or ‘.AI’) to the ends of their names. Think Stability AI, Spot AI, Mistral AI, Shield AI, People.ai, Otter.ai, Arize AI, Crowd AI, Toggle AI and so on. And, of course, there’s OpenAI, the company that has become something of a flagship for the entire wave of AI innovation that’s currently underway following its hugely successful launch of ChatGPT in late 2022 and that has probably helped to establish the ‘AI‘ suffix as the name du jour for up-and-coming brands looking to make a name for themselves in the industry.

Adding ‘AI‘ to the end of a brand or product name “is an easy but often perhaps a cheap way of doing it without much thought,” says Bell.

A parallel can be drawn between this naming phenomenon and a similar one that followed in the wake of the dawn of the internet in the late 90s when scores of new brands with ‘.com‘ at the ends of their names began to emerge. In those early days of the world wide web, it made practical sense for companies to make unambiguously clear that they were technologically savvy enough to have an online presence. (Remember, this was back when ‘online‘ was itself a new, hip word.)

Over the slow process of many years, however, the internet became so deeply embedded into most of our day-to-day lives, into the very fabric of popular culture and commerce, that it became more or less superfluous to add ‘.com‘ to the end of a brand name. Most people these days automatically assume that any given brand – unless it‘s incompetent beyond belief or run by a group of Luddites – has a website and probably some degree of social media presence.

The ‘.com‘ naming trend, in other words, began as a worthwhile marketing tactic, but “at a certain point that was eroded and it became meaningless,” says David Placek, founder and CEO of Lexicon Branding. There are still, of course, some brands (Hotels.com, for example) that have chosen to use their domain names as their official names, but such a strategy is far less common today than it was when the internet had the shiny-new-toy factor.

AI could follow a similar trajectory of cultural adoption as that of the internet: today, it’s all anyone can talk about; tomorrow, it’s basically taken for granted. Just as people today assume that brands today have an online presence – even when they don’t have ‘.com‘ in their names – we could soon reach a point as a society in which AI is so ubiquitous, so deeply integrated into our devices and our modes of working and communicating with one another, that adding ‘AI‘ to a brand or product name becomes passé. Placek says he’s “absolutely positive” that we’ll cross that threshold sometime within the next two years, after which point “everybody will assume that there’s something AI-related” built into most brands and products.

Given that forecast, adding ‘AI‘ to the end of a name “can be a disservice for building brand strength over time, because [the market] becomes crowded,” says marketing agency Tenet Partners CEO Hampton Bridwell. “There are a lot of names with a similar sound or styling and that creates a situation where you don’t have differentiation or memorability within the name.”

Anthropomorphic names and the sad tale of Clippy

There have, of course, been other naming trends that have recently emerged around AI. For example, many AI-centered products have been given human-sounding names, apparently in an effort to make the underlying technology – which could potentially come across as a bit threatening to a culture that’s been weaned on films like 2001: A Space Odyssey and The Matrix – feel a bit less alien and intimidating.

Consider IBM’s Watson, an AI model originally designed to answer questions that gained global fame when it won Jeopardy! in 2011. There are also more recent examples, including Siri (Apple), Alexa (Amazon) and Einstein (Salesforce).

As the journalist Charles Duhigg points out in a recent article in The New Yorker, Microsoft (which became a leader in the burgeoning AI industry following its recent multi-billion-dollar investments in OpenAI) has had to learn the hard way about the risks involved with trying to anthropomorphize AI. In 1996, the company introduced Clippy, a smiling virtual assistant with big eyes and a paperclip for a body, who could answer simple user questions on Microsoft Office platforms. The character became widely loathed by users. The Smithsonian called Clippy “one of the worst software design blunders in the annals of computing,” as Duhigg quotes in his article. Microsoft killed Clippy off in 2001.

The company once again tried its hand at anthropomorphizing algorithms in 2016 with the launch of Tay, an AI-powered chatbot whose conversational style reflected that of a typical teenage girl. Tay rather quickly descended into a fit of hate speech and was deactivated less than 24 hours after its launch.

Apparently wiser after the Clippy and Tay debacles, Microsoft is now naming its AI products in a manner that suggests utility and even a touch of fallibility. Copilot, the name of the company’s recently launched suite of AI-powered productivity tools, insinuates something that can be reasonably relied upon to provide a measure of assistance, not something into which one should invest one’s whole trust.

The curious case of ChatGPT

Perhaps the biggest irony in the realm of AI names is the fact that ChatGPT, the product that, more than any other, catalyzed the burgeoning AI Revolution, has such a widely disliked name.

For one thing, says Bridwell, the word ‘chat‘ in a brand name “is pretty limiting – it really doesn’t embody what the whole thing is about in terms of [how] it delivers value. It’s a terrible name. Over time, [OpenAI] should really think about rebranding it.”

Even OpenAI CEO Sam Altman agrees that it’s not an ideal name. During a recent podcast hosted by comedian Trevor Noah, Altman said that ChatGPT is “a horrible name, but it may be too ubiquitous to ever change.”

ChatGPT’s suboptimal name could stem in part from the fact that the OpenAI team that built it did not initially have high hopes for its prospects as an uber popular app. It was referred to internally as a “low-key research preview” in the period leading up to its launch and it was intended as a means through which the public could begin to interact with OpenAI’s GPT large language model more broadly so that the company could then collect feedback and fine-tune the technology accordingly.

Many within the OpenAI team were surprised when ChatGPT attracted its first million users in just five days, becoming the fastest-growing app in history.

Advice for marketers

According to Want Branding’s Jonathan Bell, brands that are looking to promote their use of AI through an optimized name should take their time. “It needs to be well thought-out,” he says. “It shouldn’t be something that’s done casually over a quick meeting, where you just simply add ‘AI’ to [the name]. Companies need to think about: What are they specifically doing? Can they deploy AI in a way that is really effective, or is this something that’s been done that could come across as bandwagon-jumping?”

Placek, who’s prone to referencing cognitive science and linguistics when discussing the psychology of brand- and product-naming, highlights the importance of sound symbolism – that is, the associations between particular sounds and the concepts that they evoke in the mind of the hearer. “You don’t want something too soft and you don’t want something too clever,” he says. “[You want something that’s] a little bit on the more serious side that [suggests] intelligence … sound symbolism should play a role in selecting and developing your names.”

When prompted to describe the qualities of a great name for an AI brand or product in fewer than 10 words, ChatGPT wrote: “Memorable, clear, unique, relevant, easy to pronounce, globally appealing, scalable.”

Feature Image Credit: Adobe Stock

By Webb Wright 

Sourced from The Drum

By Alon Goren

At this point, most enterprises are dabbling in generative AI or planning to leverage the technology soon.

According to an October 2023 Gartner, Inc. survey, 45% of organizations are currently piloting generative AI, while 10% have deployed it in full production. Companies are eager to move from pilot to production and start seeing some real business results.

However, enterprises getting started with generative AI often run into a common stumbling block right out of the gate: They suffer analysis paralysis before they can even begin using the technology. There are tons of generative AI tools available today, both broad and highly specialized. Moreover, these tools can be leveraged for all sorts of professions and business purposes—sales, product development, finance, etc.

With so many choices and possibilities, enterprises often get stuck in the planning phase—debating where they should deploy generative AI first. Every business unit (and all of the business’s key stakeholders) wants to own a part of the company’s generative AI initiatives.

Things can get messy. To stay on track, businesses should follow these guidelines when experimenting with generative AI.

Focus On Specific Use Cases With Measurable Goals

Enterprises need to recognize that every part of the organization can benefit from generative AI—eventually. To get there, however, they need to get off the ground with a pilot project.

How do you decide where to get started? Keep it simple and identify a small, specific problem that exists today that can be improved with generative AI. Be practical. Choose an issue that’s been challenging the business for a while, has been difficult to fix in the past and will make a visibly positive impact once resolved. Next, enterprises need to agree upon metrics and goals. The problem can’t be too nebulous or vague; the impact of AI (success or failure) has to be easily measurable.

With that in mind, the pilot project should have a contained scope. The purpose is to demonstrate the real-world value of the technology, build support for it across the organization and then broaden adoption from there.

If organizations try to leverage AI in too many different ways and solve multiple problems, it’ll cause the scope to grow out of control and make it impossible to complete the pilot within a reasonable timeframe. Ambition has to be balanced with practicality. Launching a massive pilot project that requires extensive resources and long timelines is a recipe for failure.

What’s a good timeline for the pilot? It depends on the circumstances, of course. Generally speaking, however, it should only take a few weeks or a couple of months to execute, not multiple quarters or an entire year.

Start small, get something functional quickly and then iterate on it. This iterative approach allows for continuous learning and improvement, which is essential given the nascent state of generative AI technology.

