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• Nidah Barber-Raymond had her first chemical-skin peel in 2009 and was hooked on the process.
• She began making her own peels and left her job in real estate to become a full-time aesthetician.
• After building her brand and creating an at-home peel kit, Barber-Raymond had a six-figure 2020.

This as-told-to essay is based on a conversation with 47-year-old Nidah Barber-Raymond, the owner of The Peel Connection. Barber-Raymond’s income has been verified with documentation by Insider. The following has been edited for length and clarity.

In 2009, I was getting a facial and I felt a light sting on my face. I asked the aesthetician what she was using, and she told me it was acid that would boost my skin’s collagen production. 

I was sold on my first peel. Now, I’m an aesthetician making six figures in revenue a year peeling people’s skin. 

A chemical peel exfoliates the top layer of skin to shed all the dead cells, revealing smoother skin underneath. 

After my first peel in 2009, I became obsessed with the process. I would order chemicals online, mix them, and then apply them to myself before applying them to friends, family, and neighbors. 

I experimented for about a year and would always test the mixes on myself first. I only burnt myself once in this process. My parents stressed to me to do everything by the book so I didn’t harm myself.

I quit my nine-to-five in property management to go all in on skin peels

At the time, I was a property manager, but it didn’t excite me. I left the industry in 2010 to focus on becoming an aesthetician. Using some residual income from the rentals I was managing, I had the financial flexibility to start training. The six-month course to become a licensed aesthetician cost $4,000. 

The moment I got my license, I became a full-time aesthetician focused on chemical peels for the face and body. I took on family, friends, and neighbors as my first clients, and they gave me a lot of referrals. 

I also posted ads on Yahoo’s local directory to help people with extreme cases of hyperpigmentation or acne in exchange for pictures and honest reviews — free of charge. Specializing in peels gave me a good niche to market myself. 

I would approach people on the street and offer them free chemical peels. 

I got mixed responses. Some people agreed, and it worked really well, but for others, I didn’t get such a great reaction. I would see people with acne or hyperpigmentation that I knew one session would help, but the question was: How do you approach that conversation? I had to start holding myself back.

After my maternity leave, I returned to skin peels with a vengeance 

I ran that business for about a year under the name Nidah Skincare until I got pregnant in 2013.  I took a two-year break from 2014 until the end of 2016. I am lucky that my husband is a fairly established real-estate developer and investor, so I was able to take this time off to stay at home with our kids.

Once I returned to work, I rebranded my company to The Peel Connection in January 2017 — my business took off from there. 

I became number one on Google and Yelp for chemical peels in LA, which helped me get a lot of organic clients. I did some standard SEO optimization, and I was only specializing in chemical peels, so all my clients’ reviews prompted Google and Yelp to rank my business as No. 1. I also went from doing three peels a day to seven, which got the word out more and grew my following. 

In 2020, I had a couple of publications come in and record videos of me doing my peels. Both of them wanted me to show feet peels — apparently, they’re very popular, which surprised me!

I pivoted quickly during COVID-19 to at-home peels and hit six figures in annual income

My business was going great, but then COVID hit, and I had to shut my studio. I pivoted quickly to home peels.

By that point, I’d done over 30,000 peels and collected data on each one. I was in a good place to figure out the correct ingredients for a safe at-home peel kit that the clients could apply themselves. 

It helped me scale my business because I was able to sell to people all over the world — rather than just within a 30-mile radius of my studio. I hit six figures in yearly revenue for the first time during COVID.

As restrictions eased, there were still customers who wanted to come into the studio and get me to peel them. And I haven’t lost many clients due to offering at-home peels.

The average face peel costs $225, and the home peels cost $100 for one, $150 for two, and $200 for three.

In the studio, I can offer more uncommon areas to peel, such as what I call the “baby-bump peel,” which is the bum, full-body peels, and peels on more sensitive areas. 

I have one client that flew all the way from Germany to have a full-body peel — and I mean everywhere. It cost $3,000 because he was fairly large, and obviously, the more area I cover, the more it’s going to cost.

