Startup companies are founded every day with two key ambitions. One is that their services will improve society (impact); the other is to make a lot of money (incentive). These two drivers can be in competition or collaboration with each other but are never completely divorced.

Fundamentally, new projects need money to get off the ground. Biotech companies in particular require millions of dollars for development before they see even a cent of revenue. This crucial period of investment and development is lovingly referred to as “the valley of death” and even the cure for cancer would need to make it across (1). To gain the necessary capital, companies need to convince venture capital investors that they will become profitable because their technology will be successful, tap a new market, or outperform competitors.

Biotech is not alone is these struggles. Publicly traded companies and garage run startups make the same promise that they will grow and that you, the investor, stand to gain money. However, we live in a competitive world. To distinguish themselves and garner interest new companies make grand promises, but at the same time lose sight of the problem they claim to solve.

AI + “your problem here” = Revenue Growth

The convergence of artificial intelligence (AI) and “insert any topic here” is ubiquitous is every market. For now we will focus on AI and music as a core example of promises made to solve a problem and promises to earn money. AI generated music has been gaining notoriety. CEOs of these companies claim that the benefit of AI generated music is that it lowers the barriers to entry (2). The problem: music is important, but time consuming to learn the fundamentals. The solution: AI music generation tools require less effort for those without advanced training, time to practice, or access to instruments. So, the promise to investors is that the technology would be helping more people access music AND you’ll make some money since everyone will want to use it. Is AI generated music truly the only solution to this problem?

A more direct solution would be to increase the accessibility to music education in schools. If music was a fundamental part of everyone’s upbringing no one would even need these AI tools. Unfortunately, increasing accessibility to music education in schools is not a promising investment but a philanthropic effort instead. That is unless all education systems are privatized and geared towards profit. Not only is this unlikely, but it would completely displace the desired impact of education systems… to educate.

An Ethical Road to Problem Solving

This is where the goal of entrepreneurial impact conflicts with financial incentives. Many people would agree that music should be accessible to everyone. However, solving the core of the problem by addressing music education does not stand to make money. Other societal problems like climate change and accessibility to healthcare face similar dilemmas. Solutions with the potential for greater impact need to find a way to sell themselves and the original goal becomes secondary. On the other hand, technologies which aim to make money first can also use these societal problems as coincidental selling points. How can we distinguish efforts that aim to solve a problem first from those that are a money making operation? Does the difference ultimately matter if progress is being made?

I don’t believe that there is a clear answer to these questions. New companies should weigh impact and incentive carefully when looking to establish themselves. The most important aspect of successful new technology is that it meets a need of customers, not just a desire. Generating a societal impact is not the main goal of a business strategy, making money is. This should not be seen a discouragement of new technology. Instead, this mindset should be inherent in addressing societal concerns. This would result in a marriage of the two entrepreneurial ambitions mentioned, rapidly progressing society as a whole.

Adapt or… Don’t - The evolution of AI

The release of ChatGPT from OpenAI in 2022 brought the capabilities of large language models into the spotlight. Now we have OpenAI, Anthropic, Meta, Google, and xAI pushing the massive scale up of infrastructure to support development. Variations of AI tools are now ubiquitous, and thoroughly part of our culture. If you use AI, you’ve probably found yourself in a conversation trying to determine the exact benefit or doom AI will bring us. The Fall of 2025 was marked by the forecast of an AI bubble, which seems like it hasn’t popped yet. AI is certainly here to stay, but how will it be embraced and by who?

In academics, AI has started an evolution of how basic research is done. Some investigators fully endorsing its use, while others can’t be bothered to take the time to learn a new tool. Overall, it appears that the true early adopters are undergraduate students, who are significantly outpacing researchers in terms of AI usage. And for good reason, it is the greatest essay cheating tool ever invented!

But really, the explosion of AI tools targeted at students is amazing. I’m happy to have experienced teaching and being a student both before and after AI’s introduction. The major change has been in accessibility, in a recent interview, Nobel Laureate Geoffrey Hinton explained that the theory and mathematics necessary for machine learning were already available by the 1980s. However, we lacked the computational power to make it a reality. Hinton’s 1986 Nature paper1 is credited with reviving the field, inspiring “deep” learning, and computer vision. Though the real advances came with major computational increases in the 2010s, allowing for the explosion of AI in the way we know it now.

