We expect our doctors to be demi-gods – flawless, tireless, always right. But they are only human. Increasingly, they are stretched thin, working long hours, under immense pressure, and often with limited resources. Of course, better conditions would help, including more staff and improved systems. But even in the best-funded clinics with the most committed professionals, standards can still fall short; doctors, like the rest of us, are working with stone age minds. Despite years of training, human brains are not optimally equipped for the pace, pressure, and complexity of modern healthcare.

Given that patient care is medicine’s core purpose, the question is who, or what, is best placed to deliver it? AI may still spark suspicion, but research increasingly shows how it could help fix some of the most persistent problems and overlooked failures – from misdiagnosis and error to unequal access to care.

As patients, each of us will face at least one diagnostic error in our lifetimes. In England, conservative estimates suggest that about 5% of primary care visits result in a failure to properly diagnose, putting millions of patients in danger. In the US, diagnostic errors cause death or permanent injury to almost 800,000 people annually. Misdiagnosis is a greater risk if you’re among the one in 10 people worldwide with a rare disease.

Modern medicine prides itself on being scientific, yet doctors don’t always practise what the evidence recommends. Studies show that evidence-based treatments are offered only about half the time to adults in the US. Doctors can also disagree about diagnoses. In a study of more than 12,000 radiology images, reviewers offering second opinions disagreed with the original assessment in about one in three cases – leading to a change in treatment nearly 20% of the time. As the work day wears on, quality slips further: inappropriate antibiotic prescriptions rise, while cancer screening rates fall.

As alarming as this is, there are understandable reasons for these failures – and viewed from another angle, it’s remarkable that doctors get it right as often as they do. The realities of being human – distraction, multitasking, even our body clocks – take a toll. But burnout, depression and cognitive ageing don’t just wear doctors down; they raise the risk of clinical mistakes.

Medical knowledge also moves faster than doctors can keep up. By graduation, half of what medical students learn is already outdated. It takes an average of 17 years for research to reach clinical practice, and with a new biomedical article published every 39 seconds, even skimming the abstracts would take about 22 hours a day. There are more than 7,000 rare diseases, with 250 more identified each year.

In contrast, AI devours medical data at lightning speed, 24/7, with no sleep and no bathroom breaks. Where doctors vary in unwanted ways, AI is consistent. And while these tools make errors too, it would be churlish to deny how impressive the latest models are, with some studies showing they vastly outperform human doctors in clinical reasoning, including for complex medical conditions.

AI’s superpower is spotting patterns humans miss, and these tools are surprisingly good at recognising rare diseases – often better than doctors. For example, in one 2023 study researchers fed 50 clinical cases – including 10 rare conditions – into ChatGPT-4. It was asked to provide diagnoses in the form of ranked suggestions. It solved all of the common cases by the second suggestion, and got 90% of the rare conditions by the eighth – outperforming the human doctors used as comparators. Patients and their families are increasingly recognising these benefits. One child, Alex, saw 17 doctors over three years for chronic pain – none could explain his symptoms. Desperate, his mother turned to ChatGPT, which suggested a rare condition called tethered cord syndrome. Doctors confirmed the diagnosis, and Alex is now receiving proper treatment.

Then there’s the problem of access. Healthcare is upside down. Those most in need – the sickest, poorest, and most marginalised in society – are the ones most likely to be left behind. Packed schedules and poor public transport mean millions miss appointments. Parents and part-time workers, including those with gig economy jobs, often struggle to attend checkups. American Time Use Survey data shows patients sacrifice two hours for a 20-minute doctor’s visit. The problems are often worse for people with disabilities, who are about four times more likely to miss out on care in the UK due to issues with transport, costs and long waiting lists. Compared with men with no disability, disabled women are more than seven times more likely to have unmet needs due to the cost of care or medication.

And yet we rarely question the idea of waiting in line at the doctor’s office in town because it’s simply the way things have always been done. AI could change that. Imagine a doctor in your pocket offering information when and where you need it. Under Labour’s 10-year plan, Wes Streeting, the health secretary, has announced that patients will soon be able to discuss their health concerns with AI via the NHS app. It’s a bold step – and one that could bring quicker, actionable clinical advice for millions.

This will only work for those who can use it, of course. Internet access is improving globally, but there are still serious gaps: 2.5 billion people remain offline. In the UK, 8.5 million people lack basic digital skills, and 3.7 million families fall below the “minimum digital living standard”, meaning they have poor connectivity, outdated devices and limited support. Confidence is a barrier too: 21% of people in the UK say they feel left behind by technology.

At the moment, AI healthcare research almost exclusively fixates on its flaws. Examining the technology’s potential for bias and errors is a crucially important task. But this orientation doesn’t take account of the creaking and sometimes unsafe systems we already rely on. Any fair assessment of AI must be weighed against the realities of what we’ve currently got – a system that too often can be frustrating, out of reach, or just plain wrong.