Skin peels can be life-changing for people, and it’s so fulfilling to see

My favorite area to peel is the back. I get clients who have had back acne for half of their life and never believed they could fix it. This can be a guy who doesn’t take his shirt off at the pool, or a bride who feels limited on what wedding dress she can buy. I love these peels because it’s really emotional and, sometimes, life-changing for these people. 

All of my clients love the peeling process. 

It feeds into that same feeling those pimple-popping videos do — that feeling of being out with the old, it’s cathartic. You peel and are left with baby-fresh skin. I love doing it to myself!

The best part of the job, though, is seeing results from clients and how shocked they can be. I know it sounds corny, but it really makes me feel like I’m making a difference. 

That’s what I was missing in my previous career. I just didn’t feel fulfilled, and it didn’t light me up the way chemical peels do.

• ChatGPT’s popularity is stirring concerns about the proliferation of AI-generated content.
• Researchers have developed tools to detect machine-made videos or text.
• Simple steps like checking your source are crucial for the average media consumer, one told Insider.

Concerns about artificial intelligence programs taking over jobs or robots going rogue are nothing new. But the debut of ChatGPT and Microsoft’s Bing chatbot has put some of those fears back in the forefront of the general public’s mind — and with good reason.

Professors are catching students cheating with ChatGPT, jobs initially thought to require a human’s judgment may soon be on the chopping block, and — like so many other AI models — tools like ChatGPT are still plagued by bias. 

There’s also the ever-growing threat of misinformation, which can be all the more potent with AI chatbots.

Chelsea Finn, an assistant professor in computer science at Stanford University and a member of Google Brain’s robotics team, sees valid use cases for tools like ChatGPT.

“They’re useful tools for certain things when we ourselves know the right answer, and we’re just trying to use them to speed up our own work or to edit text, for example, that we’ve written,” she told Insider. “There are reasonable uses for them.”

The concern for Finn is when people start to believe everything that is produced by these models and when bad actors use the tools to deliberately sway public perception.

“A lot of the content these tools generate is inaccurate,” Finn said. “The other thing is that these sorts of models could be used by people who don’t have the best intentions and try to deceive people.”

Researchers have already developed some tools to spot AI-generated content and are claiming they have accuracy rates of up to 96%.

The tools will only get better, Finn said, but the onus will be on the public to be constantly mindful of what it sees on the internet.

Here’s what you can do to detect AI-generated content.

AI detection tools exist

There are several tools available to the public that can detect text generated by large language models (LLM) — the more formal name for chatbots like ChatGPT.

OpenAI, which developed ChatGPT, has an AI classifier, that aims to distinguish between human and AI-written text, as well as an older detector demo. One professor who spoke with Insider used the latter tool to determine that a student essay was 99% likely to be AI-generated.

Eric Anthony Mitchell, a computer science graduate student at Stanford, and his colleagues developed a ChatGPT detector aptly called DetectGPT. Finn acted as an advisor for the project. A demo and paper on the tool were released in January

All of these tools are in their early stages, have different approaches to detection, and have their unique limitations, Finn said.

There are essentially two classes of tools, she explained. One relies on collecting large amounts of data — written by people and machine learning models — and then training the tool to distinguish between the text and the AI tool.

The challenge behind this approach is that it relies on a large amount of “representative data,” Finn said. This becomes an issue if, for example, the tool is only given data written in English or data that is mostly written in a colloquial language.

If you were to feed this tool Spanish-language text or a technical text like something from a medical journal, the tool would then struggle to detect AI-generated content.

OpenAI adds the caveat that its classifier is “not fully reliable” on short texts below 1,000 characters and texts written in other languages besides English. 

The second class of tools relies on the large language model’s own prediction of a text being AI-generated or human. It’s almost like asking ChatGPT if a text is AI-generated or not. This is essentially how Mitchell’s DetectGPT operates.

“One of the big upsides to this approach is you don’t have to actually collect the representative dataset, you actually just look at the model’s own predictions,” Finn said.

The limitation is that you need to have access to a representative model, which is not always publicly available, Finn explained. In other words, researchers need access to a model like ChatGPT to be able to run tests where they “ask” the program to detect human or AI-generated text. ChatGPT is not publicly available for researchers to test the model at the moment.

Mitchell and his colleagues report their tool successfully identified large language model-generated text 95% of the time. 