Organizations must also be sure to keep humans in the loop from the very beginning of the experimentation phase. The rise of AI doesn’t render human expertise obsolete; it amplifies it. As productivity and business benefits increase with generative AI, human employees become even more valuable as supervisors and validators of AI output. This is essential for maintaining control and building trust in AI. In addition, the pool of early participants will also help champion the technology throughout the organization once the enterprise is ready to deploy it widely.

Finally, once the project has begun, organizations have to stick with it until it’s complete. Don’t waste time starting over or shifting to other use cases prematurely. Just get going and stay the course. After that’s been completed successfully, companies can expand their use of generative AI more broadly across the organization.

Choosing The Right Technology

The other major component of the experimentation phase is selecting the right vendor. With the generative AI market booming, it can seem impossible to tell the differences between one solution and another. Lots of noisy marketing only makes things more confusing.

The best way to cut through the noise is to identify the requirements that are most important to the organization (e.g., data security, governance, scalability, compatibility with existing infrastructure) and look for the vendor that best meets those needs.

It’s extremely important to understand where vendors stand on each of these things early on to avoid the headache of discovering that they don’t really check those boxes later. The only way to do that is by talking to the vendor (especially its sales engineering team) and seeing these capabilities demoed first-hand.

Get Ahead Of The Competition With A Strong Start

Within the next couple of years, I expect almost every enterprise will employ generative AI in production. Those wielding it effectively will get a leg up on their competition, while those struggling will be at risk of falling behind. Though the road may be uncharted, enterprises can succeed by focusing on contained, valuable projects, leveraging human expertise and selecting strategic technology partners.

Don’t wait. Embrace this unique opportunity to innovate and take that crucial first step now.

Feature Image Credit: GETTY

By Alon Goren

Follow me on LinkedIn. Check out my website.

CEO and Cofounder of AnswerRocket. Read Alon Goren’s full executive profile here.

Sourced from Forbes

By David Gewirtz,

Here’s how to pivot your career into AI and how your existing skills can help open doors in the booming AI field.

Since I’ve been covering the new boom in AI, I’ve been getting reader letters asking how to grow into that industry. This letter from Rick is representative of many of them:

I just read your articles pertaining to free AI courses at IBM, OpenAI, and Deep Learning and wanted to see if you could offer some advice.

I’m trying to transition from my industry of life science to big tech. I want to continue to learn more about AI and its applications, with the focus on becoming a product manager who can showcase knowledge and use cases for it.

Do you have any suggestions for an experienced product manager, with very little machine learning experience, starting out on what to learn in the AI/ML space to become marketable? I’m going to start by taking the free courses from IBM as you mentioned. I would love to work with engineering and development teams on crafting products utilizing these technologies specifically.

What stands out about Rick’s letter is that he’s experienced as a product manager, but his field is life sciences rather than traditional tech. This experience is important, because he does have skills that can transfer into other fields.

Also: Have 10 hours? IBM will train you in AI fundamentals – for free

I also receive letters from readers who don’t mention experience or pre-existing skills, but just see that prompt engineers are raking in the big bucks and want to be part of the windfall. I mention this, because a lot of less experienced folks see stories about app developers making millions or prompt engineers making six-figure incomes and think that just one course, or just wanting it hard enough, will get them the gig.

Back when I taught entry-level programming, about half my students wanted to program. The other half wanted programming jobs because they paid well. Unfortunately, that second set of students weren’t all that willing to apply themselves to the craft. They just thought that the mere fact that they took a course in programming would get them a job. And it might have. But without demonstrable skills, that job wouldn’t have lasted more than a few weeks.

My point here is that you have to be willing to do the work, and you also have to be able to bring something to the job. Rick seems willing to do the work, and he has skills he can bring to the job. Below are the five steps I’d recommend Rick — and anyone interested in pivoting to AI work — take.

1. Identify your current skills

This is important if you want to switch careers. What skills do you already have?

As a product manager, Rick undoubtedly has some people-wrangling skills. Product managers have often been described as CEOs without the authority or the pay. That’s because they need to manage and cajole people from multiple disciplines and departments.

He probably has some serious writing skills. Writing a product requirements spec is not a trivial task.

Also: Is prompt engineer displacing data scientist as the ‘sexiest job of the 21st century’?

Depending on what kind of product manager he is, he might also have marketing communications skills. By this, I mean the ability to write promotional copy describing his products for prospects, not just the implementation teams.

As an experienced product manager, he probably also has strong project management skills, strong organization skills, and some level of product knowledge (in his case, for life science-related offerings).

2. Identify skills that might transfer

Rick might not be aware of this, but he has skills that are particularly well-suited to the world of AI. Prompt engineering (the writing of instructions for generative AI tools) is much more about structuring requests in natural language than it is about writing code.

Also: 6 skills you need to become an AI prompt engineer

If a product manager can do anything, it’s writing clearly articulated specifications that take into account known constraints. That’s already very close to prompt engineering. He’ll have to learn the particular nuances of prompt engineering and how to battle those constraints, but he’s in the perfect place to move into that role.

He also understands development teams, projects, and the product management process, which is as important to tech companies as it is to life science businesses.

What about if you’re not a product manager? What skills do you have that might transfer?

Back in the old days of AI, expert systems were built by modelling specific expertise of subject matter experts. But today’s large language models pull information from vast tracts of information, often straight off the internet. If you have a domain-specific expertise that’s valuable, say medical knowledge or petroleum modelling, or even how a house is constructed, that knowledge may be valuable to AI companies trying to break into those industries.

Also: How to use ChatGPT

Don’t assume that knowledge needs to be high-tech or super high-end. If you’re a teacher, you have expertise in teaching and communicating knowledge, as well as the fields you teach in. If you’re a parent, you sure have experience with the real ins-and-outs of raising kids. If you have warehouse experience, go to the front of the supply chain line.

To be clear, just because you know something doesn’t mean you’re instantly going to get an AI gig in that area. But make sure you are aware of the subjects you’re strong in, and make sure you communicate those subjects as part of your transfer search.

Let’s go back to that teacher example. Teaching involves breaking down information into understandable chunks, creating lesson plans, and creating validation procedures to ensure students have learned the material. That’s very valuable in the AI process as well.

Also: 6 AI tools to supercharge your work and everyday life

What about if you’re a good salesperson? Sales skills are perhaps the most important skills anyone can have because selling pays for our salaries. Learn about the AI business, especially the types of prospects and sales cycles. And then present yourself to an engineering-driven company desperate for sales skills. Here’s a hint: most engineers don’t have a clue how to sell.

What if you don’t have so-called professional skills? What if you’re a secretary or administrative assistant? If you’re smart, can apply yourself, and can learn, you also have an opportunity here. All companies need strong organizational skills and the ability to structure and manage projects. Do the learning tasks outlined in this article, do the resume-building tasks described at the end, and you might be able to change that title from administrative assistant to logistics manager for an AI company.

What about if you’re a coder, but not familiar with AI coding? Coding skills are hugely important. Just focus on the next section and train yourself on how your coding skills can use AI. Build a project or two. I talk about that in-depth next.

3. Train yourself

But, Rick says he doesn’t know the AI field. He doesn’t know the business of AI (all the players, how they relate, their competitive landscape). He doesn’t really know how it all works. And he’s never done any actual AI work.

The first is very easy to improve on. Read publications like ZDNET. Read voraciously about the AI industry. In fact, the very best way you can learn about a business you want to move into is to consume all the trade materials you possibly can. Read constantly. If you put in an hour of reading every day for six months, all of it centered on your desired target industry, you’ll build a strong familiarity with that industry.

Also: I took this free AI course for developers in one weekend and highly recommend it

Taking the free courses is also a good idea. But it’s very important not to just consume the material, but to do the exercises. The ChatGPT Prompt Engineering for Developers course offered by OpenAI (the folks who make ChatGPT) and DeepLearning (an education provider) has a hands-on simulator where you can construct prompts with code, and play with them.

The IBM course has a module where you can use IBM’s tool to do some project work. Use it and practice with it. Amazon, too, has free courses that include hands-on experience.

I’ll be spotlighting more free courses. Take them. Take as many as you possibly can. Give yourself time to really work the assignments and learn the material.

Then, get yourself a ChatGPT Plus and Midjourney account. You’ll spend about $30/mo, but you’ll have access to more powerful tools than just the free stuff. Use those tools. A lot. Experiment. Learn their limits and explore their strengths. Become comfortable with what they can do and how they fall short.

Also: You can build your own AI chatbot with this drag-and-drop tool

My point here is simple: make yourself knowledgeable. If you want to get a job in a field where you don’t possess the experience, expertise, or credentials, you won’t get anywhere without any of them. Fortunately, AI is a field that doesn’t require board certification or a specific terminal degree. But it does require knowing stuff.

