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Twitter and Reddit are now charging for their APIs and this looks to be the beginning of a battle to control data - and the impact on AI cannot be underestimated. There is a storm brewing online over the control of data and it will have massive repercussions for AI. While the output of ChatGPT and image generators attract most of the attention, the input data is being almost entirely overlooked. I have tremendous respect for the scientists and coders who have brought AI to life - but the key to Artificial Intelligence and Machine Learning in practice is data. The reason for this is technology tends to proliferate. With so much of the open-source community (admirably) working on AI, the actual software and models are not going to remain the key differentiators in this artificial intelligence arms race. Once the software exists only those with the requisite data will be able to use it. Elon Musk knows this, as do the Reddit moderators . OK, admittedly neither of these groups might be thinking specifically about data for AI models, but both have recently taken drastic steps to protect data they believe is theirs. Technology 101 The foundation of any technology lecture is Junk In, Junk Out. And to paraphrase that somewhat, the value of a piece of software, from a basic database to the most complex AI, depends enormously on the quality of the data you put in. For many years data on the internet, certainly publicly visible data, was deemed to be free to access. You could see every Tweet (in theory) via the Twitter app, so why not give unlimited bulk access to developers via APIs? Until recently this was not an issue. However, as AI has expanded and the value of data has been more acutely noticed. API stands for Application Programming Interface . That might sound technical but in this context consider an API like having a backdoor or bulk access to a platform. For example, you could use a Twitter API to retrieve all posts referencing “AI” in one go, rather than browning the app itself for hours. Twitter Data and APIs You may have seen the headlines about Twitter, now known as X, limiting how much people can use the app. You can read more details here but the gist is that Elon Musk has come to the conclusion that Twitter’s data, in its aggregate form, is a uniquely useful data set. Yes, your Tweet about what you ate for breakfast might be of interest to your followers. However, with bots that scrape Twitter data from everybody, it could be far more useful to see the trends of what thousands of users are eating for breakfast. Or, what politicians they like. This approach has been used to predict elections and trade financial markets and is vital to many highly profitable sentiment analysis tools. Twitter has always allowed other applications to access its data in bulk via APIs at no or de minimis cost. Now, they have jacked up the price. Reddit Moderators and APIs Similarly, the moderators on Reddit have been protesting over recent API price hikes. Reddit has historically allowed third-party apps to access subreddits via APIs at no cost. However, with growing talk of an IPO, Reddit executives are keen to boost revenue. So they have added a significant charge for the use of these APIs, much to the chagrin of the moderators who essentially manage the data. This has forced many apps to shut down including the hugely popular iOS app Apollo. It is worth noting that Reddit moderators are not employees of the company. They are generally motivated by a passion for the subreddits they moderate and many dedicate a huge amount of time for no direct reward. Why Do They Care About Data and the APIs? It is probably just as simple as wanting to own what they create. I doubt very much Reddit moderators would see themselves on the same side as Twitter but in this context there are similarities. Twitter’s quality data only exists because of Twitter’s infrastructure and user base. Twitter carries the cost, so they do not want other for-profit apps or organizations to profit. Reddit’s quality data only exists because of the moderators. Yes, there are some technical costs to hosting Reddit, but the role of moderators is staggering. There are more than one million communities on Reddit and 140,000 active subreddits . Each of these has between one and 25 moderators. It is estimated that these moderators save the company millions each year, but in truth, the benefit is far more than just the dollars saved. Facebook, Youtube, and other social media companies have moderators to maintain legal and policy standards (i.e. no violence). The Reddit moderators ensure subreddits remain on topic and truly cultivate a high level of content. What Next for Twitter and Reddit APIs? Neither Elon Musk nor the Reddit mods are keen for others to unduly profit from, or limit the use of, the data they help curate . (There may well be more complexities but this is at least partly the case.) Many AI-powered sentiment analysis tools have had to switch off their Twitter feeds because the data is simply too expensive. Reddit threads that were historically public are now going private to block the paid-for API data harvesting. Some subreddits have been flagged as NSFW (i.e. an adult content warning) in protest. Reddit is now threatening to remove moderators who do not comply. How far this will go is impossible to say, but I believe we are only in the early stages of the AI data battles. Own The Data, Own The Software These two examples are just the warning shots in what I expect to turn into a full-on war to control data online. As AI progresses and becomes more accessible, the demand for unique data will skyrocket. The advantage of an AI sentiment analysis tool will no longer be in who can access the software, but who controls the data feed. The same goes for facial recognition, election polling, marketing, and countless other fields that have gotten used to leveraging bulk data online. Soon, those who own the data will essentially own the software.

