Technology

While ChatGPT stokes fears of mass layoffs, new jobs are being spawned to review AI

Published

1 year ago

October 12, 2023

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The logo of generative AI chatbot ChatGPT, which is owned by Microsoft-backed company OpenAI.

CFOTO | Future Publishing via Getty Images

Artificial intelligence might be driving concerns over people’s job security — but a new wave of jobs are being created that focus solely on reviewing the inputs and outputs of next-generation AI models.

Since Nov. 2022, global business leaders, workers and academics alike have been gripped by fears that the emergence of generative AI will disrupt vast numbers of professional jobs.

Generative AI, which enables AI algorithms to generate humanlike, realistic text and images in response to textual prompts, is trained on vast quantities of data.

It can produce sophisticated prose and even company presentations close to the quality of academically trained individuals.

That has, understandably, generated fears that jobs may be displaced by AI.

Morgan Stanley estimates that as many as 300 million jobs could be taken over by AI, including office and administrative support jobs, legal work, and architecture and engineering, life, physical and social sciences, and financial and business operations.

But the inputs that AI models receive, and the outputs they create, often need to be guided and reviewed by humans — and this is creating some new paid careers and side hustles.

Getting paid to review AI

Prolific, a company that helps connect AI developers with research participants, has had direct involvement in providing people with compensation for reviewing AI-generated material.

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The company pays its candidates sums of money to assess the quality of AI-generated outputs. Prolific recommends developers pay participants at least $12 an hour, while minimum pay is set at $8 an hour.

The human reviewers are guided by Prolific’s customers, which include Meta, Google, the University of Oxford and University College London. They help reviewers through the process, learning about the potentially inaccurate or otherwise harmful material they may come across.

They must provide consent to engage in the research.

One research participant CNBC spoke to said he has used Prolific on a number of occasions to give his verdict on the quality of AI models.

The research participant, who preferred to remain anonymous due to privacy concerns, said that he often had to step in to provide feedback on where the AI model went wrong and needed correcting or amending to ensure it didn’t produce unsavory responses.

He came across a number of instances where certain AI models were producing things that were problematic — on one occasion, the research participant would even be confronted with an AI model trying to convince him to buy drugs.

He was shocked when the AI approached him with this comment — though the purpose of the study was to test the boundaries of this particular AI and provide it with feedback to ensure that it doesn’t cause harm in future.

The new ‘AI workers’

Phelim Bradley, CEO of Prolific, said that there are plenty of new kinds of “AI workers” who are playing a key role in informing the data that goes into AI models like ChatGPT — and what comes out.

As governments assess how to regulate AI, Bradley said that it’s “important that enough focus is given to topics including the fair and ethical treatment of AI workers such as data annotators, the sourcing and transparency of data used to build AI models, as well as the dangers of bias creeping into these systems due to the way in which they are being trained.”

“If we can get the approach right in these areas, it will go a long way to ensuring the best and most ethical foundations for the AI-enabled applications of the future.”

In July, Prolific raised $32 million in funding from investors including Partech and Oxford Science Enterprises.

The likes of Google, Microsoft and Meta have been battling to dominate in generative AI, an emerging field of AI that has involved commercial interest primarily thanks to its frequently floated productivity gains.

However, this has opened a can of worms for regulators and AI ethicists, who are concerned there is a lack of transparency surrounding how these models reach decisions on the content they produce, and that more needs to be done to ensure that AI is serving human interests — not the other way around.

Hume, a company that uses AI to read human emotions from verbal, facial and vocal expressions, uses Prolific to test the quality of its AI models. The company recruits people via Prolific to participate in surveys to tell it whether an AI-generated response was a good response or a bad response.

“Increasingly, the emphasis of researchers in these large companies and labs is shifting towards alignment with human preferences and safety,” Alan Cowen, Hume’s co-founder and CEO, told CNBC.

“There’s more of an emphasize on being able to monitor things in these applications. I think we’re just seeing the very beginning of this technology being released,” he added.

“It makes sense to expect that some of the things that have long been pursued in AI — having personalised tutors and digital assistants; models that can read legal documents and revise them these, are actually coming to fruition.”

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Another role placing humans at the core of AI development is prompt engineers. These are workers who figure out what text-based prompts work best to insert into the generative AI model to achieve the most optimal responses.

According to LinkedIn data released last week, there’s been a rush specifically toward jobs mentioning AI.

Job postings on LinkedIn that mention either AI or generative AI more than doubled globally between July 2021 and July 2023, according to the jobs and networking platform.

Reinforcement learning

Meanwhile, companies are also using AI to automate reviews of regulatory documentation and legal paperwork — but with human oversight.

Firms often have to scan through huge amounts of paperwork to vet potential partners and assess whether or not they can expand into certain territories.

Going through all of this paperwork can be a tedious process which workers don’t necessarily want to take on — so the ability to pass it on to an AI model becomes attractive. But, according to researchers, it still requires a human touch.

Mesh AI, a digital transformation-focused consulting firm, says that human feedback can help AI models learn mistakes they make through trial and error.