4. Build yourself some AI resume points

By the time you’re ready to ask for a job interview, make yourself into someone who can answer those interview questions with confidence and competence. When asked, “Show me what you’ve built with AI,” have something you’re proud to show off. When asked about the future of AI, have enough knowledge to clearly articulate all the issues, opportunities, and concerns. When asked about the strengths and weaknesses of offerings by Amazon, Google, Microsoft, OpenAI, and others, know enough to be able to answer.

Also: Generative AI now requires developers to stretch cross-functionally. Here’s why

Another thing that will make you more attractive to hiring managers in the AI space is some experience in the AI space. Now, obviously that’s the Catch-22 that’s existed with jobs since there were jobs. Hiring managers want folks with experience, but how are you supposed to get experience without the job?

Well, here’s how: Be creative.

For someone in product marketing, there are two clear ways to add some fairly easy line items to your resume.

The first is a writing a blog or a newsletter. Starting a Substack is super easy. Write about marketing and business observations involving the AI industry. Deconstruct products and strategies of the various AI players. Even talk about your journey into learning more about AI. Use your product marketing background to provide weight to your discussion.

Also: AI is transforming organizations everywhere. How these 6 companies are leading the way

Now, for those of you not of the product marketing ilk, find how you can relate what you do know to AI and write about it. Experiment with the AI tools you do have and see how they might apply to your unique set of skills. Let yourself tinker, but ultimately, you want to do something you can put up on LinkedIn that has the word “AI” in it.

Speaking of that, especially for our product marketing friend Rick, find an AI Kickstarter project or a small AI startup, and offer to be a part-time advisor. You can offer services like looking over their marketing plans and offering advice or editing, or you can offer to write some marketing copy. The point is, if you don’t require payment, and put in a few hours a week, you can start relating with folks in the AI field.

Now, here’s the trick that will better help you move the job needle: Agree to do these services in return for giving you a title associated with the company. It doesn’t have to be a line title, like “marketing manager.” It can simply be “advisor.” The point is, you want to be able to legitimately list on your LinkedIn profile something like, “Advisor, Happy Valley AI Enterprises,” or something similar.

5. Give it six months

I know. Now that you’ve decided you want to transition into AI, you want the gig tomorrow. Well, pal, that’s not going to happen. But if you give yourself six months, and you work it seriously, you’ll have a pretty good chance of moving into this new field.

Put in an hour each day. Make sure you read relevant articles every day. Do some project work and tinkering in the field every few days. Make AI part of what you do. Try using AI in your current job, just to see how it can fit in.

Also: I spent a weekend with Amazon’s free AI courses, and highly recommend you do too

The point here is that by the end of six months, make it so that AI isn’t this new thing you want to move into, it’s this thing that you’re already very familiar with and use as a matter of your daily activities.

That way, by the end of the six months, you’re not asking to “move into AI,” but to “use your AI skills and knowledge in the AI field.” That’ll come across as much more powerful to hiring managers.

Feature Image Credit: J Studios/Getty Images

By David Gewirtz,

You can follow my day-to-day project updates on social media. Be sure to subscribe to my weekly update newsletter on Substack, and follow me on Twitter at @DavidGewirtz, on Facebook at Facebook.com/DavidGewirtz, on Instagram at Instagram.com/DavidGewirtz, and on YouTube at YouTube.com/DavidGewirtzTV.

Sourced from ZD NET

By Sabrina Ortiz

Implementing AI is only half the battle, but a new report suggests it’s risky not to try. Just make sure you prep your employees first.

When generative artificial intelligence first burst upon the scene, the technology showed potential for making people’s everyday lives easier. Now, AI solutions have been developed to help enterprises optimize their operations, and here’s why you might want to consider using them in your business.

Pluralsight’s AI skills report surveyed 1,200 executives and IT professionals across the US and the UK to better understand how organizations deploy AI and its effects on businesses and their employees.

The report found that implementing AI in organizations had promising results, with 97% of organizations that have already deployed AI technologies benefiting. Moreover, 18% reported experiencing increased productivity and efficiency, 13% reported improved customer service and repetitive task reduction, and 11% said AI reduced business costs.

Pluralsights chart

Despite the benefits, 25% of these organizations said they don’t have plans to deploy AI, while 20% already have and 55% plan to. The hesitation stems from inadequate budget or talent required to use the new tools properly.

A majority of the professionals acknowledged that hesitation could be disastrous in the long run, with 94% of executives and 92% of IT professionals sharing that organizations investing in AI in the near future will be better able to compete, according to the report.

However, the lack of talent to properly use the new tools is an obstacle to the successful implementation of AI, and the report finds that the answer may lie in organizations helping upskill employees.

The report cites IDC research that found investments in skills and digital training of employees will be organizations’ most enduring technology investments in 2023 and 2024, even over investments in generative AI solutions.

Feature Image Credit: Getty Images/Andriy Onufriyenko

By Sabrina Ortiz

Sourced from ZDNET

By Will James

Ready for a masterclass on how to survive and thrive with an AI site in today’s Google?

Casey Botticello joins us on the podcast to share how he took an AI site from zero to $21k+ monthly in under a year.

To explain his approach, he dives into all sorts of interesting topics:

  • AI content production advice,
  • Careful niche selection when using AI,
  • Tips for avoiding the Google sandbox,
  • The importance of adding value and new information in the chosen niche,
  • His process of topical mapping,
  • Focusing on broad research,
  • Coupled with in-depth analysis to identify important topics,
  • And much more!

Casey shares that AI played a role in content ideation and the generation of article drafts, but how extensive editing and fact-checking were essential before publishing.

He highlights the importance of maintaining high-quality content and strategic planning to avoid appearing AI-generated.

As well as the increasing dangers of obvious optimization and over-reliance on popular keyword research tools.

There’s also a discussion on multimedia with some advice that seems to work even in an age where Reddit and other forums are seeing an advantage on Google.

Overall, this is an excellent and actionable look into how to successfully harness AI for blogging, and it’s a must-listen for the Niche Pursuits audience.


  • The types of niches that work best with AI
  • How he built his site with GPT 3.5
  • Avoiding overly-SEO targeted topics
  • Deep diving into a niche
  • Personalizing AI content
  • Topical mapping tips
  • The importance of original visuals
  • Important tips to speed up indexing
  • Topical authority
  • Avoiding over-optimization
  • Staying under the radar and scaling fast
  • Monetization
  • Setting goals
  • And more…



Jared: All right, welcome back to the Niche Pursuits podcast. Today, we are, my name is Jared Bauman. Today, we’re joined by Casey Botticello. Casey, welcome on board.

Casey: Well, thanks so much for having me,

Jared: Jared. It’s great to have you. We are talking all about AI today, which is always a fun subject, especially as we venture into the new year here.

And I mean, I, you have such a cool case study that you’ve, you’ve published. And really, I mean, it’s my first time getting to talk to you, but you’re not exactly. A newbie here to the niche pursuits audience. You did a really good, uh, YouTube live with Spencer and I believe, you know, you’ve been a bit of a listener as well.

Um, uh, I’ll stop telling everyone about you. You tell us about yourself. Give us a little background on who you are.

Casey: Well, you’re right. I, I am a big fan of the Niche Pursuits podcast, so I’m an avid listener. But yeah, I think I’m, I’m active on a lot of, uh, the private forums and different discussion groups.

So I’m sure people have run into me before, but for those who haven’t, um, I’m Casey Botticello and, uh, for the past, I guess now about 10 years, I’ve been doing Digital marketing in some capacity. Really? I haven’t, I’ve done blogging for probably. Full time for the past five years or so basically It started off as a side hustle as it often does.

I Actually kind of got into it a little differently than most. I was a writer I I did sort of high end ghostwriting for clients at a lobbying PR firm Uh, I’m based in Washington DC, so that’s sort of, that’s why, you know, why I live here. So that’s sort of like how I came at it and I was very familiar with SEO, although we kind of referred to it as online reputation management, that was sort of the PR buzzword.


Jared: how you’re able to charge more for SEO, is what you call

Casey: it. I was going to say, no, you’re exactly right. Like I, people think SEOs are expensive, but you, you attach that three letter acronym to anything and you, you wouldn’t believe it. It’s like instant five figures for the fee. It is like, yeah. And which makes sense.

It’s. High profile people, their reputation. So I, I did have a fair amount of SEO experience. Um, and I was, uh, enjoyed writing. So of course, creating my own blog and sort of a portfolio of work with my name attached to it. Or at least just something I could show to people I thought was useful. Um, since most of my other writing was, like I said, I was sort of the ghost writer.

Um, so given that today’s topics all about AI, I, I have a deep respect for writers. Um, and I definitely think that AI is a tool meant to assist writers. This site that we’re talking about today, uh, it. It was generated pretty much using AI content exclusively on the first draft, but the content, to be clear, was edited by me.