Google issued a spam policy update and it looks like good news for content creators and consumers. AI Content Is Taking Over If you are in the business of creating content you have probably spent most of 2023 either worrying about AI or trying to leverage AI to take your content creation to the next level. If you are just a consumer of content you have probably come across some, or lots, of AI-generated content, from spam posts on Quora to viral pontiff pictures . Regardless, AI-generated content is now everywhere and Google is beginning to take notice. Google Policy Takes Aim at AI There is a lot of talk about how Artificial Intelligence will destroy search engines like Google. Although that might be hyperbolic there is definitely some truth to it. So it should come as no surprise that Google is tackling the issue head-on. In a recent policy update, the search engine behemoth has said AI-generated reviews will be flagged as spam. Automated Content: We don’t allow reviews that are primarily generated by an automated program or artificial intelligence application. If you have identified such content, it should be marked as spam in your feed using the attribute ( via Google ). For the time being the focus is specifically on AI-generated reviews but the writing is on the wall. Google does not value LLM or AI-Generated content. However, this update was not unexpected and ties in neatly with an experiment carried out by Aiifi SEO expert Fergal O’Shea . AI Content Creation SEO Experiment Earlier this year Aiifi published an article entirely written by AI, specifically by ChatGPT. This was intended to be the SEO equivalent of a control group in a scientific experiment. Everything else on Aiifi is human-generated and we wanted to see how this AI in Real Estate article would perform in comparison. After just three months we had a conclusive answer: In August Google de-indexed the article. Google does not give explicit reasons for de-indexing but it feels safe to assume this was related to their policy crack down on AI-generated content. What Does Google Policy Update Mean for Creators? If you are currently running an AI Content Factory then this is bad news - very bad news in fact. But for everyone else this is great. Rather than reward spam, Google will continue to index and promote useful, user-friendly, content that aligns with its E-E-A-T policy . Why is Google Clamping Down on AI? A simple glance at Google’s policies gives us the answer. A policy called E-E-A-T underpins Google’s approach to SEO. In order for content to rank, the author must demonstrate Experience, Expertise, Authoritativeness, and Trustworthiness (E-E-A-T). ChatGPT and LLMs in general have none of these attributes. An LLM has no “experience” or “expertise” using the product being reviewed . Nor, in our experiment, does it have any “experience” or “expertise” in real estate. Furthermore, LLMs are prone to what is often called “hallucinations”. ChatGPT has been shown on countless occasions to generate factually incorrect or nonsensical text. So from Google’s perspective, they are the polar opposite of “Trustworthy” and not deserving of ranking. What Does this Google Update Mean for AI-Generated Content? The takeaway for SEOs and digital marketers is clear. Human-written content that is rich in personal experiences and expertise will beat AI-generated content in the SERPs. This is based on our own experience and experiment here at Aiifi and matches Google's E-E-A-T policy and the August 2023 update to its product review policy. That being said, we are still in the very early stages of LLM and generative AI. You can most certainly still leverage AI content for SEO purposes, but mass-produced LLM output is not a "hack" to rank on Google. The best thing to do is stay up to date on Google and other search engine policy and generate your own content accordingly. AI will have a profound impact on everything we do but do not lose sight of what makes your content valuable - for your readers and for Google.

Much has been written in 2023 about the collapse in Twitter's ad revenue, a fact admitted by new owner Elon Musk himself. Yet, the deeper and more interesting story lies not in dwindling advertising revenue or even in subscription revenue through the much-maligned Twitter Blue. The real reason Musk bought Twitter, a detail often overlooked, is it's a veritable 'oil field' of data. In this article, we will look at why Musk, a recognized figure in the AI space, is less interested in Twitter as a social media and advertising platform and is far more interested in its value as a data source. From clamping down on bots and data scrapers to monetizing API access and incentivizing high-quality content creation by paying users, we'll analyze Musk's actions since his takeover. We'll see that the real reason Musk bought Twitter is not to control the discourse on social media but to control the valuable output of this discourse - data - the new oil of the AI age. Twitter is not a social media company What people are missing is that Elon Musk did not pay $44 billion to buy a social media platform. He paid $44 billion to buy a data source. While Twitter appears to be a social media company to its users, most people miss that to its owners; Twitter's value is as a data source. Let me explain. Free API access revoked & data scrapers sued In February 2023, Twitter announced that free access to its API, which allows external software to access Twitter's tweets and user data automatically, would be revoked. Instead, those who wanted API access to Twitter's data would need to pay. In July, Twitter began limiting the number of tweets users could view daily in response to extremely aggressive data scrapers - programs extracting large volumes of tweets and user data violating the terms of service. The company also sued 4 Texas-based individuals who they claimed illegally scraped data from Twitter, a sign of how seriously they were taking the data scraping issue. Charging for API access and clamping down on data scraping shows the value Musk assigns to Twitter's data. Now that he is controlling the Twitter data supply, the question we need to ask is - will there be a demand for Twitter's data? We need to look to a different part of the Musk empire for the answer. AI models need data. LOTS of data Interest in Artificial Intelligence tools has exploded since ChaptGPT launched in late 2022. Billions of dollars have been invested in AI companies and start-ups, particularly in the Generative AI space. These companies build and deploy complex AI models and algorithms that are essentially data-hungry engines. These AI models require vast amounts of data to create accurate, insightful, and valuable outputs or predictions. Without