“With this approach organizations can automate analysis and tracking of their regulatory commitments,” Michael Chalmers, CEO at Mesh AI, told CNBC via email.

Small and medium-sized enterprises “can shift their focus from mundane document analysis to approving the outputs generated from said AI models and further improving them by applying reinforcement learning from human feedback.”

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Technology

Athenahealth to offer Abridge’s AI scribe to its network of thousands of doctors

Published

3 hours ago

February 25, 2025

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Athenahealth to offer Abridge's AI scribe to its network of thousands of doctors

A doctor looks at an AI-generated clinical note.

Courtesy of Athenahealth

Health-care software vendor Athenahealth on Tuesday said it will offer Abridge’s artificial intelligence scribing tool to its network of more than 160,000 clinicians.

Athenahealth has developed an electronic health record, revenue cycle management tools and patient engagement tools for ambulatory care providers, which include outpatient facilities like independent practices. The company introduced a solution called Ambient Notes in October that allows doctors to choose between various AI-powered documentation tools, and Abridge is the latest addition.

Abridge uses AI to draft clinical notes in real time as doctors consensually record their visits with patients. The startup is part of a red-hot market that has exploded as health-care executives search for solutions to help reduce staff burnout and daunting administrative workloads.

“The market is going to evolve rather rapidly, there are going to be winners and losers over time,” Athenahealth CEO Bob Segert told CNBC. “Different physicians will prefer different ways that notes are taken and that the information is delivered, and we want to be able to provide that flexibility.”

Athenahealth and Abridge declined to share the financial details of the partnership.

Clinicians spend nearly nine hours a week on documentation, according to an October study from Google Cloud. And more than 90% of physicians report feeling burned out on a “regular basis,” according to a survey commissioned by Athenahealth last February.

Companies including Abridge, Microsoft’s Nuance Communications, Suki and others say their AI scribing tools can help. Suki and iScribeHealth already offer their tools through Athenahealth’s Ambient Notes solution.

“It’ll be incumbent upon us to make sure that we’re able to demonstrate differentiation,” Abridge CEO Dr. Shiv Rao told CNBC. “So far, we’ve had good luck these last few years doing that.”

Abridge has deployed its technology across more than 100 health systems in the U.S., including organizations like the Mayo Clinic, Duke Health and Johns Hopkins Medicine.

The company announced a $250 million funding round earlier this month. It also unveiled a new Contextual Reasoning Engine that can pull information that’s relevant to a specific clinician and their clinic’s best practices. Abridge’s Rao said that technology will be available to Athenahealth clinicians.

Athenahealth’s Ambient Notes solution is currently available in a limited capacity, but the company said it plans to widen availability for clinicians through 2025.

“The more they try it, the more they like it, and I think we’re going to see a pretty steep adoption curve as this continues to move forward,” Segert said.

Technology

Nvidia to report earnings amid infrastructure spending, DeepSeek concerns

Published

4 hours ago

February 25, 2025

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Nvidia to report earnings amid infrastructure spending, DeepSeek concerns

Nvidia is scheduled to report fourth-quarter financial results on Wednesday after the bell.

It’s expected to put the finishing touches on one of the most remarkable years from a large company ever. Analysts polled by FactSet expect $38 billion in sales for the quarter ended in January, which would be a 72% increase on an annual basis.

The January quarter will cap off the second fiscal year where Nvidia’s sales more than doubled. It’s a breathtaking streak driven by the fact that Nvidia’s data center graphics processing units, or GPUs, are essential hardware for building and deploying artificial intelligence services like OpenAI’s ChatGPT. In the past two years, Nvidia stock has risen 478%, making it the most valuable U.S. company at times with a market cap over $3 trillion.

But Nvidia’s stock has slowed in recent months as investors question where the chip company can go from here.

It’s trading at the same price as it did last October, and investors are wary of any signs that Nvidia’s most important customers might be tightening their belts after years of big capital expenditures. This is particularly concerning in the wake of recent breakthroughs in AI out of China.

Much of Nvidia’s sales go to a handful of companies building massive server farms, usually to rent out to other companies. These cloud companies are typically called “hyperscalers.” Last February, Nvidia said a single customer accounted for 19% of its total revenue in fiscal 2024.

Morgan Stanley analysts estimated this month that Microsoft will account for nearly 35% of spending in 2025 on Blackwell, Nvidia’s latest AI chip. Google is at 32.2%, Oracle at 7.4% and Amazon at 6.2%.

This is why any sign that Microsoft or its rivals might pull back spending plans can shake Nvidia stock.

Last week, TD Cowen analysts said that they’d learned that Microsoft had canceled leases with private data center operators, slowed its process of negotiating to enter into new leases and adjusted plans to spend on international data centers in favor of U.S. facilities.

The report raised fears about the sustainability of AI infrastructure growth. That could mean less demand for Nvidia’s chips. TD Cowen’s Michael Elias said his team’s finding points to “a potential oversupply position” for Microsoft. Shares of Nvidia fell 4% on Friday.