So I, I, I want to add that disclaimer that this was not like a one click and then publish sort of AI get rich quick scheme or something like that. I, I spent a considerable amount of time, um, editing this, but I’ll let you get into

Jared: that. Well, and I’m glad you, cause we’re going to get into it. I’m going to ask you a couple of the tough questions on that.

I mean, we are going to be talking about a tool you used, which would be considered like a one click AI publishing tool. And so the clarity is good because I think a lot of people will come into hearing this was written by this tool or a tool IE. It means, oh, okay. And, and all the stereotypes will prevail.

Right? So coming at it from the front end of saying, yeah, we’re going to be talking about a tool. An AI writing tool, notice how I’m trying to build intrigue here, trying to keep them engaged and interested. We’re going to be talking about this tool, but I’m glad you had a clarity that we’re going to really get into how you use the tool beyond just what it spit

Casey: out.

Definitely. And like I said, it’s, it, it, it makes it. Big difference. I know some people say they edit and fact check, but as we’ll get into, there’s a fair amount of time spent doing this. So really this is just sort of blogging with sort of bionic superpowers. That’s how I think of it. It extends my ability to scale content production.

Jared: Modern blogging. Um, so let’s, um, let’s give people a little tease of what we’re talking about here. Now, you published a case study on the Koala Writer blog. So there it is. And then from there, we’re going to be talking about that site and that project. Maybe from a high level, just spend maybe one or two minutes telling us what the project is and if you can, any anything you’re comfortable sharing with where it’s at right now.

And again, really just to give people context into the scope of what we’re talking about. So then we can start unpacking how you did it.

Casey: Sure. So the site is. Uh, almost a year old now, so it’s a, still a relatively new site. Um, it generates, so it’s monetized through Mediavine. So it’s almost purely display ads as far as the, uh, income.

It generated 21, 700 some last month. So that was by far its highest month ever. And it’s been sort of climbing at a rapid pace ever since it was, I guess it was accepted in the Mediavine in mid May. So it’s been kind of, you know, the growth has been up and to the right, um, for sure. It’s obviously been kind of a crazy time with all these algorithm updates.

So I think that really, The case study, uh, does show that if you sort of focus on topical mapping and you focus on clustering the content in a very non SEO oriented way, but then go back and apply some basic SEO framework to the content, you can, you know, Basically scale a site really fast. Now I think these results aren’t typical.

I think, you know, it takes, uh, honestly, there’s a degree of luck in there, but I’ve launched several of these sites and while we’re only focused on this one, all of the sites have more or less survived. Pretty much all the updates since the helpful content update. So, and I’m talking 25 sites, so there, there might be something to that.

Uh, I don’t know if that’s enough data to draw that conclusion. But that’s where the site is today. It’s about this month. It’ll in December, it’ll probably do about 25 K or so if it continues. And that’s about 550, 000 sessions.

Jared: It’s so there’s so many interesting storylines there, right? Like just. Having a site that survived the HCU, I mean, I’m not going to say it’s rare, but it’s, it’s, it’s certainly an accomplishment, right?

At this point, the helpful content update has come through and really hit a lot of the sites that listeners have. And maybe it’s mild, maybe it’s 5, 10%, many, obviously it was crippling. You don’t just have a site that survived the HCU, it’s continued to thrive post. HCU and October core update, November core update, but not only does it thrive through all that, it is basically built entirely on the backbone of AI.

So, um, anyways, this is going to be such a fun, I’m worried we’re not going to get it all in, in the hour or so that we have. So, um, Hey, let’s start, let’s start at the beginning. And again, like, let’s try to keep this as tactically focused as possible, because every, I’m going to go ahead and just assume that everyone listening is either heavily utilizing AI.

Um, using AI in part of their workflow, but not all of it, or knows they need to going into the new year and the year’s coming. So they’re gonna be very interested in a lot of the tactics you use. Where did the concept for this come from? And, you know, what, what, what sort of AI were you, you know, using prior to this website that got you interested in using this?

Casey: So basically, as soon as I’ve been playing around with sort of the pre chat GPT tools like Jasper and stuff like that, um, in 2022, but I wasn’t really happy with any of them. I certainly wasn’t going to build an entire site based on them, but I was intrigued by sort of like the precursors and then like most people when chat GPT came out in late November.

Uh, I was, I immediately, especially as a writer, I was kind of confronted with the reality that, you know, AI can produce, and this is very niche specific, and a big part of this is niche selection, which I can go into, but, you know, for the right niche, AI can definitely produce at least a great first draft Or sort of subtopics or different sort of topical mapping structures that can really save you an enormous amount of time.

So, I immediately dove in. I began playing around with it. Uh, I, you know, was basically looking for I knew there’d be a good opening basically to, to try it. Now at first Google didn’t clarify their stance. So if you remember back in like January, it was sort of like almost considered black hat for a while, you know, before we really knew their stance.

And then come, so I, I started the case study in January and at first I just kind of shared it on a few of the forums I’m on, but it wasn’t. I didn’t even post it on my own site blogging guide. Like I didn’t start documenting it until February when, uh, Google made that update where they basically said, you know, AI, any content is okay, as long as it’s high quality.

So at that point, you know, it was sort of off to the races. Um, I still didn’t want to, I didn’t want the site to have any of the characteristics of an AI site. So I made a real effort to sort of hold back on not going wild with publishing. Um, I have all the likes, the specifics on my website and on that qual article of the number of posts, but I started off with first month with.

27 posts or 29, I believe. And basically these posts, again, I also wasn’t sure the effective AI’s basically I wrote these posts and, or I have a team of freelance writers who I outsourced this to. So the typical process you would go through for a niche site, but I made sure these first posts were really good and not just really good, but they were very much the sort of.

Helped Google Understand what the site was about and that was very deliberate So the site the site was a fresh. Well, it was a domain I had purchased Actually like a year prior to this and it was just a brand of a good brandable name So that the domain itself had no backlinks there never been a site as far as I could tell on the The built on the domain, you know, it had been listed for sale basically.

So it’s not

Jared: like a classic age domain. It was just a, you kind of had city. Had you published even like a landing page to it? Or was it

Casey: just, that’s a good point. And I I’m glad you asked that. So basically I do what I call like a shell site where I knew that basically I was going to be doing this AI experiment starting.

Maybe around November. So I, I, as soon as that was clear, I put up the legal pages, the homepage. Uh, supporting sort of pillar pages and if, and maybe like five blog posts that were generic enough that I wouldn’t have to scrap the whole site later, but were specific enough that Google could start, you know, understanding potentially what the site was about.

And I made very, a very deliberate effort to get that site indexed. And although there weren’t that many posts or pages. It was indexed, and I noticed that the, the crawling of the site seemed pretty good. I made sure the, the host, I put it on my top notch hosting. I had a fast theme. I stripped all plugins.

You know, I did it kind of textbook. And uh, that, that was how the site was left until I started publishing in January. I know for a fact that helped that helped definitely just it was indexed when I started publishing in January The the post didn’t index right away, but we’re talking only like a month or less delay so there was no like traditional sandbox period let’s say and so yeah, like If you’re going to do this in such a short period of time, that, that almost has to happen, you know, you have to really hit the ground running, um, otherwise you just mathematically can’t get to like, I mean, I was, I thought the goal was to get to Mediavine in a year.

That was, I thought, ambitious, uh, like starting from zero. So the fact that I did it in half that time and really. After I talked to Mediavine, they were like, cause I kind of was like updating them on my progress because I have other sites with them and they always throw around that often cited statistic that second sites don’t do as well as first sites, Casey.

And I was like, no, this one’s going to be like. A big one. And they were like, okay, and I literally updated them every month, of course, asking to make an exception and allow me in. And they said, no, in fact, I learned that your site actually, for most networks need to be at least four months old, including Mediavine, uh, to, to be accepted.

So the fact that it got in, In May, the fifth, five months in about as soon as possible. It was like six days after the

Jared: cutoff or whatever. Now you said that you, I mean, I don’t want to put words in your mouth, but it sounds like you hand wrote or had a handwriting process for the first, I think he said like 29 articles.

Then you moved into an AI assisted model after that. So the first 29 was just full bore on a traditional setup and publishing style that you would have used pre. Well, pre chat GPT,

Casey: basically. Yeah, I think ten were written by me, um, and they were like long form, sort of. Pillar posts, if you will. And then maybe 19 were done by the kind of experienced team of freelancers that I was currently working with.

Jared: Now, I don’t want to move on to fat. I have more questions, but before we move on from it, you did mention, I wrote it down that there’s a bit of a different process, I think you said to picking a niche when you are looking at using AI and, uh, maybe expand on that a bit. Like how did you select this niche in particular?

Or what about this niche do you think has caused it to be successful with an AI focused model?

Casey: That’s a good question. Um, and I’m still experimenting with that with the other sites. But the short answer of what I’ve kind of learned is that for a site to work, you know, you need to think about what the value add or like the information gain sort of is.