data to input, the sophisticated frameworks underlying them would be worthless. Elon Musk understands the AI game Musk was a co-founder of OpenAI, the research lab behind ChatGPT. AI is a crucial part of Tesla's, the electric car company Musk runs, efforts to develop fully autonomous, self-driving vehicles. Musk also co-founded Neuralink, a neurotechnology company that uses AI to create brain-computer interfaces. Musk's involvement in AI is deep, and it has spanned several years. He is no AI bandwagoner. Is Twitter data valuable? Twitter is the source if you want real-time data on sentiment, trends and discussions. Users express their opinions and engage in debates, an online town square, making it a rich source of timely insights you cannot get from any other platforms. Marketers can use Twitter data to gauge sentiment about products or brands. Politicians and organizations can assess public opinion on policies and candidates. Investment banks can analyze tweet patterns to predict market trends. In the new AI-driven world, clean and high-quality data from Twitter is a valuable commodity. Bots and spam lead to dirty Twitter data Musk's criticisms of bots and spam before and during his Twitter takeover can be linked to his understanding of AI models' need for "clean" Twitter data. 'Clean data' is accurate, free of irrelevant noise, like bot-generated tweets, and ready for processing by AI models. AI engineers and data scientists often talk about "data cleansing". This involves collecting raw data from sources, like Twitter, and "cleaning it" to remove noise and inaccuracies before it is fed into AI models. Musk's focus on bots and spam makes even more sense in the context of his buying Twitter as a data source rather than as a social media platform. Musk has been encouraging people to add to the conversation In July, Musk announced that Twitter would begin paying verified content creators for the ads displayed in their replies. Soon afterwards, screenshots started appearing from excited Twitter content creators confirming the first payouts they would receive from Twitter. For several reasons, starting a monetary incentive to create content or data makes sense. It encourages users to create more content on Twitter and potentially move their content publishing from other platforms, YouTube & Spotify, for example, to Twitter. It encourages users to create high-quality content that others engage with, as payments are based on ad views in replies. Musk wants users to discuss and debate with each other in replies, as these are quality insights for the Twitter dataset. High-quality content = high-quality data. More interesting content will drive new users to the platform, users who, in turn, can create or engage with content, adding to the conversation on Twitter. Musk has killed off the Twitter brand Musk did not buy Twitter because it was "Twitter". When he dropped the Twitter name and logo in July, branding and advertising agencies went into overdrive about the foolishness of the move and the billions in brand value he wiped out. But this is because they are still thinking of Twitter/X as a social media platform for advertisers to advertise on. Musk is not. Companies stopped advertising. But are they paying Twitter for data? Sure, advertisers left, and Musk no longer receives their advertising revenue. But as mentioned earlier, Twitter data is extremely valuable to marketers. The question is, are any of the advertisers who left Twitter quietly paying Twitter for data? Marketers are thinking about Twitter revenue from the visible ads they see on the platform. They need to shift their perspective and think of income from the data side. If they did, they would see that companies can pay for and use Twitter data discreetly, including via 3rd party apps, without outsiders ever knowing. Firms who stopped paying for "advertising" on Twitter may still be paying Twitter for a far less obvious product - their data. Musk is not playing the conventional social media or advertising game Charging for API access, killing off data scraping, combating bots and spam, and offering monetary incentives to encourage the creation of more content. He even went as far as killing off the Twitter brand. These moves make it clear that Musk did not buy Twitter/X to generate revenue from advertising or subscriptions. His game is different. In a world where data flows like oil, Elon Musk didn't just buy an oil field; he crowed himself a Data Sheikh.

Pickleball is one of the fastest-growing sports in the USA and around the world. The appeal is clear - it is easy to get started but takes years to master and there is always room to improve your game. There are now almost 9 million players in the United States alone, not bad for a sport that was invented in the 1960s . The sport is often described as a combination of tennis, ping-pong and badminton. It’s a great workout, beginners can become competitive almost immediately and it is suitable for players of almost all levels. And now, with the help of Pickleball Vision AI , it is part of the AI revolution. Their patent-pending software uses computer vision and machine learning to turn videos of pickleball games into training material, match reporting and insights. Computer vision is a branch of Artificial Intelligence most commonly known for its use in facial recognition software. It takes video feed or camera inputs and transforms the data into numerical representations that can be more easily processed and analyzed by algorithms. This is then processed via convolutional neural networks ( CNNs ) and Machine Learning algorithms are applied to identify patterns and garner understanding. " Convolutional Neural Networks (CNNs) are to computer vision what Large Language Models (LLMs) are to natural language processing. Just as CNNs extract hierarchical features from images to recognize patterns and objects, LLMs parse through vast amounts of text to understand and generate coherent language." PB Vision appear to be in the early stages of their AI journey, at least in terms of their release schedule . We recommend you follow them on Twitter to stay up to date with them, and maybe pick up a fun new hobby along the way. This software might seem a little more frivolous than the sort we usually cover here at Aiifi but it is a great example of outside-the-box thinking when it comes to Artificial Intelligence applications. Most instances of computer vision we see in the news related to facial recognition and employee monitoring , but here is a company putting that software to use in a much more positive manner. It is also useful to see the application of AI in new areas rather than the simple automation of tasks that humans currently carry out.

