Where some see artificial intelligence (AI) flattening human creativity, Kaili Meyer (‘17 journalism and mass communication) sees an opportunity to prove that authenticity and individuality are still the heart of communication.

As founder of the sales and web copywriting company Reveal Studio Co., Meyer built her business and later included AI as an extension of her work. She developed tools that train AI to preserve the integrity of an individual’s tone.

Wellness to print

Meyer began her undergraduate studies at Iowa State in kinesiology but realized she had a talent for writing.

“I thought about what I was really good at, and the answer was writing,” Meyer said.

She pivoted to a journalism and mass communication major, with a minor in psychology. Her passion for writing led her to work on student publications in the Greenlee School of Journalism and Communication, where she founded a health and wellness magazine.

That drive to build something from scratch set the tone for her entrepreneurial approach to her business.

Building an AI copywriting company

After graduation, Meyer joined Principal Financial in institutional investing, where she translated complex economic reports into accessible updates for stakeholders. She gained business skills – but her creative energy was missing.

By freelancing on the side for content, copy and magazines, she eventually replaced her salary, left corporate life, and began the process of launching her own company.

Reveal Studio Co started out with direct client interactions, grew to include a template shop, and now includes AI tools.

In 2023, the AI chatbot ChatGPT had its one-year anniversary with over 1.7 billion users. As generative AI went mainstream and pushed into more areas, Meyer was skeptical of the rapidly growing adoption of AI in society. She began to flag AI-written content everywhere and set out to prove that it could never replicate the human voice.

“In doing so, I proved myself wrong,” Meyer said.

As Meyer researched AI, she realized it could be tailored to one’s own persona.

She developed The Complete AI Copy Buddy, a training manual that teaches an AI platform to mimic an individual’s style. By completing a template and submitting it to an AI source, users can acquire anything they need – from content ideas to full pieces such as social posts, emails, web copy and business collateral – all specifically tailored to their audience, brand, and voice.

The launch of the training manual earned $60,000 in two weeks, more than her first year’s corporate salary.

That success propelled Meyer into creating The Sales & Copy Bestie, a custom Generative Pre-trained Transformer (GPT) built from her knowledge in psychology and copywriting. Contractors support her work while she keeps the control and direction.

“If people are going to use AI – which they are – I might as well help them do it better,” she said.

Meyer prioritizes sales psychology, understanding how neuroscience drives decisions and taking that information to form effective and persuasive messages.

“Copy is messaging intended to get somebody to take action,” Meyer explained. “If I don’t understand what makes someone’s brain want to take action, then I can’t write really good copy.”

Meyer’s clients range from educators and creative service providers to lawyers, accountants, and business owners seeking sharper websites, sales pages, or email funnels.

Meyers’ vision of success

Meyer attributes her growth to persistence and a pure mindset.

“I don’t view anything as failure. Everything is just a step closer to where you want to be,” she said.

This year, Meyer plans to balance her entrepreneurial success with her creative side. She is finishing a poetry book, sketching artwork, and outlining her first novel.

“I’ve spent eight years building a really successful business,” Meyer said. “Now I want to build a life outside of work that fulfills me.”

There are plenty of ways to play the artificial intelligence (AI) craze that’s dominating Wall Street these days. The tried-and-true stock is Nvidia (NVDA -2.71%), the designer of the advanced chips that are the tech world’s most popular choices for running large language models, generative AI, and other cutting-edge functions. Nvidia has made a lot of investors richer over the last few years, and has now grown to become the largest publicly traded company in the world, with a market capitalization approaching $4.4 trillion.

But another possible pick for tech sector investors is Intel (INTC -1.52%), which is more of a legacy computing company. Intel has lagged badly in the AI race, particularly with its foundry division, but it could benefit from the recent investment by the U.S. government, which has taken a 10% stake in the company.

Intel stock is up by 20% so far in 2025. Could it be a better AI investment from here than Nvidia?

The market position for Nvidia

Nvidia’s graphics processing units (GPUs) are the industry standard when it comes to providing the types of computing power required to teach AI models and deploy them in real-world applications. Its CUDA parallel computing platform lets developers write code and build applications on Nvidia GPUs. Every GPU is a parallel processor — capable of performing thousands of operations at once. The CUDA platform helps developers take certain types of computationally heavy processes and divide them into small individual threads that can be handled separately and simultaneously by such chips, thus getting more effectiveness out of them. The results are faster processing times and a more efficient use of computing resources.