Microsoft pushed back Monday, saying it still planned to spend $80 billion on infrastructure in 2025.

“While we may strategically pace or adjust our infrastructure in some areas, we will continue to grow strongly in all regions. This allows us to invest and allocate resources to growth areas for our future,” a spokesperson told CNBC.

Over the last month, most of Nvidia’s key customers touted large investments. Alphabet is targeting $75 billion in capital expenditures this year, Meta will spend as much as $65 billion and Amazon is aiming to spend $100 billion.

Analysts say about half of AI infrastructure capital expenditures ends up with Nvidia. Many hyperscalers dabble in AMD’s GPUs and are developing their own AI chips to lessen their dependence on Nvidia, but the company holds the majority of the market for cutting-edge AI chips.

So far, these chips have been used primarily to train cutting-edge AI models, a process that can cost hundreds of millions dollars. After the AI is developed by companies like OpenAI, Google and Anthropic, warehouses full of Nvidia GPUs are required to serve those models to customers. That’s why Nvidia projects its revenue to continue growing.

Another challenge for Nvidia is last month’s emergence of Chinese startup DeepSeek, which released an efficient and “distilled” AI model. It had high enough performance that suggested billions of dollars of Nvidia GPUs aren’t needed to train and use cutting-edge AI. That temporarily sunk Nvidia’s stock, causing the company to lose almost $600 billion in market cap.

Nvidia CEO Jensen Huang will have an opportunity on Wednesday to explain why AI will continue to need even more GPU capacity even after last year’s massive build-out.

Recently, Huang has spoken about the “scaling law,” an observation from OpenAI in 2020 that AI models get better the more data and compute are used when creating them.

Huang said that DeepSeek’s R1 model points to a new wrinkle in the scaling law that Nvidia calls “Test Time Scaling.” Huang has contended that the next major path to AI improvement is by applying more GPUs to the process of deploying AI, or inference. That allows chatbots to “reason,” or generate a lot of data in the process of thinking through a problem.

AI models are trained only a few times to create and fine-tune them. But AI models can be called millions of times per month, so using more compute at inference will require more Nvidia chips deployed to customers.

“The market responded to R1 as in, ‘oh my gosh, AI is finished,’ that AI doesn’t need to do any more computing anymore,” Huang said in a pretaped interview last week. “It’s exactly the opposite.”

Technology

Bitcoin drops to a 3-month low below $90,000 in risk-off move

Published

6 hours ago

February 25, 2025

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Bitcoin drops to a 3-month low below ,000 in risk-off move

A worsening macroeconomic climate and the collapse of industry giants such as FTX and Terra have weighed on bitcoin’s price this year.

STR | Nurphoto via Getty Images

Bitcoin fell through the $90,000 level overnight, weakened by sell pressure in equities as the crypto market awaits its next catalyst.

The price of bitcoin fell 6% to $88,519, according to Coin Metrics. Earlier, it fell as low as $87,736.

The decline puts the blue chip coin almost 20% off its all-time high reached on President Donald Trump’s inauguration day.

“Equities have faced a few difficult sessions over the last week, with top-performing stocks down many times the index, as markets grapple with increased uncertainty under the new administration,” said Steven Lubka, head of private clients and family offices at Swan Bitcoin. “This pressure has spilled over into bitcoin and crypto markets.”

The S&P 500 on Monday posted a three-day losing streak as it failed to recover from last week’s sell-off, driven by concern over a slowing economy and sticky inflation.

“Ultimately, the lack of visible short-term catalysts and pressure from equities creates an environment for profit-taking and pressure from shorts,” Lubka added.

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Bitcoin falls below the key $90,000 level Monday

Bitcoin kicked off the year in rally mode, fueled by optimism about the positive changes the new Trump administration was expected to make for the crypto industry. However, since the President issued his widely anticipated executive order on crypto at the end of January – the contents of which were well received by the industry despite its tamer than hoped for language on a strategic bitcoin reserve – the market has had little to look forward to.

While optimism about the long-term positive impact Trump’s policies could have for crypto remains high, its movements have been and may continue to be dictated by macroeconomic trends.

“From November through January, the market was very enthusiastic about pricing in a crypto-friendly U.S. administration,” said Joel Kruger, market strategist at LMAX Group. “Now it’s a question of waiting for that next catalyst. We know that all of this is in place, and the market is in a bit of a sell-the-fact consolidation sell as it kind of waits.”

The $90,000 level marks the bottom of the narrow range bitcoin has been trading in since the end of November. Analysts have warned that if bitcoin were to meaningfully break below the level, it could see a deeper pullback toward $80,000.

“There is room for bitcoin still to go back down towards the $70,000 to $75,000 area without doing anything to compromise the outlook,” Kruger said, “and we suspect that there will be plenty of demand as we head down towards those levels.”

Lubka said he believes bitcoin will finish digesting this move and resume its long-term move higher by mid-March.

Other cryptocurrencies fared worse on Monday. Ether and Solana’s sol token each tumbled 9%. The broader market of cryptocurrencies, as measured by the CoinDesk 20 index, lost more than 8%.