As Co-Founder and CEO of Google Deepmind, Demis Hassabis is an AI visionary who has dedicated his professional life to the field. A chess prodigy as a child, he holds a PhD in cognitive neuroscience and worked as an AI video games programmer early in his career. Hassabis co-founded the AI research laboratory DeepMind Technologies in 2010. Google acquired it in 2014, with Hassabis continuing as CEO. The acclaimed 2017 AI documentary film AlphaGo centres on a computer program developed by DeepMind and Hassabis that plays the abstract strategy board game "Go". Given his pivotal role in spearheading Google's AI efforts , Demis Hassabis's quotes on AI are a valuable insight into today's AI landscape. 1 - On his reasons for being involved in AI "I want to understand the big questions, the really big ones that you normally go into philosophy or physics if you’re interested in. I thought building AI would be the fastest route to answer some of those questions." In a 2023 interview with Time , Hassabis articulated how attempting to answer age-old questions about human existence fuels his passion for AI. 2 - On using AI to solve some of humanity's biggest problems "I would actually be very pessimistic about the world if something like AI wasn’t coming down the road." At the 2018 Economist Innovation Summit, Hassabis spoke about how AI could be the quantum technological leap needed to help address pressing global issues like inequality and climate change. 3 - On AI's role in supporting human experts "It is in this collaboration between people and algorithms that incredible scientific progress lies over the next few decades." Writing in the Financial Times in 2017, Hassabis stated his belief that combining the abilities of AI to identify patterns and insights with the expertise of scientists will drive incredible progress over the next few decades. 4 - On the timeline to achieve artificial general intelligence (AGI) "Right now, I would not be surprised if we approached something like AGI or AGI-like in the next decade." Speaking with the Verge in 2023, Hassabis gave his thoughts on the timeline for achieving artificial general intelligence (AGI). 5 - On concerns about AI development "I think there are valid concerns and they should be discussed and debated now, decades before there's anything that's actually of any potential consequence or power that we need to worry about, so we have the answers in place well ahead of time." Speaking in 2015, Hassabis acknowledged legitimate concerns raised around the use of AI by, among others, Professor Stephen Hawking and Elon Musk, an early DeepMind investor. 6 - On the risks of AI being used by bad actors "This technology has such potential for enormous, enormous good, but it’s a dual-use technology. So if bad actors get hold of it, it could be used for bad things." Speaking with the New York Times in 2023, Hassabis discussed signing an open letter on making the risk of extinction from AI a global priority like other risks such as pandemics and nuclear war. 7 - On adapting to the rapid pace of AI change "You look at today, us using all of our smartphones and other devices, and we effortlessly adapt to these new technologies. And this is gonna be another one of those changes like that." In a conversation with CBS in 2023, Hassabis stated his belief that humans are an infinitely adaptable species and that we will adapt to AI as we did to mobile phones and social media. Aiifi's Thoughts on Demis Hassabis's Quotes Demis Hassabis's quotes on the future of AI provide us with a balanced perspective on AI's potential and challenges. Drawing on his lifetime of real-world experience, his thoughts highlight the transformative potential of AI, along with the need for caution in how we apply it. With his ongoing involvement at the forefront of AI development, he will continue to be a source of wisdom over the next few years and decades.

People are wondering about job losses due to AI and it is easy to understand why. There has been a deluge of AI content so far in 2023 - both written by AI and about AI - and a lot of it has focused on job losses. From mass layoffs to doomsday reporting it would seem that we are on the verge of an AI jobs armageddon. But is that really the case? Let's take a look at some AI job loss statistics so far in 2023. AI Job Loss Statistic #1: 800 Million Jobs Will Be Lost To AI by 2030 That is according to one small business research firm and I personally think this is totally incorrect. AI will utterly change how we work and interact with technology. However, artificial intelligence is only the latest in a long line of technologies that were slated as job-destroying, like the tractor, the sewing machine, and the computer, that in fact increased human production. This is most certainly the Worst Case Scenario and I deem it very unlikely. AI Job Loss Statistic #2: 300 Million Jobs Will Be Lost To AI by 2030 This is a similarly pessimistic report from Goldman Sachs . It is a lower number than the 800 million mentioned above but aside from that it is equally unlikely. There is no doubt about the ability of generative AI but some recent high-profile mistakes show we are a long way off AI taking over the workplace. AI Job Loss Statistic #3: 56% of Companies Have Adopted AI Already Over half of firms are using AI already according to Forbes . This one I certainly can believe. In fact, I see this as a positive step. We all have tasks we hate and letting advanced artificial intelligence software proofread long documents or speed up presentation making makes our lives easier. I do not see many farmers yearning for the days of manual plowing or accountants rushing back to paper and ink ledgers. AI Job Loss Statistic #4: 80% of Workers Will Be Impacted by AI This one is from the ever-reliable OpenAI and I tend to agree. They measure this as workers having 10% of their day impacted by artificial intelligence. In all my years of working, I have not come across an employee who would not jump at the chance of trimming their hectic workday by 45 minutes. AI Job Loss Statistic #5: Women Will Be Impacted More Than Men This is a tricky one but I think is going to be the case. Men tend more towards physical jobs than women and this current AI revolution is very much focused on white-collar and office-based work. Throughout history women have shown themselves to be resilient to the changing job market so this is not a reason to despair - but it is certainly something to keep in mind. AI Job Loss Statistic #6: 31% of Workers Are Worried About AI Taking Their Job This one comes from PwC and there is no reason to doubt it. In my own personal experience, at least one in three workers is concerned about AI. Here at Aiifi we are firm believers that learning about AI and adapting to it is the best approach to insulate yourself from any job losses due to AI. AI Job Loss Statistic #7: AI Could Create 97 Million New Jobs by 2025 Now that's more like it. The usually gloomy World Economic Forum is optimistic about the impact of artificial intelligence on the jobs market. Their very detailed report looks at this topic in great detail and one part stands out. They highlight the key skills needed to thrive in this new AI world as analytical thinking, creativity and flexibility. AI Job Loss Statistic #8: Only 122,900 AI Job Losses in 2023 Only 122,900 job losses in 2023 were attributed to artificial intelligence. This is only a small fraction of the widespread layoffs announced globally and shows that things are not as bad as the attention-grabbing headlines suggest. The US and Europe are both experiencing unemployment rates well below recent norms Conclusion From AI Job Loss Statistics It is perfectly understandable that workers are worried about the impact of artificial intelligence on their jobs and livelihoods. Technology has always forced us to change how we work and AI will be no different. However, the reports proclaiming hundreds of millions of job losses are based on a whole lot of assumptions that are not borne out by the reality of 2023 employment. That being said, there is absolutely no doubt that highly repetitive white-collar tasks will disappear, and this will impact different sectors of society unequally. The invention of the tractor impacted mostly rural men and the automated sowing machine impacted mostly urban women. AI will impact office workers, work-from-home freelancers, and anyone whose job is computer-based. F inancial analysts and web designers are among the most exposed to AI job losses according to Euronews for instance. So what can you do next? The obvious answer is to follow us here at Aiifi to learn about AI. Meanwhile, you can read about how to leverage AI (here) or about setting up your own AI automation agency (here) .