Finn said every tool has its pros and cons but the main question to ask is what type of text is being evaluated. DetectGPT had similar accuracy to the first class of detection tools, but when it came to technical texts, DetectGPT performed better.

Detecting Deepfakes? Human eyes — and veins — provide clues

There are also tools to detect Deepfakes, a portmanteau of “deep-learning” and “fake” that refers to digitally-made images, videos, or audio.

Image forensics is a field that has existed for a long time, Finn said. Since the 19th century, people were able to manipulate images using composites of multiple photos — and then came Photoshop.

Researchers at the University of Buffalo said they’ve developed a tool to detect deepfake images with 94% effectiveness. The tool looks closely at reflections in the eyes of people in the video. If the reflection is different, then it’s a sign that the photo was digitally rendered.

Microsoft announced its own deepfake detector called Microsoft Video Authenticator ahead of the 2020 election with the goal of catching misinformation. The company tested the tool with Project Origin, an initiative that works with a team of media organizations, including BBC and The New York Times, to provide journalists the tools to track the source of origin for videos. According to the tech company, the detector closely examines small imperfections at the edge of a fake image that is undetectable by the human eye.

Last year, Intel announced its “real-time” deepfake detector, FakeCatcher, and said that it has a 96% accuracy rate. The tool is able to look at the “blood flow” of a real human in a video and uses those clues to determine a video’s authenticity, according to the company.

“When our hearts pump blood, our veins change color. These blood flow signals are collected from all over the face and algorithms translate these signals into spatiotemporal maps,” the company wrote in an announcement of its tool. “Then, using deep learning, we can instantly detect whether a video is real or fake.”

Detection tools are an evolving science. As models like ChatGPT or deepfake applications get better, the tools to detect them also have to improve.

“Unlike other problems, this one is constantly changing,” Ragavan Thurairatnam, founder of technology company Dessa, told The New York Times in a story about internet companies’ fight against deepfakes.

Other ways to spot AI-generated content

The effectiveness of detection tools still relies on an individual’s better judgment.

Darren Hick, a Furman University philosophy professor, previously told Insider that he turned to a ChatGPT detector for a student essay only after he noticed that the paper was well written but “made no sense” and was “just flatly wrong.” 

As Finn said, ChatGPT can be helpful when the user already knows the right answer. For average media consumers, the old adage of checking one’s source remains salient.

“I think it’s good to just try not to believe everything you read or see,” Finn said, whether that’s information from a large language model, from a person, or from the internet.

Social media makes media consumption a seamless experience, so it’s important for users to pause for a moment and check the account or outlet from which they’re seeing a piece of news, especially if it’s something sensational or particularly shocking, according to St. Louis’s Washington University’s guide on spotting fake news.  

Viewers should ask themselves if they’re seeing a video or text from a meme page, an entertainment site, an individual’s account, or a news outlet. After seeing a piece of information online and confirming the source, it helps to compare what else is out there on that subject from other reliable sources, according to the university’s guide. 

When it comes to AI-generated videos or images, there are also still visual cues the naked eye can detect. AI has been reported to have issues drawing hands or teeth.

“Usually there are some small artifacts, maybe in people’s eyes, or, if it’s in a video, the way that their mouth is moving looks a little bit unrealistic,” Finn said.

The photo-editing app LensaAI, which also recently became popular with its Magic Avatar feature, had a habit of leaving “ghost signatures” in the corner of its AI-generated portraits. That’s because the tool was trained on pre-existing images, in which artists often left their signatures somewhere on their paintings, ARTnews reported.

“Right now it’s still possible to spot some of these if you’re looking for the right thing,” Finn said. “That said, in the long run, I suspect that these kinds of machine learning models will probably get better, and that may not be a reliable way to detect images and video in the future.”

Good morning, readers. I’m senior reporter Phil Rosen. 

If you missed Jerome Powell’s remarks from his first day on Capitol Hill yesterday, the TLDR is that more rate hikes are coming because the economy’s still running hot.

Here’s how Powell put it: 

“The latest economic data have come in stronger than expected, which suggests that the ultimate level of interest rates is likely to be higher than previously anticipated.”