So if you can add perspective, if you can add original photos, if you can, you know, basically if there’s a human element. That you can incorporate that and the AI writing, at least can take care of sort of what amounts to, I don’t want to say fluff, but what amounts to sort of the body supporting content, almost like if you were doing an e commerce store, I think of it more like that, where you’d have product descriptions that are, you know, AI can write quickly and punchy copy that saves me a ton of time and money.

So the idea was to find something that. It was fact based, um, that was fairly evergreen. And as I later got into it, the other thing that became obvious, cause at first I wasn’t, Koala didn’t exist. Right. So it, it wasn’t even around at the beginning. I was literally using chat, GBT, and then like kind of manually assembling articles.

So it became clear later on though, that the key was finding topics. That were cost effective and had the highest ROI relative to the cost of AI content production. And so it costs different amounts depending on which GPT model you use. So my goal was actually, everyone was excited for GPT 4 and it’s better.

It’s great. And now I use 4. 5, the turbo, of course it’s even better, but the, this site was built almost entirely on 3. 5. And my best sites still are, and that’s like, kind of, that’s a real hidden gem there that I think people, you know, should take note of because the cost is about five times less. So if you’re out there competing with people, you know, there’s always the question of, well, how do you build a mode if you’re, everyone else can pump out this content.

In addition to adding your own unique images, insights, videos, all that stuff, another thing too is the, just the cost. You know, you can deter people when you can put up a thousand posts, you know, for, you know, 1 and they can do it for five, you know? So it’s, so you have to think about things that topically make sense, ideally things that you have some real expertise in.

And going back to your, I think your previous question, I picked this niche based on a, uh, topic. It was a tech topic and. Um, I basically had taken a few courses on this in college. So it wasn’t like my major or anything, but it was like a sort of like a passion or like interest of mine and the topic and part of why this grew so fast was the topic was well established, but like a lot of things with tech, you know, it’s undergoing quick, rapid change.

So the specific kind of angle I was covering. You know, was, was rapidly evolving. There were no sites dedicated purely to this sub niche. And this was like, like I said, the laser focus. I wrote out the map of the first 750 articles. Which I just finished actually, and I wrote that out about a year ago in, in December, I think, and I Stuck to that and I had to try really hard, Jared, not to Deviate when I would find ones where the, it was clearly keyword search volume But the whole idea behind this was to avoid anything that would appear SEO overly SEO driven and Although I didn’t know it at the time that would also come back and save me probably during all these helpful content updates because This was like the least SEO oriented site I’ve ever done.

And I’ve had very successful ones that are SEO driven and long tail keywords and all that. This was the exact opposite. I mapped every article before I had even.

Jared: Well, that was going to be my second question because you talked earlier about how AI contributed to both your niche selection, but also your, also your topical mapping.

I think maybe a traditional SEO approach outside of this AI model that we’re about to talk with that you went into would be go to a keyword research tool and start with a seed keyword. You know, maybe I’m not, I don’t even know what your site is about for the record. But let’s say you mentioned text, let’s say iPhone, right?

So you go into a keyword research tool and you would type iPhone in and you start parsing through and building out the main topics, the subtopics, the long tail topics, the questions, the answers, the comparisons, the reviews, the buyer guides, the how to’s, all this, and maybe create a topical map that way.

If that was something you even wanted to do before you started a website, right? Like, what does it look like to build a topical map in an AI world that isn’t SEO driven? I think,

Casey: well, a lot of people, unfortunately, are still using it the same way. But if the way I did it, and I think the way I recommend to people, um, and I have a, a post I recently added.

Just to give a little more insight if people want to look at on blogging guide just on if you just google topical mapping I’m sure it’ll come up but Basically, the idea is that instead of starting with the keyword research tools You start in a very broad research phase where you don’t use a single keyword research tool You totally ignore volume.

This does require you knowing your niche because you have to Pick something that’s laser focused while also knowing intuitively that there’s enough traffic for whatever You’re trying to accomplish in my case. I was saying okay if I can win, you know half of these 750 articles it are there 50, 000 sessions because that that was just the media vine cutoff so that was sort of And then from there, I basically on a whiteboard at first, but later just in like a notion would basically write down article ideas after thoroughly investigating the subject.

And I mean, everything from manually Google searching. every possible sort of query to pot scraping podcast transcripts for things that maybe weren’t indexed but were valuable info, to watching YouTube videos. I joined a private forum related to this niche. I actually went to an event Um, related to it.

So like I actually talked to people and that, uh, I joined even a few webinars. So the goal was to get, you know, a real tight, you know, sort of feel for like what people actually cared about and what they were talking about and kind of where things were headed. Cause I knew that I didn’t have a chance to outrank the large tech incumbents that were had broadly covered.

Some of these like shoulder niches, but I knew if I stayed in this very narrow lane that, uh, and people are not covering these topics and When I would later spot check them in hrefs or whatever They would be a lot of them would be zero You know search keywords So it was a classic case of like, you know If you were just going through looking for search volume, this would never have registered.

But, you know, if you knew anything about the niche, and even with just a little bit of common sense, You could be like, okay, the tools are not picking this up, which is fantastic because less people are going to be going after this, but also less people are going to understand the strategy of the site. Um, so I basically didn’t put ads on the site until I got to Mediavine.

So I think from both the user experience perspective, but also just strategically. I knew this site, if it was going to grow fast, it needed to stay under the radar on Ahrefs or not appear on one of those Twitter lists. Where someone’s showing like low, you know, low DR, high trap, you know, so the goal was like, yeah, basically to stay off the radar and do this as fast as possible without it appearing AI generated.

So I tended to stick to like 20 to 80 posts a month. And these were posts that, like I said, I heavily edited manually. So a

Jared: lot of that. Because you came up with 750 articles to write, and a lot of what you just described sounds very manual. Where did AI play a role in that or did it?

Casey: It, it, so AI played a big role in, after I collected the research, the content sort of ideation phase, I, you know, and maybe it’s partially biased because this was a tech niche, but it did a great job coming up with all these sort of questions and perspectives.

Uh, that allowed me to write about a topic is not like, that would appear as maybe a people also ask sort of query, but that, uh, it, it really was, my article would be more sort of focused on, like I said, the perspective or some very like granular or. Part of that question, I would indirectly answer it. And the bet I was making was that, you know, basically Google at some point, I figured was going to destroy some of these sites that were just.

regurgitating people also ask and suggested questions and all that. And so this was meant to be actually, even though it was AI built, uh, very high quality. Um, so the, the, as far as how I used AI. Besides content ideation, I literally wrote every, other than the first 29 articles, I used Koala for the other, you know, 700 some, and, uh, yeah, I, I would, there was a lot of tinkering and perfecting my settings and getting that right, but I didn’t focus on that too much because I’m just using Um, Quala advertises itself as a one click publishing tool.

However, if you actually read anything about it, there’s, it very quickly, you know, explains that’s not the best use case. The best use case for this is to, you know, basically generate a draft and then go in and fact check, edit, link, you know, kind of do all the things you’d normally do. And so. I think at the beginning I probably spent two to three hours editing an article, uh, just out of like an abundance of caution and Like kind of really wanting to get this right.

Uh, but toward the end, like in this last month here, I got the process down to probably like more like an hour per article, maybe a little under. So it wasn’t like a one click and then publish model. We’re still talking, you know, like I said, it’s, you know. 80 to 100, you know, hours. And this was like a substantial part time job.

Um, at least at times it was more like almost like a full time job. So now the, the advantage though, was of course. I was only spending about two to three dollars an article to produce these, uh, not counting my time, which isn’t nothing, but still it was allowed me to basically make this just a really profitable and just kind of, yeah, scalable process.

Jared: Let’s move into content production. I’m looking at what you published over at the koala case study. I mean, yeah, I think a lot of people, I think it’d be good for people to hear like, this isn’t one of these, you know, press a button, 5, 000 articles go live on the site. We’re off to the races, right? I’m looking at like January 29 articles, which you talked about handwritten February 21, March 85, April one 20 may back down to 30, June 25, July 30, August 47.

And then now we see September 130, October 112. So certainly. More than you could publish, um, if you were what, not more, but more than you could publish usually as a single operator of a website, but not crazy flood the internet with five, 10, 000 pages. So definitely in the lower end of what many might expect to hear.

So let’s get into the process of what heavily edited content looks like. Like, how do you. Um, how did you utilize KoalaWriter? Perhaps maybe we should start there and just some tips for people who are struggling to get results out of KoalaWriter they feel are even capable of publishing.

Casey: So for starters, the, if the easiest route is to use there, when I started there, GPT 4 wasn’t available and 5.