This is a question a lot of workers are asking themselves in 2023. From ChatGPT to killer robots the news coverage of AI tends towards the pessimistic and it is understandable that workers are concerned. But which jobs will be impacted by AI first? Will AI Take My Job in 2023? For an unlucky few the answer is yes. In fact, 122,900 workers have already lost their job to artificial intelligence this year. Almost one in three workers are afraid that AI will take their job and it is easy to see why. The news coverage is predominantly negative and some reports are quoting job loss numbers into the hundreds of millions. However, if you have survived this far then you will probably survive the rest of the year. Will AI Take My Job Soon? That really depends on your industry and the type of work you do . If your role is in any way physical then you are safe. Robotics is a fascinating area but the advancements are slow (relatively speaking) and the technology is expensive. Plumbers, electricians, farmers, nurses, bricklayers and anyone else who is in need of a good sit down at the end of their work day is safe. The main exception here is warehouse workers as this is one area where robotics already exist. However, if your job requires nothing physical beyond the occasional trip to the printer then you are potentially at risk. The more repetitive and monotonous the job, the more likely it is AI will replace it. Jobs that require creativity, decision-making, or responsibility are much safer. A recent report in Euronews highlighted mathematicians, accountants and legal secretaries as jobs that were 100% exposed to AI. These are by no means low-skill jobs, quite the contrary in fact. However, they are repetitive, fact-based jobs that often rely on rules rather than creativity or judgment. Remember, the more your job can be automated, the bigger the risk of it being taken over by AI. The reality is many companies will be targeting an aggressive reduction in headcount in these roles by 2030. That does not mean they will achieve it, but in this high-cost, high-inflation environment companies focus on cutbacks. The first wave of these job losses will come via automation. AI Job Automation As we discussed in our AI Job Loss Statistics post, AI and automation will impact all of us. Some jobs and some demographics may bit hit sooner but the AI automation drive is well and truly underway. While some jobs will be "automated away", for example, legal secretaries according to Euronews, others will remain but change massively. Sales jobs are one area that illustrates this AI impact from extreme automation. Sales jobs will never go away but through the use of generative AI, the industry could look very different in the near future. Where Will AI Take Jobs There is a general consensus that AI will impact the world's wealthier countries the most . This makes sense. The economies of wealthier nations are far more services based and already rely on technology more and that makes them far more susceptible to AI automation. There is also an economic factor. Companies are generally motivated by profits and reducing headcount in expensive regions generates more financial save than reducing headcount in low-cost regions. This might sound blunt but it is true, and the recent wave of job losses illustrates it. Will AI Take My Software Engineer Job For the most part, unfortunately, the answer looks like yes. OpenAI reckons software engineering jobs might vanish entirely. Personally, I am not so pessimistic but there is absolutely no doubt that AI will drastically change software engineering as a career and industry. This example from ChatGPT shows the ease at which generative AI can produce useful C++ code. Will AI Take My Graphic Designer Job Graphic design is another industry expected to suffer at the hands of artificial intelligence. Apps like Leonardo and Midjourney have gone viral in 2023 and it is easy to see why. Visual content is naturally engaging and text-to-image AI generators have caught the public's attention. The question is what will this do for job prospects for graphic designers? It is probably too soon to say, but my expectation is there will be far fewer people employed in this area by 2030 than there are today, and freelancers will be the worst hit. How To Prevent AI from Taking Your Job There are a few ways to prepare yourself against AI automation and AI job losses. The first thing to do is understand how vulnerable your particular job is to automation. Is your job white-collar or more physical in nature? Is it repetitive or creative? The other key thing to focus on is your own skill set. If, like many officer-based workers, your skillset has developed into the smooth execution of repetitive tasks it might be time to up-skill . A more drastic approach might be to consider a new line of work entirely. Of course, you can always follow Aiifi to stay up to date with all the latest AI news.