Charlotte Blease is a health researcher and the author of Dr Bot: Why Doctors Can Fail Us – and How AI Could Save Lives, published by Yale on 9 September.

Further reading

Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again by Eric Topol (Basic Books, £28)

Co-Intelligence: Living and Working with AI by Ethan Mollick (WH Allen, £16.99)

Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell (Pelican, £10.99)

In the 1990s, an elite group of investors worked for a tech-focused hedge fund called Tiger Management, led by the legendary investor Julian Robertson. Not only did Robertson mentor this group of investors, but he would go on to seed many of their future hedge funds as the talented group, referred to as the Tiger Cubs, went on to become great investors in their own right.

Philippe Laffont, the founder of Coatue Management, is part of this group, and is now viewed as one of the great tech investors of the modern era. Coatue Management’s equity holdings were valued at roughly $35 billion at the end of the second quarter. That’s why investors are always paying attention to which stocks Coatue is buying and selling.

In the second quarter, the fund sold its stake in Super Micro Computer (SMCI -5.42%) and piled into another artificial intelligence (AI) giant that generated a total return over 336,000% since its initial public offering.

Super Micro Computer: Beating the shorts so far

AI and tech infrastructure and server maker Super Micro Computer has been a controversial and volatile play for the past year. In August 2024, short-seller Hindenburg Research came out with a major short report alleging potential accounting fraud at the company. The report said that Supermicro rehired executives who had been a part of an accounting scandal at the company in 2018 that involved understating expenses and overstating revenue.

The stock got hit hard after Supermicro announced it would need to delay its annual 2024 filing to assess its internal controls. However, the company would eventually go on to file its 2024 10-K and did not need to restate any of its financial statements, a good sign for investors. Furthermore, management earlier this year also provided strong fiscal 2026 guidance of $40 billion in revenue, way ahead of consensus at the time. Supermicro’s fiscal year ends on June 30 of each year.

In August, shares struggled after the company reported lower-than-expected quarterly results and weaker-than-expected guidance, due to President Donald Trump’s tariffs, which resulted in less working capital in June and “specification changes from a major new customer.” Laffont and Coatue loaded up on the stock some time in the fourth quarter of 2024 and sold in the second quarter of this year, so the fund could have bought the dip after the short report and might have sold over concerns about tariffs, although that’s speculation. Supermicro’s stock is up about 46% this year, so Coatue seems to have timed its trade well.

Supermicro looks real cheap right now for a stock benefiting from the AI boom, trading around 16 times forward earnings. Tariffs are likely to be an ongoing issue but if AI demand remains strong, Supermicro, which supplies servers to the likes of Nvidia, should be a major beneficiary. The stock may remain volatile, but I think investors can take a position in the more speculative part of their portfolio.

Oracle: A longtime tech player benefiting from AI

With a market cap of nearly $664 billion, Oracle (ORCL -5.97%) isn’t part of the “Magnificent Seven,” but it’s another large tech company expected to benefit from the AI capital expenditure boom. Coatue purchased over 3.8 million shares in the second quarter, valued at over $843 million.

The cloud giant offers clients the ability to tap into a number of AI solutions including generative AI and machine learning capabilities that provide automation tools and AI application development, among other services. Similar to Microsoft and Amazon, although not as dominant, Oracle’s position as a cloud provider positions the company well to be a first point of contact for clients looking to add AI capabilities.

In the company’s most recent earnings report for its fourth quarter of fiscal 2025, which ended May 31, Oracle reported results ahead of Wall Street estimates and said that cloud infrastructure revenue sales should increase 70% in fiscal year 2026, after generating 52% growth in fiscal 2025.

Oracle CEO Larry Ellison said the company is particularly well positioned because it has a strong data advantage and has developed one of the most comprehensive databases in the world. “Our applications take all of your application data and make that data available to the most popular AI models,” he said on Oracle’s earnings call for the company’s fiscal fourth quarter of 2025.

If you like ChatGPT, you use ChatGPT. If you like Grok, you use Grok. You use that in the Oracle Cloud. We are the key enabler for enterprises to use their own data and models. No one else is doing that.

Having gone public in 1986, Oracle has been a major tech disruptor for decades. The stock is up over 336,000% since its initial public offering and also up over 41% this year. Trading at 34 times forward earnings, the stock is not necessarily cheap, but given its track record and strong expected growth in cloud infrastructure, Oracle can benefit from AI without being as much in the spotlight as some of the Magnificent Seven names.

TikTok’s US plans are up in the air due to a divest-or-ban law that puts its future in jeopardy. But it’s still offering six-figure salaries to workers this year in key areas like e-commerce and artificial intelligence.

It’s sought to hire data scientists to sharpen its search algorithm, court workers to grow its e-commerce platform TikTok Shop, and bring in machine learning engineers to improve its content feed and recommendations.