Let’s get into how markets, strategists, and traders reacted to the hawkish commentary. 

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Brendan Smialowski/Reuters

1. The market response to Powell’s testimony was anything but muted. Major indexes all fell more than 1% following comments from the Fed boss, while the 2-year Treasury note jumped to its highest mark since 2007.

Remember, when the Fed raises interest rates, the move influences a range of loan products and it becomes more expensive for companies and individuals to borrow money, which is supposed to cause less spending and generally tighten conditions in the real economy. 

The idea is to eventually lower inflation — which most recently clocked in at 6.4% — but the more rate hikes we see, the greater the risk of a recession. 

Higher rates also drag on stocks because the higher cost of debt eats into corporate profitability. 

They also have the effect of making other investments a more attractive alternative to stocks.

For reference, the 2-year Treasury is yielding nearly as much as the S&P 500 has returned in 2023, all while being a much less risky bet. 

So in short: stocks sold off, bond yields jumped, and traders eyed greater potential for a bigger rate hike this month. 

“If the totality of the data were to indicate that faster tightening is warranted, we would be prepared to increase the pace of rate hikes,” Powell said.

At the last Federal Open Market Committee decision on February 1, policymakers downshifted to a 25-basis-point hike, which followed a 50-basis-point move in December and three 75-basis-point moves before that.

But Powell’s now opened the door to up-shifting to another 50 basis-point move.

Before he spoke on the Hill, traders had been pricing in about 31% odds for a half-point rate hike in March. 

Those odds doubled on Tuesday, according to CME’s FedWatch Tool. 

“Powell just said the quiet part out loud,” Callie Cox, US analyst for eToro said Tuesday. “The economy is performing impressively well, but that could complicate the Fed’s efforts to bring inflation down.”

And John Lynch, chief investment officer for Comerica Wealth Management, said the comments weren’t surprising but investors should be taking Powell’s “higher-for-longer” warning seriously.  

Did anything about Powell’s comments yesterday surprise you? What will you be watching for in his statement today? Tweet me (@philrosenn) or email me (prosen@insider.com) to let me know. 

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In other news:

Mike Blake/Reuters

2. US stock futures struggle to push higher early Wednesday, after tumbling yesterday thanks to the prospect of more hikes. Powell will be speaking again today, this time before a House committee. Here are the latest market moves.

3. Earnings on deck: Adidas, Admiral Group, and more, all reporting.

4. Leading Wall Street firms and analysts have a lot to say about the AI boom. The nascent industry could be worth more than $15 trillion by 2030, according to leading experts. Here’s how the pros recommend capitalizing on the new tech wave.

5. Citadel’s Ken Griffin made $4.1 billion in 2022. He topped Institutional Investors’ rich list for the first time in six years after his flagship fund surged 38% in the 12-month stretch. His 2022 earnings are the highest ever in the history of the rankings.

6. Oil prices are flashing signs that the US will avoid a recession. Benchmark crude prices are an underrated indicator of a downturn, and right now they suggest reason for optimism, according to DataTrek Research: “The risk of a recession over the near term seems quite low.”

7. Morgan Stanley is warning investors to brace for US stocks to come crashing down once again. The firm’s strategists said the extreme valuations for companies now echo the 2022 bear market — which suggests the “real damage” for this cycle may still be ahead.

8. Dan Lane has interviewed hundreds of real-estate investors. He shared three of his favorite examples from his podcast with 100,000 monthly listeners of how people have used real-estate investing to accomplish their financial goals. 

9. Morgan Stanley recommended a batch of stocks that can protect your portfolio from a coming market downturn. Margin expectations have fallen at the fastest rate since the financial crisis as earnings turn negative — but these 16 tech stocks look poised to rise anyway.

Markets Insider

10. Nvidia has room to climb another 19%, according to Credit Suisse analysts. When it comes to artificial intelligence, no other tech firm comes close, the bank said. Shares of the top chip-maker have soared more than 60% year-to-date.

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Curated by Phil Rosen in New York. Feedback or tips? Tweet @philrosenn or email prosen@insider.com.

Edited by Max Adams (@maxradams) in New York and Hallam Bullock (@hallam_bullock) in London.