Of all the industries Artificial Intelligence (AI) has been transforming recently, podcasting stands out. While AI tools like ChatGPT and Synthesia have garnered much press attention, several AI tools have been making waves in the world of podcasting. These tools use AI to solve common podcast production pain points and problems. They have transformed podcast editing and production for established podcasters and have made podcasting far more accessible to newcomers. So how exactly is AI solving podcast production ? Rather than doubling as a sound engineer or video editor, AI lets you focus on the most vital part of podcasting - storytelling. Everyone has their own unique story to tell. Read on to see seven ways AI will help you to tell yours. 1. AI Speech-to-Text: Automatic Podcast Transcription No need to manually transcribe your audio or video files anymore. AI can automatically transcribe your recordings in seconds using advanced speech-to-text technology. Riverside, a leading AI podcasting tool , advertises " AI transcriptions with 99% accuracy " as part of its offering. A quick review of your AI-generated transcript to catch the 1% that was missed, and you are ready to go with a fully accurate transcript. 2. One-Click Removal of Filler Words like "Ums" and "Ahs" Research proves that people who speak without using filler words sound more persuasive and better educated. Thanks to AI, you can automatically remove filler words from your recordings. You and your guests sound more polished, while listeners have a better listening experience with filler words cut out. AI podcasting tool Descript lets you " purge your recordings of "ums," "ahs", "you knows", and a dozen other filler words with a click ". 3. Eliminate Audio Echo, Background Noise, and Auto-Level Sound Good quality audio is critical to a successful podcast. Listeners will switch off if your audio has background noise, an echo, or static. AI acts like your personal sound engineer, isolating speaker voices and regenerating and enhancing their audio quality while eliminating echo and background noise. Podcastle, an AI podcast editing tool , states its users have " no need to worry about recording in a noisy environment or using fancy noise cancelling equipment. AI-powered noise cancellation delivers clean, crisp audio to help you sound like you're in a professional studio. " 4 . Edit Audio and Video Using Text, Bypassing Complex Editors Audio and video editors can be horribly complex and intimidating, especially for beginners. AI has turned post-production podcast editing on its head. Now, you can edit your audio and video by editing your AI-generated text transcript, document style. Adobe is leading the way here with their AI-Powered Adobe Podcast tool - " Adobe Podcast Studio transcribes every word using the same industry-leading transcription as Adobe Premiere Pro. Simply cut, copy, and paste your audio just like a text document. Editing audio has never been easier. " 5. AI Voice Cloning: Easily Edit Words or Phrases in Podcasts AI text-to-speech tools use generative AI to clone your voice. Instead of manually re-recording individual words or sentences, you type what you want to be said. The AI model of your voice will say the words or sentences and match the tone of the surrounding content. Listeners will have no idea the new words were not part of the original recording. Descript's Overdub " makes correcting your recordings as simple as typing. Type any words that your audio or video tracks are missing. Make mid-sentence changes to real recordings – Overdub will match the tonal characteristics on both sides ". 6. AI-Powered Selection of Key Moments for Short-Form Clips Engaging, short-form clips for TikTok, Instagram Reels or YouTube Shorts are essential to hook people into watching entire episodes of your podcast. Creating them used to involve carefully watching your finalized recording to find key moments and then editing them into short clips. AI tools like Opus Clip automate this process " Our AI analyzes your video to identify the most compelling hooks, extracts relevant juicy highlights from different parts of your video, and seamlessly rearranges them into cohesive viral short videos. " These tools can automatically add customizable subtitles, emojis and sounds to increase engagement and build excitement for your podcast. 7. Leverage AI to Repurpose Your Podcast into Other Content Forms Why not take your podcast transcript and get AI to turn it into a blog post with an AI writing tool like WriteSonic ? Or use an AI marketing tool like Jasper to turn your transcript into captivating social media posts. Or use Opus Clip, mentioned above, to turn your long-form video into viral short-form clips. With AI, your podcast is no longer a single piece of long-form audio or video. It can be repurposed into other content types automatically, enabling you to extract total value for yourself and your brand from each and every episode. In recent years, rapid advancements in artificial intelligence have reshaped numerous sectors. Podcasters have been massive beneficiaries of this transformation. And the good news is the momentum shows no signs of stopping. AI podcasting tools continue to add new features, further alleviating the pain of podcast production. If you have a story to tell, there is no better time to launch your podcast.

There are a lot of questions surrounding AI Automation Agencies (AAA) and whether they are a legitimate business model. Several YouTubers began posting videos on "starting your own AI Automation Agency" over the summer. The volume of videos on the topic has grown exponentially, and many of these videos are of questionable quality. Certain YouTubers are merely posting videos about AAAs to get views rather than provide valuable insight. Despite the low quality of content, I have written previously about the overall idea behind AI Automation Agencies being sound. If you ignore the noise and the dubious online claims, there is a legitimate business model there if you target the correct niche . In this post, I will fact-check four claims made about AI Automation Agencies to see if they are legit. By clearing these points up, we can make an informed answer to the question, is AI Automation Agency Legit? Claim 1 - You can start an AI Automation Agency with $0 While technically accurate, the reality in the real business world is you need to spend money to make money with an AAA. The AI tools you will use in your agency all cost money. Charges for using AI tools are monthly, annual or per-usage basis. Before approaching potential clients, you must test and get familiar with these AI tools. Therefore, you must pay to get started and use these tools. Some tools do offer free trials or freemium versions. However, if you are serious about starting a business, you must create samples/prototypes to show potential clients what you can do for them. Solely depending on free trials or freemium versions is not a sustainable business strategy. You must also pay for basic business necessities, like a website and branded email address. You could use a free website builder and a Gmail or Outlook mail address. But these look cheap and unprofessional. Businesses will trust agencies that invest in a professional online presence. In business, as in life, first impressions matter. If you want to be taken seriously, you need to look serious. So the claim you can start an AAA with $0 is not legit in the real world. Claim 2 - AI Automation Agency is a brand-new business model YouTubers and content