Now you can use both of those. So the, those. Those large language models are much better, and they do produce, like, on the first try, much better content. So if you’re just starting, or you’re struggling, sort of, with getting the first draft right, you might have a more complex niche that does require, uh, using one of those.

And to those people, I’d say, don’t, don’t get hung up on my strategy of really trying to drive the cost down by using 3. 5. Just Go with the, the, the, at the beginning, go with some of these, you know, higher caliber models, see if that affects the output and. Because a lot of people try to do what I did. They start with the cheaper one.

Um, I would say like, you know, unless you’re literally going to be doing 10, 000 posts or something, you know, the cost is still low enough that, that really shouldn’t be an issue. Uh, so there’s that like start with the right, the right version of these AI systems. The other thing is. Adjust the tone depending on your content.

Koala basically has different, um, personas sort of, or like writing voices that you can choose. Like the default is SEO optimized, right? So don’t pick that. I’ll, I’ll just, it’s nothing wrong with it, but just don’t pick that. It’s in a

Jared: post HCU world. Don’t pick that. Yeah,

Casey: no, I mean, I thought that was obvious even in a year ago, but yeah, like.

There’s no, it quality is a great, everything is already SEO optimized, like within reason. So don’t worry about that. You know, I choose professional if it’s like, you know, if I was talking about like kind of trying to basically, yeah, sound more like I’m giving like sort of a. A talk on like, maybe like a SAS product or like something like that.

But I actually like to use the friendly setting, which sounds kind of stupid. Like you’re like, and it does generate some weird titles when you do that. Like it’ll ignore that though. You’re, you’re going to have to rewrite the titles. So, but the titles will be like, I don’t know, like, you know, they insert weird, like kind of kinder language, but the actual article.

It’s basically a slightly more down to earth and it’s sort of more explanatory. Like I, my articles included a lot of tutorials and a lot of just me walking people through the process and I included screenshots and. And custom images of like products and infographics. So I wanted it to be clear. So that’s kind of where I arrived at that.

The other thing I would say is, uh, when I started, it didn’t have this internal linking system, but in the last like month here, the thing that I struggled the most with has been solved. So you can basically like it maps your site. And as you add new articles, basically. It does the internal linking automatically.

So that’s kind of like the greatest feature right now, I think, and I, I don’t know why more people aren’t using that, but it, it does not go overboard. It, it very sort of judiciously, like if it makes sense, it uses it. But, um, I had to do a lot of that manually. And that was a big part of why my time per article dropped in the latter part of the year, because I, I right there, I didn’t have to do any.

internal linking, it would do plenty for me. So the other thing too, is that you have to recognize, and this is true of all AI writers, AI writing tools, you need to basically make sure that you’re removing. Sort of the fluff and Koala is no exception. There’s a couple key phrases that if you start to use it enough, you’ll recognize and That to me was like a pretty obvious red flag.

They’re almost always transition sentences between sections and paragraphs Lots of like, you know, um, you know, in summary, like, like, but in conclusion, yeah, but doing weight, like doing that sort of on a micro level, like over and over and a lot of like, sort of even flowery kind of language in some of the.

So basically I would say you should plan on rewriting like kind of each section, intro and conclusion. The subheadings are usually done perfectly fine, but you might have to adjust like a word or two. Again, the title should be completely, that, that should not be left up to Koala. That should be either you, or you can use chat GPT for that to help.

It’s actually pretty good at that. And I also use chat GPT for like the meta descriptions. That’s been like a big time saver. I used to spend so much time, like I, I’m big on on page SEO. So like everything needs to be dialed in and I, if I don’t have the right meta description, I don’t publish, but now with chat GPT, you know, an easy prompt you can use is, you know, please write me a meta description for the following blog posts based on the titles that are between 130 and like 160 pages.

Characters, including spaces. I have some command macro to my keyboard like that. And I basically just do that and it’ll actually iterate and automatically give you like five to 10, um, just by entering that. And I would say, start with those. And again, edit that, you know, use that for the article, you know, you have to use all these things.

Both to save time, but also to improve the quality. If it’s not improving the, the quality, I don’t think there’s really much purpose in using the AI tool because eventually, you know, Google SGE is going to come and it’s going to, it’s going to take, you know, sort of your limited text, low quality responses.

You still need, like I said, I’m very big on original images. Uh, I, I don’t think I have a single blog where I haven’t done either like extensive branded infographics or I haven’t done like, you know, I’m sort of like an amateur photographer and I’ll, you know, go out and I love taking pictures. So that, that’s like an easy.

A very easy way that you can set your content apart and it just looks a lot more natural. And like I said, you’re not relying on the text so heavily. Uh, the other thing too is AI can run a little long, so I would always add prompts manually in any tool, but including Koala to basically say paragraphs should be no longer than three sentences.

Sentences should be no longer than, I forget how many words I have it set up, but a few basic parameters like that go a long way. Um, and it’ll depend on your niche. I, I’ve been doing this with other sites and. Some sites, it just works really well with like some sites, it clearly was trained on like, you know, it got into those like, you know, Reddit forums and it like is pulling real actionable insights on others.

It’s, it’s really pretty high level generic fluff. So the key is, in my mind, you have to be able to give it a very specific topic. Um, and so what I enter for the article title or prompt is usually, that’s why I spend a lot of time mapping it out. I need to think, okay, first, what’s the actual topic that I’m trying to cover?

And then the last step after ideation is basically for me to translate it into a SEO friendly title, not just for the reader, for the AI tool to even write the article. Because if The tool may not actually do a very good job, um, writing the article if it doesn’t understand sort of the nuance of what you’re trying to get at.

So I, I basically give it a long title.

Jared: Let’s talk. Once you get the article out of Koala, you talk about the extensive amount of editing that goes into this. So what does that look like? Because for a lot of people, I mean, you’ve already touched a little bit on it. So. I mean, just read back a few of the things that you’ve said, um, shortening the sentence structure of the paragraphs because of the, the run ons and the, the fluff, um, uh, you know, kind of modifying the titles, uh, perhaps not the headers as much, but like, what else goes into heavily editing, um, these types of con, these types of articles that come back, like, what are people needing to look for?

Casey: I think that people need to add, there needs to be some, it doesn’t have to be a lot in terms of words, but it, there does need to be like. Maybe let’s say if you’re writing like a 1500 word article, I would say ballpark two to 300 words of like real sort of actionable insight. Like, and that might be, I think it’s important.

To front load that are sort of positioned at the beginning of the article. Um, again, Google’s sort of moving away from, you know, these long articles that are designed for ads where you have to scroll through and maximize impressions. So it’s, it’s definitely key that. You know, get the human insight in there, and it should be like clear that this was written by a human, uh, it’s not whether it’s human insight per se, it’s just, it should be original if, if there should not be another article with that paragraph, if you run it through copy scape should come up as 100 percent original, it shouldn’t be like, you know, a rewording of someone else’s content, like you need to actually be.

Right. add some value. And if, if you’re a subject matter expert at all, that’s easy to do. If you’re

Jared: not, what kind of value do you mean? Like, are you, you know, um, how do you find something to add a value that hasn’t been surfaced or wouldn’t show up in copy scape?

Casey: So the, a lot of times, like the best examples I like to give her with like tutorials, they’re really actually a great way.

So, uh, Koala can do a good job with like outlining the steps, but unless you’ve actually used like a software product, Koalas and all AI tools are limited to scraping basically. The like user documentation, uh, that like a piece of software has out there. So like, if you were talking about how to take like the perfect picture, like with.

DSLR cameras or something you, yes, it could sort of scrape the steps and do that perfectly. But in there, you need to be adding your own insight explaining like, okay, this article is about urban exploration and the sort of, and how to get like this amazing photo for your Instagram. So the. Basically, you need to figure out what your audience is really looking for.

And the answers to something like that is probably like, they want to know, not just how to do it, but what is the right, like, low light setting or, you know, something that is relevant to whatever you’re writing about in that article. But I pick urban exploring because I’m big into that. And basically, like, I’ve read through, like, photography tutorials.

And yeah, you can be the best photographer or teacher in the world, but if you haven’t, if you’re not writing to the hyper specific sort of user, then if you don’t have that persona in mind, then you, you probably miss sort of, you’ll rank, but you’ll miss all those people that actually care about, you know, the quality of the content.

And to them, that’s for half the people that read the article, that’s probably all they really care about. Yeah. Some people are looking for how to set up and navigate to this mode and adjust this setting. But most are really probably looking for something different. And they basically. Don’t know how to type that in 20 words or less because it’s, they don’t even know the term for it, so yeah, you need to focus on really, I think having like this sort of persona of your audience and, and that’s why you do have to pick a niche that you’re.

At least somewhat familiar with ideally pretty experienced with if you’re not, you need to be willing to learn. And like I said, that research process might be vastly longer, I think, um, for you. Yeah. So

Jared: beyond the text that goes on the page for this website, maybe just Give us a punch list of additional things that are going into it beyond Koala.