That’s particularly important because it keeps hyperscalers and other developers locked into the Nvidia platform when they take their projects live — because CUDA can only be run on Nvidia’s chips. Its Hopper GPUs were the gold standard for GPUs, but now it’s selling its new Blackwell architecture chips, which deliver faster performance with lower power consumption. Blackwell sales generated $11 billion for Nvidia in the first quarter they were available — its fiscal 2025 Q4, which ended Jan. 26 — and boomed to $27 billion in the first quarter of its fiscal 2026. Blackwell sales rose another 17% to roughly $31.6 billion in fiscal Q2, which ended July 27. That was about 76% of the company’s data center sales. CEO Jensen Huang described demand for the Blackwell GPUs as “extraordinary.”

The market position for Intel

Intel, meanwhile, is the market leader in the data center central processing unit (CPU) space, but it’s facing serious challenges from rivals Advanced Micro Devices and Arm Holdings. Analysts with Mercury Research and International Data Corporation (IDC) predict that Intel’s market share will slip to 55% this year as AMD’s rises to 36%. Further, they project that Intel’s market share will fall below 50% by 2027, with AMD getting about 40% and Arm getting between 10% and 12% of the market.

Intel has also been attempting to build up its third-party foundry business, but that unit has struggled to find its footing. While Taiwan Semiconductor Manufacturing is still getting the lion’s share of the world’s chip fabrication business, Intel has had trouble landing clients. Management has announced that it’s shelving its plans to build chip foundries in Germany and Poland, and will slow the pace of construction at its foundry project in Ohio.

The company is investing more than $100 billion in its domestic foundry business, with its next plant expected to open this year in Arizona.

“We are also taking the actions needed to build a more financially disciplined foundry,” CEO Lip-Bu Tan said in the fiscal Q2 earnings press release. “It’s going to take time, but we see clear opportunities to enhance our competitive position, improve our profitability and create long-term shareholder value.”

What’s moving Intel stock now

While Intel is in a weaker financial position than Nvidia, some investors are speculating that it could be hitting a bottom — especially now that the U.S. government has taken a stake in the business. The Trump administration announced in August that it would purchase 433.3 million shares of Intel stock, taking a 9.9% stake in the company. The U.S. also gets a five-year warrant for $20 per share to take an additional 5% of shares should Intel not own a majority of its foundry business.

These moves are part of a push by Washington to encourage the development and manufacturing of high-end semiconductors in the U.S.

“As the only semiconductor company that does leading-edge logic R&D and manufacturing in the U.S., Intel is deeply committed to ensuring the world’s most advanced technologies are American made,” Tan said.

There’s still skepticism about Intel

Investors have already baked some high expectations into Intel’s stock price. Its forward price-to-earnings ratio, which a couple of years ago was roughly in line with Nvidia’s, has surged higher since then, and is now approaching 200, while Nvidia trades at a more reasonable 38.

NVDA PE Ratio (Forward) data by YCharts.

Intel’s stock hasn’t traded at levels like this in two decades. “The stock looks incredibly expensive here,” Wayne Kaufman, chief market analyst at Phoenix Financial Services, told Bloomberg. “That kind of multiple is a bet that the government will push Intel so hard on customers that it becomes a winner.”

Most analysts who revisited Intel following the Trump administration announcement reiterated their hold positions, but also are projecting significant downside for the stock. Bernstein’s Stacy Rasgon has a $21 12-month price target on Intel, which would amount to a roughly 12% downside, while TD Cowen’s Joshua Buchalter has a $20 price target.

Intel has had a net loss of $21 billion over its last four reported quarters, and I don’t see a path for the company to turn its finances around abruptly enough to justify its frothy forward P/E. While its still-downtrodden share price might represent a buying opportunity for investors, I think it’s a shaky bet at best considering that Intel is playing catch-up in AI.

Intel’s new government backing gives it a potential tailwind, but Nvidia’s leadership in GPUs, its CUDA platform, and its AI infrastructure make it a safer bet for long-term investors.

Back in the 2000s, the American pharmaceutical firm Wyeth was sued by thousands of women who had developed breast cancer after taking its hormone replacement drugs. Court filings revealed the role of “dozens of ghostwritten reviews and commentaries published in medical journals and supplements being used to promote unproven benefits and downplay harms” related to the drugs.

Wyeth, which was taken over by Pfizer in 2009, had paid a medical communications firm to produce these articles, which were published under the bylines of leading doctors in the field (with their consent). Any medical professionals reading these articles and relying on them for prescription advice would have had no idea that Wyeth was behind them.