Side note, Dartmouth is now recognized as the birthplace of AI in 1956 when they hosted an AI focused summer workshop. This is where diverse research on computer simulation of intelligence coalesced into a single field of AI.2

For a long time, functional AI has been hypothesized or predicted, but it took nearly 60 years to become what it is today. Here the system of science has worked, generations have built upon previous understanding with new tools to create better solutions. What this means, is that really, ChatGPT is nothing new. We’ve long had the math to make it real, and finally the infrastructure can power it. The downside is that the rush en masse to embrace AI has led to poor science and marketing hype. Slapping the “AI Inside” sticker on your product has a certain feeling reminiscent of the old “Intel Inside” sticker.

What does your AI start up do?

Now every new company must incorporate AI in some way to stay relevant. Many recent pitches I’ve seen have some statement of “we’re using AI models for xyz purpose”. This leads me to the two classifications of AI businesses that are likely to exist: vibe coding and vibe analysis.

First is the vibe coding company, which uses AI tools to enhance production. Just like a student writing an essay, things get done faster with a relative expert (the AI) guiding the way. I don’t really have an issue with this because it is unavoidable, efficiency improvements to get more work done will always be adopted. Though in the near term, it’s not great for the future of jobs for skilled and educated workers who thought they were insulated from technological redundancy.

This does make me wonder what will happen when product developers can clock out at 10am after sending out their AI prompts for the day. We must be close to seeing “Prompt Engineer” job postings.

Second, we have the AI science implementation company. This is where the core product itself is “AI” doing ~things~, but the company can’t tell you what that really means. In this usage of AI, the data processing is sent off to a server where it’s manipulated and returned with some added valuable insight. Is it useful? Yes, but it’s not transparent. This is the dangerous version of AI startup development which has the potential to erode scientific rigor. Forget vibe coding, this is pure vibe analysis. When a human is taken out of the loop, the drive to have positive impact for another human is gone. Who cares how efficient you can make cancer diagnosis billing cycle, if there’s not a real person considering what it means to be diagnosed with cancer in the first place?

As a scientist, a black box analysis system is unacceptable due to its susceptibility to bias, data artifacts, confounding variables, and noisy data. This is the area where real researchers work to improve and explain how AI models function. But these drawbacks are hidden from the everyday user who wakes up and decides to build a health data app powered by AI for the first time. With these tools I have no doubt accessibility to technology will enable many more developers and creators by giving them opportunities that did not previously exist. Though we will all have to improve our ability to understand the reality of what we are looking at in an AI world. Anyone can recognize sloppy AI content, but will you know good AI content when you see it?

Relevant podcast recommendations:

This month we’re covering:

• Startups - May 1

• AI - May 8

• Podcasts - May 15

• Quantum - May 22

• Doctors (MDs) - May 29

Who we are

For the last few months Noah, Frank, and Becca have been working away, crafting several stories that have been taking up space in our brains. In the month of May you can expect weekly updates from the founding JUNC writing team. We will be laying the groundwork for more ideas and different series to come.

This month the core team of writers includes Noah and Frank who hold PhDs in biomedical engineering, and Becca who holds a PhD in chemical biology. We are from the same institution and have similar interdisciplinary training, however our research areas differ significantly.

Noah’s dissertation focused on the biological risks encountered during spaceflight. Frank studies innate cell chirality, and how cytoskeleton arrangement drives spatial organization. Becca’s chemistry work aims to modify natural fermentation byproducts to improve the chemotherapeutic properties.

Submit your article

Want to publish with JUNC? Head over to the Submit page.

Enjoy this month’s installment from the team at JUNC!

The Plan

Born from a New Years resolution from 2025, the Journal of Unsubmitted Content (JUNC) is finally taking shape. Our goal is simple: uncover the ideas from academics, researchers, and others in a science and technology that never make it to publication.

Think of all the hours spent contemplating a paradox or unanswered question that never quite becomes a paper. Think of the conversations after seminar where you unpack the implications of new work in your field. Remember the post-work happy hour where you debated something really nerdy for hours?

That’s what we’re after.

What to Expect

We are aiming to launch in May! Here’s a peek at the slate:

• Vibe Coding vs Vibe Analysis, and the Illusion of Understanding

  • Noah explores what it means to be an AI start up in 2026.

• Innovation at the Intersection of Impact and Incentive

  • Frank asks if money will solve all our problems.

• Modern Medicine: Are Doctors Ready?

  • Becca evaluates the current landscape of medicine and asks if anybody is prepared for personalized healthcare.

• Do We Have Free Will in a Quantum World?

  • Frank revisits his interest in figuring out what the heck we’re doing on Earth.

• Top 5 Science Podcasts to Learn More About the World Around You

  • Noah reviews his favorite shows that inspire and educate.

Get Involved

The Journal is looking for contributors! Have something to pitch or a draft ready to go? Review the Author Guidelines on the Submit page and send in your idea to start the process.

Have writers block? We have extra ideas, reach out through the About page for more details.

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