The company’s jobs portal lists over 1,800 open roles in the US in cities like Austin, San Jose, Seattle, and New York.

Like other Big Tech firms, work expectations at TikTok and its owner, ByteDance, are demanding. The company runs performance reviews twice a year, and low scorers can be placed on performance-improvement plans or even shown the door. But the opportunity to work at one of the most influential tech companies in the world continues to draw in talent.

Outside e-commerce, TikTok is shaking up areas like music marketing and young people’s news habits. If it can navigate political tides in the US and China, where ByteDance was founded, it will stand alongside YouTube and a few other players in shaping the next phase of media.

“From a career growth standpoint, you have access to huge budgets and big names,” a former staffer said of working at TikTok. “Everyone in the industry wants to talk to you.”

While TikTok and ByteDance don’t disclose salary information publicly (unless required by state law), they do submit pay ranges in federal filings when they look to hire workers from outside the US.

To understand more about the company’s pay rates, Business Insider reviewed thousands of TikTok salary offers for foreign hires at the company, as well as its owner, ByteDance, for the first three quarters of this reporting year that ran through June 30. The results don’t include equity or other benefits that employees often receive in addition to base pay. But they paint a picture of the range of pay a worker might expect in roles like software engineering, data science, or product management.

The foreign-hire data shows a wide range of salaries at the companies. For example, a finance representative could earn $65,000 a year, and a global head of product and design position could fetch a $949,349 annual salary.

Backend software engineers at TikTok could earn between $144,000 and $301,158, based on the salary data, though rates increased beyond that for specialties like trust and safety. Data scientist positions at TikTok were generally offered between $85,821 and $283,629 — or more in specific areas like e-commerce. For TikTok machine learning scientists, the range was between $168,000 and $390,000, while general marketing managers were offered between $85,000 and $430,000.

These salary offers fall in line with pay rates in federal applications at other Big Tech firms. Meta’s first-quarter visa filings revealed it offered data scientists between $122,760 and $270,000, for example. Meanwhile, a staff software engineer at Google could receive between $220,000 and $323,000, according to the company’s first-quarter filings.

Here are the salary ranges TikTok and ByteDance offered for other roles in key business areas, based on recent applications. TikTok and ByteDance did not respond to requests for comment.

E-commerce and TikTok Shop roles

TikTok Shop – Celebrity Team Live Operation Manager: $94,000

TikTok Shop – US Data Analyst – Logistics: $128,000

TikTok Shop – Campaign Strategy Operations Manager: $132,000

TikTok Shop – Category Manager – Health: $135,000

TikTok Shop – Anti-Fraud Ops Program Mgr – Global Selling: $180,000

TikTok Shop – Data Scientist: $218,000 to $304,000

Product Manager, User Growth Customer Lifecycle-TikTok Shop: $220,000

Strategy Manager, E-Commerce: $228,000 to $230,000

Software Engineer – E-commerce Recommendation Infrastructure: $237,000 to $315,207

TikTok Shop – Inventory Placement Strategy Manager: $250,000

TikTok Shop- Compliance Operation: $257,600

Senior Machine Learning Engineer, E-commerce: $320,000

Tech Lead – E-commerce Recommendation Infrastructure: $320,113

Logistics Procurement Lead, TikTok US E-commerce: $350,000

Senior Data Scientist, Content E-commerce: $350,000

Tech Lead, Global E-commerce Governance Platform: $365,000

Global E-commerce Solutions Manager: $480,000

AI and machine learning roles

Software Engineer (AI Platform): $144,000

Research Scientist (TikTok AI Privacy): $188,000

Product Manager GenAI Safety, Trust & Safety: $218,400

Senior Product Designer, Creation (AI Projects): $221,368

Machine Learning Engineer – Computer Vision: $228,960

Software Engineer, Machine Learning Infrastructure: $270,000 to $320,783

Site Reliability Engineer, AI Applications: $276,000

AI Product Manager: $300,010

Product Manager Lead, Emerging Product & AI Safety: $336,000

AI Security Researcher – Security Flow: $340,000

Senior Machine Learning Engineer, TikTok Recommendation: $386,115

Search roles

Search Product Operations – Creator Search Optimization: $110,000

Software Engineer – TikTok Search Business Infrastructure: $154,880 to $214,720

Product Manager, Search Ads: $205,000

Machine Learning Engineer – Search Ads: $229,200 to $354,000

Machine Learning Engineer – TikTok Search: $241,200 to $300,000

Senior Machine Learning Engineer – TikTok Search Business: $268,920

Product Manager – TikTok Search: $287,500

Product Manager, Search Content Ecosystem: $400,000

Leader of Search and Recommendation Product (ByteDance): $540,552

Search Ads Closed-loop Product Manager: $564,000