creators are using creative licence here. The term "AI Automation Agency" is new. It first started appearing online over the summer of 2023. The idea of companies/agencies automating tasks and processes for other companies using AI is not new. Large consulting firms like McKinsey , Deloitte and PWC have offered AI automation services for years. AI Automation Agencies are different because they are not targeting the large firms that McKinsey, Deloitte and PWC are. AAAs will target small and medium-sized businesses instead. These businesses are too small to be attractive to the large consulting firms. So an AAA can slide in and offer AI automation services to these companies instead. So while the AAA terminology and online buzz suggest a novel concept, in reality, it's a repackaging of an existing idea. They are scaled-down, focused versions of businesses that are already operating. This does not mean there is no opportunity to target small and medium-sized companies; I absolutely believe there is. However, it's essential to debunk the notion that an AI Automation Agency is an entirely novel concept. Claim 3: You can start an AI Automation Agency with zero experience No certification or qualification is needed to set up an AI Automation Agency. It is different from becoming a doctor or lawyer, where you need to pass exams to get credentialed. However, you need to know about AI, how AI is applied to automate business processes and how the AI tools used to automate business processes work. If you're unfamiliar with terms like machine learning , natural language processing, large language models or training data, then you don't understand the basics of what an AI Automation Agency does. Understanding the fundamentals of AI is a prerequisite to running a business that sells AI services. Before you go and pitch a basic service like an AI-powered customer service chatbot to a potential client, you need to understand how these chatbots work. Because you can be guaranteed, the client will ask you how they work. No one will use an AI Automation Agency if the owner does not understand the basics of the service he claims to offer. Clients want expertise, and they will only hire an agency whose owner is well-versed in the fundamentals of their services. So the claim you can start with zero experience is not legit. You need to understand AI, AI tools and what specific problems and pain points AI tools can solve for a business. If you already have these, then great, you have experience. If you do not have this experience, you need to learn about AI, start using the tools, and automate some of your own tasks to test the business model and see if this is really for you. Claim 4: You can hire a person or team to do the technical part If you have the thousands or tens of thousands of dollars, it would cost to pay a person or team to do the technical part; this claim may be valid. For 99% of people, this claim is not financially feasible. Most people will not have the cash to invest in hiring people from day one. The only realistic scenario for this to happen is to team up with technically-minded people to co-found your agency. For instance, combining forces—a marketer and a software developer—offers an agency a balanced foundation. In that case, it makes sense that the marketer leaves the technical work to the developer. Relying on freelancers from platforms like Fiverr or Upwork without a clear understanding of AI is also a poor approach. You will be talking with potential clients, looking at their processes and determining what you can automate. You need to understand how AI automation tools work to determine what you can do for clients. You only have a business if you can figure this out. I hope that by addressing these four claims, I have helped clarify whether AI Automation Agencies are legit. For the right people, there is a great opportunity out there. However, the idea that someone can jump in with $0, zero experience and no intention of getting involved on the technical side is erroneous. AI Automation Agencies are legit and represent a valid and promising business model. It is essential to differentiate between the accurate information written about them and misleading content.

In previous articles, we looked at what an AI Automation Agency (AAA) is, if AI Automation Agencies are a scam and the types of niches AI Automation Agencies target . However, we have yet to delve into the particulars of the AI Automation Agency business model. How exactly does an AAA make money? What does it sell, and how does it sell it? This article will be handy for budding AAA entrepreneurs and those curious about the new world of AI agencies. A business model is a company's plan for generating revenues and profit in a specific market. This article will examine how AI Automation Agencies are structured, their target market, their services, and how they price their services. We will also look at the expenses an AI Automation Agency will face and how they look to turn a profit on the revenue they earn. It is important to note that no one-size-fits-all business model exists. AI Automation Agencies are a new concept in online business. What we outline here is the general approach owners are currently taking. Some may do things differently, and that is ok. No AI Automation Agency rulebook exists that must be adhered to. A smart company will regularly review and update its business model to anticipate trends and challenges ahead. In a space as fast evolving as AI, AI Automation Agencies would be wise to do likewise. Structure of an AI Automation Agency The first piece in the AI Automation Agency business model is the setup and structure of an AAA. Most AAAs adopt one of three structures: 1. Sole trader A single person sets up an AAA. They do all the AAA's work, including product development, marketing, client onboarding, and the technical part of AI automation delivery. 2. Partnership Two or more founders join together to create an AAA. Often the founders will have complementary skills so that each can focus on a specific aspect of the AAA's business. For example, a marketer and software developer may join forces to create an AAA. The marketer is in charge of identifying potential clients and selling the AAA to the potential clients. The developer oversees service delivery and the technical side of using AI tools to automate tasks and processes. 3. Agency An owner, or owners, founds an agency and hires employees or freelancers to work in the agency. Different teams or departments in the agency will have different roles and responsibilities. An AAA might start as a solo venture or partnership before evolving into a fully-fledged agency as demand for its services grows. AI Automation Agency Value Proposition The "value proposition" is a core component of any business model. A value proposition describes the products/services an AI Automaton Agency offers and why they are valuable to potential clients. An AAA must also explain why its product or service is different and better than its competitors. There are two items an AAA must define for its value proposition: 1. Define a specific problem the AAA will solve There have to be problems or pain points companies have that the AAA can identify and define so that they can use AI to solve these problems. An AAA could specialise in a single issue or solve multiple problems. The key is that these problems must exist and be painful enough that a business owner is willing to pay to solve them. 2. Define the Target Market Agency owners must define a specific