The pharmaceutical company insisted that everything written was scientifically accurate and – shockingly – that paying ghostwriters for such services was common in the industry. Pfizer ended up paying out more than US$1 billion (£744 million) in damages over the harms from the drugs.

The articles in question are an excellent example of “resmearch” – bullshit science in the service of corporate interests. While the overwhelming majority of researchers are motivated to uncover the truth and check their findings robustly, resmearch is unconcerned with truth – it seeks only to persuade.

We’ve seen numerous other examples in recent years, such as soft drinks companies and meat producers funding studies that are less likely than independent research to show links between their products and health risks.

A major current worry is that AI tools reduce the costs of producing such evidence to virtually zero. Just a few years ago it took months to produce a single paper. Now a single individual using AI can produce multiple papers that appear valid in a matter of hours.

Already the public health literature is observing a slew of papers that draw on data optimised for use with an AI to report single-factor results. Single-factor results link a single factor to some health outcome, such as finding a link between eating eggs and developing dementia.

These studies lend themselves to specious results. When datasets span thousands of people and hundreds of pieces of information about them, researchers will inevitably find misleading correlations that occur by chance.

A search of leading academic databases Scopus and Pubmed showed that an average of four single-factor studies were published per year between 2014 and 2021. In the first ten months of 2024 alone, a whopping 190 were published.

These weren’t necessarily motivated by corporate interests – some could, for example, be the result of academics looking to publish more material to boost their career prospects. The point is more that with AI facilitating these kinds of studies, they become an added temptation for businesses looking to promote products.

Incidentally, the UK has just given some businesses an additional motivation for producing this material. New government guidance asks baby-food producers to make marketing claims that suggest health benefits only if supported by scientific evidence.

While well-intentioned, it will incentivise firms to find results that their products are healthy. This could increase their demand for the sort of AI-assisted “scientific evidence” that is ever more available.

Fixing the problem

One issue is that research does not always go through peer review prior to informing policy. In 2021, for example, US Supreme Court justice Samuel Alito, in an opinion on the right to carry a gun, cited a briefing paper by a Georgetown academic that presented survey data on gun use.

The academic and gun survey were funded by the Constitutional Defence Fund, which the New York Times describes as a “pro-gun nonprofit”.

Since the survey data are not publicly available and the academic has refused to answer questions about this, it is impossible to know whether his results are resmearch. Still, lawyers have referenced his paper in cases across the US to defend gun interests.

One obvious lesson is that anyone relying on research should be wary of any that has not passed peer review. A less obvious lesson is that we will need to reform peer review as well. There has been much discussion in recent years about the explosion in published research and the extent to which reviewers do their jobs properly.

Over the past decade or so, several groups of researchers have made meaningful progress in identifying procedures that reduce the risk of specious findings in published papers. Advances include getting authors to publish a research plan before doing any work (known as preregistration), then transparently reporting all the research steps taken in a study, and making sure reviewers check this is in order.

Also, for single-factor papers, there’s a recent method called a specification curve analysis that comprehensively tests the robustness of the claimed relationship against alternative ways of slicing the data.

Journal editors in many fields have adopted these proposals, and updated their rules in other ways too. They often now require authors to publish their data, their code and the survey or materials used in experiments (such as questionnaires, stimuli and so on). Authors also have to disclose conflicts of interest and funding sources.

Some journals have gone further, such as requiring, in response to the finding about the use of AI-optimised datasets, authors to cite all other secondary analyses similar to theirs that have been published and to disclose how AI was used in their work.

Some fields have definitely been more reformist than others. Psychology journals have, in my experience, gone further to adopt these processes than have economics journals.

For instance, a recent study applied additional robustness checks to analyses published in the top-tier American Economic Review. This suggested that studies published in the journal systematically overstated the strength of evidence contained within the data.

In general, the current system seems ill-equipped to cope with the deluge of papers that AI will precipitate. Reviewers need to invest time, effort and scrupulous attention checking preregistrations, specification curve analyses, data, code and so on.

This requires a peer-review mechanism that rewards reviewers for the quality of their reviews.

Public trust in science remains high worldwide. That is good for society because the scientific method is an impartial judge that promotes what is true and meaningful over what is popular or profitable.

Yet AI threatens to take us further from that ideal than ever. If science is to maintain its credibility, we urgently need to incentivise meaningful peer review.