You mentioned photos, like unique photos. You mentioned, uh, insights, whether they’re expert insights or just your own insights. Like what other things, maybe just a punch list of things on the top of your head that you’re adding after Koala.

Casey: I’m adding a custom featured image for every article. That’s.

given, I consider that more important than the title, to be honest, like Google, you know, we’ll replace text, but like the, the images right now. And if you want that, you know, featured snippet, like if you have a great featured image, you know, that’s a great way to get to win those. Like, especially if you then do custom images.

Let’s say you are doing a tutorial, if you do that for each step, and when I say custom images, I mean, it could literally be a screenshot, but like I was helping somebody with a site recently, and they were doing these great tech tutorials, but like, I was like, you know, run this against like a reverse Google image search.

Okay, there’s like, you are doing this, and this is an original, like, it’s your account, you know, I can see all that. But like, it looks like all the others and it, Google recognizes as that. And I’m like, create a custom border. It doesn’t take that much time to figure out how to do that or outsource that to somebody.

You’re saving all this money on writers. So you can spend a little on graphic design and add annotations, add colorful boxes. You know, the photo itself, just like the text should come up with basically no results or only your results when you run it through at the end. So I’d say the featured image should be its own style.

Each, you know, and it doesn’t have to be steps. If you’re, if you’re doing something more generic, you can just sort of have like, you know, find like some vector images. of a certain style, uh, and basically reuse those same characters, but in unique ways over and over. And that’s a great way to both build sort of a brand and some sort of continuity while also like, like I’m always shocked how many people don’t do that.

And I rank for a lot of image searches, even though this isn’t inherently visual, uh, Niche. So that’s why it’s, it’s interesting. And of course if you’re doing a product in there at all, even if you’re not like reviewing a product, I think having the original photos best, does that mean you even need to buy everything?

I don’t think so. I, I think like, honestly, like the smartest people doing this right now. I saw a site the other day that I recognized as like AI written, but with really good images and it was going after sort of the most competitive niche mattresses and this person, like what I think happened was they basically.

Probably paid somebody who has a mattress store like to have the to rent the place for like a day Because they clearly or or there was they just weren’t paying attention, but somebody was running around And they even changed outfits and things but I could tell based on like Kind of the, when I really looked in the background, I was like, no, this is all one big, like a continuous shoot.

You can even see the daylight sort of fading throughout the day. So this wasn’t their mattress lab. This wasn’t, you know, uh, like they didn’t buy a million dollars worth of mattresses. You might have to get creative, like, if you don’t have a niche where you already physically have the products. But, I mean, these are just, you know, you gotta get creative, and that’s one way I’ve seen people do it.

Jared: I mean, it’s clear this site had a pretty meteoric growth trajectory, you know? It was qualified for Mediavine within, in month five. It’s gone on, you know, it should make somewhere around 25 grand this month, and it’s 12th month of existence. Like This is an impossible question, but I just want to give you a nice big high level question to see where you go with it.

Like, what are the things that are causing this site to succeed, especially in this world where this type of blog approach by and large for a lot of people isn’t working as well as it used to, right? And that’s not meaning that it’s the sunset of these types of sites, but it just means that in this current state in time.

With a lot of the updates that have come around, a lot of people aren’t succeeding with this. You’re succeeding with that on a very new site using kind of an AI driven model. So if you look at these things, I know you have these kinds of perspectives because of your history in this space. Like what are, what, what are the things you think that are causing or driving this success?

Casey: Obviously, yeah, the million dollar question. I think though there actually are a few answers though. And mainly because I’ve since launched like 10 other similar sites. And based on the data, I can tell you a few things first. Like if you have something that’s overly SEO optimized, there’s no question.

It’s more susceptible to updates. I had sites that were making a lot and. A site that in particular I bought actually right before the helpful content update that got nailed. So I hadn’t written any of that content, but it was already on Raptive. It was doing well and it got obliterated. So like I did a deep dive of that one and I don’t think even before I looked at it, I realized how kind of gratuitously SEO optimized it was.

There was a lot of keyword stuffing, you know, articles. We’re clearly just chasing long tail queries. Like if, if I were to like arrive at this site as a user and enter into the search bar, like a related question I would have, I still wouldn’t come up with the right answer. Like it wouldn’t show me like I hadn’t covered, they hadn’t covered the topic completely.

So, whereas mine now that’s, that’s kind of the goal is. Uh, you know, I, it, not in a strict sense, but I think topical authority and sort of just covering the topic, uh, is, is kind of critical because if you do that and there, and you manage to find a niche that’s slightly underserved, you know, you can, Google kind of doesn’t have a choice to rank you like, I don’t think.

This content is unbeatable. Um, but I do think that there, there is no substitute. So Google keeps just, you know, they have to rank it. You know, it’s the only thing even answering like in an authentic way, the question, the rest are like, and to be clear, cause I know a lot of people will be like, well, what about user generated content and read it?

This is a niche that actually is very. Like heavy on that and I I do lose sometimes to like a reddit post every now and then like I’ve noticed that like but nine out of ten times I still win, so I Read it in court. They’re not you know, like I said, that’s where the visual comes in I think a reddit post almost never has It doesn’t, you know, if it has a video, it’s not an original video, it’s, you know, just reposting something same with Quora.

So that’s why I would say that you need to be adding those, those extra elements. They’re not even just human elements, but sort of like just, you know, multimedia elements. Infographics are one of the best things that I’ve probably added to the site. And I have some of those that get an okay amount of social traffic, but almost all the traffic is still, you know, organic search.

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By Will James

Sourced from NichePursuits

In the dynamic realm of AI, crafting effective prompts is pivotal for success. Matt Wolfe, an AI reporter and analyst, stresses the importance of specificity and goal-oriented prompts. For instance, transforming a generic request into a refined prompt involves defining clear intentions, considering context and persona, and envisioning the desired output. A concrete illustration of this approach involves upgrading a generic prompt for a blog post into a detailed, AI-ready masterpiece.

Developing an AI workflow

Once mastered, AI prompts open the door to a transformative workflow. A day in the life of an AI-enabled marketer involves leveraging AI at various stages, from summarizing performance metrics and generating email subject lines to organizing customer feedback and developing blog post titles. The integration of AI-driven audience segmentation and retrieval augmented generation (RAG) techniques can lead to significant improvements, as demonstrated by a 38% lift in click-through rates achieved through AI-personalized email campaigns.

Testing AI tools

HubSpot’s AI Marketing Report reveals a notable increase in the adoption of AI and automation among marketers. Dharmesh Shah, HubSpot’s CTO, emphasizes the rapid evolution of AI and encourages marketers to test its capabilities. With breakthroughs like web-browsing capabilities in ChatGPT, marketers are advised to explore a variety of AI tools tailored to their specific challenges. Matt Wolfe recommends tools like Conveyor for chatbot latency reduction and image generation tools like Leonardo and Kaiber for creative assets.

AI integration across marketing workflows

AI has proven to be a valuable contributor across marketing workflows, exemplified by HubSpot’s AI-powered content assistant streamlining creative processes. Ramon Berrios from DTC Pod integrates AI extensively in marketing tasks, showcasing its versatility in newsletter production, podcast automation, social media management, and content creation. However, it’s crucial to note that AI should complement, not replace, human creativity, as high-quality and engaging content remains the cornerstone of marketing success.

Identifying AI-generated content

As AI-generated content becomes more prevalent, marketers must discern between quality and spam. Key indicators include the content’s originality, insightfulness, and alignment with the brand’s voice. The article stresses the enduring importance of high-quality content, emphasizing that engaging and captivating material will always prevail. As AI evolves, marketers will need to identify tasks best suited for AI while maintaining a human touch in areas requiring creativity and authenticity.

Ensuring brand safety

With the integration of AI, brand safety becomes paramount. Transparent communication and the ethical use of AI are essential to preventing unintended consequences. Privacy concerns are addressed by HubSpot through clear terms and conditions for data import. Marketers are urged to be cautious about data security and aligning AI usage with brand values. Transparent communication within the organization is crucial when using multiple AI platforms with distinct models.

Integrating AI intentionally

While AI presents significant opportunities, there are inherent risks that marketers must navigate cautiously. The article outlines five AI no-go’s, including gathering data without consent, having unrealistic expectations, using AI tools with unclean data, neglecting source verification, and ignoring ethical considerations. The emphasis is on specificity, purpose, and ethical data use, as these principles are foundational to HubSpot’s AI strategy.

The human touch in AI

Despite AI’s advancements, it remains a work in progress, subject to biases and limitations. The article concludes by highlighting the importance of humanity in marketing. Kipp Bodnar, CMO at HubSpot, emphasizes the need for a real point of view in marketing, rooted in belief and humanity. The key to winning with AI is strategic, intentional, and vigilant use, amplifying human potential rather than overshadowing it.