target market . The agency's AI product/service must solve the target customers' problems. Many AAAs target businesses with the following characteristics: Small and medium-sized (potentially sole traders too) Have issues/pain points the AAA can identify, define and solve with AI Do not have AI expertise in-house to solve the problems/pain points Are open to hiring and paying outside agencies for their expertise Products and Services an AAA Sells Once an AAA has a target market and identifies a problem/pain point to solve, they can finalise the products and services they will offer. The product portfolio of an AAA largely depends on market needs. Below are four examples of services AAAs are currently offering. Note that this list is merely indicative of services AAAs can offer. The range of services an AAA can offer is vast. Over 200 AI tools in 30 different categories are currently listed on Aiifi. An AI Automation Agency's services will only be limited by the imagination and business nous of its owner: 1. AI Content Generation Models Content in this context means marketing material, social media posts, product descriptions etc. With AI, you can train a content generation model on a company's existing content. The new content the model generates will then match the company's style and tone. Jasper is an example of an off-the-shelf AI marketing tool companies use to create new content. AAAs can use off-the-shelf tools or build more complex proprietary models for clients using other available AI tools. 2. AI Business/Data Analysis AAAs can offer AI-powered tools that provide new insights to help businesses in their decision-making processes. Many companies generate vast amounts of data every day. Before AI, they could not utilise or analyse this data meaningfully. An AAA can create a model to ingest and analyse company data automatically. This analysis can identify trends, patterns and anomalies to help businesses make informed decisions. 3. Internal & External AI Chatbots AI Chatbots can be integrated with a client's own data that is stored in their existing data/knowledge bases. Employees/clients can then query the chatbot to get answers to questions where the stored data/knowledge contains the answer to that question. Chatbots are used for many reasons, such as customer service, internal training and HR queries. 4. AI Integrations and API Flows AI automation tools like Zapier and Make, for instance, help different software applications communicate with each other. AAAs can automate workflows between various applications so that humans do not need to transfer data between the systems manually. Doing this saves time and increases accuracy as it minimises the chances of human error during the data transfer. Pricing AAA Products and Services Fee-for-service model An AAA will charge a set fee for an agreed service. The price can be a one-off, hourly/weekly monthly rate, or a client can engage the AAA on a retainer basis. Consider an example where an AAA is building an internal chatbot for a 15-person legal firm: One-off fee The AAA and the legal firm agree on an amount for the AAA to deliver the completed chatbot. This amount is agreed upon before the AAA starts to build the chatbot. Whether it takes the AAA 10 or 100 hours to build the chatbot, the amount they receive will remain the same. Hourly/weekly/monthly rate The AAA and the legal firm agree on an amount the AAA will be paid for a period of time worked. Again, the amount is agreed between both parties upfront. In this case, the AAA will get ten times the revenue if the chatbot takes them 100 hours instead of 10 hours. However, it would be difficult for the legal firm to measure how productive the AAA is during the hours they work. This method is more suited to short projects or AI automation consultancy work you may do for a client. Retainer basis Regarding an AAA, working on retainer means you agree to a scope of work or deliverables with a client in advance, and they pay you a certain amount each week or month. In our law firm chatbot example, the AAA agrees to work on retainer and is paid a monthly fee by the law firm. The AAA agrees to monitor the chatbot to ensure it is operational, ensures new data is feeding into it, and fixes any problems that arise with the chatbot directly with the chatbot service providers. AI Automation Agency Expenses One benefit of the AI Automation Agency business model is that starting with very little upfront investment is possible. Start-up costs are minimal compared to starting a franchise or brick-and-mortar retailer. You can also manage and minimise ongoing costs. Such a model enables entrepreneurs to launch an AAA with minimal initial investment. It is important to note that you will require some cash to start, and you need to identify where this cash will come from - personal savings, a loan, an investment etc. You will also need to budget how long the money will allow you to fund your AI Automation Agency, as you may not have revenue coming in the door for some time at the start. As AAAs grow, other expenses inevitably emerge. The below costs are vital for an AAA owner to consider from the very start: Building and hosting a website for your AAA A professional-looking website is essential for an AAA. You can build your website with a service like WordPress or Wix to keep costs low. The website can be as simple as a homepage, an about us page, a services page and a contact us page. Ideally, you will have articles/videos showing some of the work your agency has done for marketing purposes. Business email address with your AAA website domain Whatever website domain you use, you need a business email with that domain. If your website is: www.aiifi.ai, your email needs to be: sales @ aiifi.ai, support @ aiifi.ai or similar. A @gmail or @outlook address will do, but any serious business will have a business email address. Cost of using AI tools to train and learn how they work You must familiarise yourself with the AI tools you expect your AAA to use before you start soliciting clients. Ideally, you will use the tools for your own business/side project or friends and family first. You learn how the tools work and can now create articles/videos/prototypes to show potential clients. If you have a prospect interested in a chatbot and you can show them a working example of a chatbot you built that they can test, it is an invaluable marketing tool. Cost of using AI tools to deliver your AAA services Your most significant cost will be the cost of the AI tools you use to deliver your services. For some tools, you need to decide who pays for the tools and whose name the tool accounts are in. Either the AAA sets up the account in their name and pays for the tools, or the account is set up in the client's name, and the client is charged directly for the tool usage. Remember that some tools charge on a per-usage basis, so it is vital to monitor usage levels to ensure an unexpected bill does not suddenly arrive. Concluding AI Automation Agency Business Models The AI Automation Agency (AAA) landscape is rapidly evolving. For those interested in AAAs, a clear grasp of the AI Automation Agency business model is essential. Understanding their structure, value proposition, service offering, pricing models, and expenses provides an excellent foundation for anyone considering starting or hiring an AAA. As highlighted, smart businesses will review and update their business model periodically. I expect AAAs to do the same and look forward to the evolution of this new type of agency.