In the rapidly evolving landscape of AI marketing, these seven must-know tips from HubSpot’s AIMS team provide a comprehensive guide to navigating the complexities, ensuring marketers make the most of AI while upholding brand values and delivering meaningful experiences

Disclaimer. The information provided is not trading advice. Cryptopolitan.com holds no liability for any investments made based on the information provided on this page. We strongly recommend independent research and/or consultation with a qualified professional before making any investment decisions.

Derrick is a freelance writer with an interest in blockchain and cryptocurrency. He works mostly on crypto projects’ problems and solutions, offering a market outlook for investments. He applies his analytical talents to theses.

Sourced from Cryptopolitan

By David Nield

Generate your own text—but get help from the AI bot to make it stand out.

It’s been quite a year for ChatGPT, with the large language model (LLM) now taking exams, churning out content, searching the web, writing code, and more. The AI chatbot can produce its own stories, though whether they’re any good is another matter.

If you’re in any way involved in the business of writing, then tools like ChatGPT have the potential to complete up-end the way you work—but at this stage, it’s not inevitable that journalists, authors, and copywriters will be replaced by generative AI bots.

What we can say with certainty is that ChatGPT is a reliable writing assistant, provided you use it in the right way. If you have to put words in order as part of your job, here’s how ChatGPT might be able to take your writing to the next level—at least until it replaces you, anyway.

Find the Right Word

Using a thesaurus as a writer isn’t particularly frowned on; using ChatGPT to come up with the right word or phrase shouldn’t be either. You can use the bot to look for variations on a particular word, or get even more specific and say you want alternatives that are less or more formal, longer or shorter, and so on.

Where ChatGPT really comes in handy is when you’re reaching for a word and you’re not even sure it exists: Ask about “a word that means a sense of melancholy but in particular one that comes and goes and doesn’t seem to have a single cause” and you’ll get back “ennui” as a suggestion (or at least we did).

If you have characters talking, you might even ask about words or phrases that would typically be said by someone from a particular region, of a particular age, or with particular character traits. This being ChatGPT, you can always ask for more suggestions.

Screenshot of ChatGPT in a browser window

ChatGPT is never short of ideas. OpenAI via David Nield

Find Inspiration

Whatever you might think about the quality and character of ChatGPT’s prose, it’s hard to deny that it’s quite good at coming up with ideas. If your powers of imagination have hit a wall then you can turn to ChatGPT for some inspiration about plot points, character motivations, the settings of scenes, and so on.

This can be anything from the broad to the detailed. Maybe you need ideas about what to write a novel or an article about—where it’s set, what the context is, and what the theme is. If you’re a short story writer, perhaps you could challenge yourself to write five tales inspired by ideas from ChatGPT.

Alternatively, you might need inspiration for something very precise, whether that’s what happens next in a scene or how to summarize an essay. At whatever point in the process you get writer’s block, then ChatGPT might be one way of working through it.

Do Research

Writing is often about a lot more than putting words down in order. You’ll regularly have to look up facts, figures, trends, history, and more to make sure that everything is accurate (unless your next literary work is entirely inside a fantasy world that you’re imagining yourself).

ChatGPT can sometimes have the edge over conventional search engines when it comes to knowing what food people might have eaten in a certain year in a certain part of the world, or what the procedure is for a particular type of crime. Whereas Google might give you SEO-packed spam sites with conflicting answers, ChatGPT will actually return something coherent.

That said, we know that LLMs have a tendency to “hallucinate” and present inaccurate information—so you should always double-check what ChatGPT tells you with a second source to make sure you’re not getting something wildly wrong.

Choose Character and Place Names

Getting fictional character and place names right can be a challenge, especially when they’re important to the plot. A name has to have the right vibe and the right connotations, and if you get it wrong it really sticks out on the page.

ChatGPT can come up with an unlimited number of names for people and places in your next work of fiction, and it can be a lot of fun playing around with this too. The more detail you give about a person or a place, the better—maybe you want a name that really reflects a character trait for example, or a geographical feature.

The elements of human creation and curation aren’t really replaced, because you’re still weighing up which names work and which don’t, and picking the right one—but getting ChatGPT on the job can save you a lot of brainstorming time.

Screenshot of ChatGPT in a browser window

Get your names right with ChatGPT. OpenAI via David Nield

Review Your Work

With a bit of cutting and pasting, you can quickly get ChatGPT to review your writing as well: It’ll attempt to tell you if there’s anything that doesn’t make sense, if your sentences are too long, or if your prose is too lengthy.

From spotting spelling and grammar mistakes to recognizing a tone that’s too formal, ChatGPT has plenty to offer as an editor and critic. Just remember that this is an LLM, after all, and it doesn’t actually “know” anything—try to keep a reasonable balance between accepting ChatGPT’s suggestions and giving it too much control.

If you’re sharing your work with ChatGPT, you can also ask it for better ways to phrase something, or suggestions on how to change the tone—though this gets into the area of having the bot actually do your writing for you, which all genuine writers would want to avoid.

Feature Image Credit: PM Images /Getty Images

By David Nield

David Nield is a tech journalist from Manchester in the UK, who has been writing about apps and gadgets for more than two decades. You can follow him on Twitter.

Sourced from WIRED

By Anant Jhingran and Matt Roberts

A look at how an integration layer completes AI applications and how integrations can be done better with the help of AI.

AI is reshaping the enterprise landscape. Already, developer productivity, digital labour, email marketing, website creation, etc., seem ripe for a major transformation. It is also well understood that general AI foundation models like GPT4 and Falcon-40B need to be fine-tuned or prompt-tuned for enterprise-specific tasks, and therefore must be fed some curated data that allows for some subset of the parameters to be “adjusted,” or output changed based on new task information given in prompts.

However, training the models is one thing. Enterprise applications today live and die on access to current enterprise data. For example, an e-commerce website might return the status of the orders of a logged-in customer. Or a chat application might process the return of a product. In neither of these cases can anything useful be done without real connectivity to ( integration with) one or more enterprise applications. First, we’ll speak to how an integration layer completes AI applications.

In addition, these integrations do not magically appear. They have to be coded, and they have to be tested and maintained. Later, we’ll speak to how integrations can be done better with the help of AI.

AI Without Integration is Incomplete

How would an AI application return useful information? AI without integration is like fish without water.

Feature Image Credit: Shutterstock. 

By Anant Jhingran and Matt Roberts

Sourced from THENEWSTACK



By Miranda Nazzaro

Media titan Barry Diller confirmed Sunday he and a group of “leading publishers” plan to take legal action regarding the use of published works in training artificial intelligence (AI) systems.

Diller, the chairman and senior executive of internet and media conglomerate IAC, said he thinks generative AI is “overhyped, as all revolutions that are in the very beginning,” in an interview Sunday morning with CBS’s Margaret Brennan on “Face the Nation.”

AI systems are trained and improved using large language models, which ingest compilations of written works like books, news stories and social media posts.

Diller said he and others in the publishing industry don’t agree with how AI systems take in publishers’ content.

“It’s not that either Google or Microsoft, who are the two real leaders of this in terms of, certainly Google with having a monopoly on advertising. They, too, want to find a solution for publishers,” Diller told Brennan. “The problem is they also say that the fair use doctrine of copyright law allows them to suck up all this stuff.”

“It is, it will be, long-term catastrophic if there is not a business model that allows people professionally to produce content,” Diller continued. “That would be, I think everybody agrees is catastrophic.”

Diller claimed legislation or litigation is needed to protect the copyright of publishers.

“Of course, say we’re open to commercial agreements. But on the side of those people who are depending upon advertising, Google, for instance, they say, ‘Yes, we’ll give you a revenue share,’” Diller said. “Right now, the revenue share is zero. So, what percent of zero would you like today? I mean that’s rational, but it’s not the point. The only way you get to the point is protect fair use. In other words, protect the copyright.”

Diller would not disclose or confirm who is he planning to launch litigation with, only calling them “leading publishers.”

“It took 15 years to get back paywalls that protected publishers, I don’t think that same thing is going to happen,” Diller said.

When asked if generative AI poses a threat to Hollywood studio workers’ jobs, Diller said, “In this case, I think the one-to-three-year period, not much is going to happen. But post that, there are, of course, all these issues.”

Diller is not the first to consider legal action over AI publishing. Comedian Sarah Silverman and two other authors are currently suing Meta and OpenAI for alleged copyright infringement, claiming the platforms’ AI systems were “knowingly and secretly trained” with unauthorized copies of their books.

The Associated Press announced last week it would license its archive of news stories to ChatGPT maker OpenAI to help train the AI company’s system.

Feature Image Credit: (AP Photo/Kathy Willens)

By Miranda Nazzaro

Sourced from The Hill