Recently, I read that Biolizard’s platform, Bioverse Navigator, won Bioinformatics Innovation of the Year at the 2025 Biotech Breakthrough Awards, and it made me curious. Bioinformatics tools get released all the time, but awards for such tools are not as common. So I started to look into what Bioverse Navigator actually does, why it stood out, and what it really changes.

As I looked into it more, it was interesting for me to see that it was not that they created a new algorithm or that they discovered a new gene. They were fixing something more basic but also more frustrating. Biology is becoming more and more of a data-heavy science, and a lot of researchers are struggling not with collecting the data, but with organizing it, maintaining it, and making sense of it in a timely fashion.

Biology is Generating More Data Than it Can Easily Handle

Modern biology does not produce small datasets anymore. A single human genome contains about 3 billion DNA base pairs, and sequencing technologies read these base pairs multiple times to reduce error, which makes the data files grow even larger. Gene expression experiments add another layer, since researchers measure activity across more than 20,000 genes at once, often across many samples. When proteomics, metabolomics, imaging data, and patient health records are included, the amount of data becomes overwhelming very quickly.

Scientists have been warning about this for years. A well known paper published in PLOS Biology predicted that by 2025, genomic data storage alone could reach between 2 and 40 exabytes, depending on how fast sequencing technology advances. One exabyte equals one billion gigabytes. The authors even compared genomics to platforms like YouTube in terms of data demand, which sounds dramatic but actually helps explain the scale of the problem

At this point, scientists do not struggle to generate data. They struggle to store it, process it, and actually understand what it is telling them.

Data Silos Make The Problem Worse

Through research and projects, I’ve learned that the size of data and data storage is not the only issue. The way the data is stored and managed also causes problems. In most research, different types of data live in different places. DNA sequencing data might be stored in one system, RNA expression results in another system, protein data in another, and clinical outcomes somewhere else entirely.

And different teams often manage each part, which makes it very hard to connect everything later. Sometimes these teams are on different continents altogether. These data silos slow down analysis and collaboration. Even when all the data exists, combining it can take months. Sometimes researchers spend more time organizing the files, making sure they have the right versions, than actually doing the research and answering scientific questions.

These silos have created barriers that reduce the effectiveness of the analysis. This is exactly where tools like Bioverse Navigator start to make sense. The issue is not that scientists lack tools. The issue is that these tools do not work across the scale and the silos.

What BioVerse Navigator is designed to do

Bioverse Navigator was launched in December 2024, and it was described as a visual analytics platform that helps researchers explore complex biomedical data in a single environment. Now, instead of moving between different programs and silos and constantly exporting files, researchers can load different data types into one workspace and explore them together.

The big pitch with Bioverse Navigator was that it uses a unified data environment supported by a data orchestration layer. In simple terms, the platform keeps all data organized, connected, and traceable, which is very important so researchers can trust what they are looking at. Biolizard also described the platform as AI-native, meaning machine learning tools are built into the workflows instead of needing additional work.

Ultimately, Bioverse navigator isn’t just for faster analysis but also clearer analysis

How Researchers actually use the Platform

According to Biolizard, this platform focuses on making analysis and visualization coexist together. Researchers can explore patterns in the data visually while running advanced analytics in the same environment. This is great because it allows them to test ideas and see results quickly instead of waiting for long processing steps and then trying to interpret results afterward.

This platform also reduces the barrier for any researcher who does not have a strong programming background. Not every biologist needs to be a data scientist, and not every clinician needs to learn how to code. This tool allows direct interaction with data and can change who gets to ask questions and participate in analysis. I think that’s pretty important because now curiosity gets answered much faster.

Medical research examples that show real impact

The most convincing part of BioVerse Navigator is how it has been used in medical research and development.

Bioverse Navigator’s site published a few case studies that I found interesting:

Prostate cancer research

In one project focused on prostate cancer, Biolizard reports that the analysis identified 187 genes strongly associated with prostate cancer, many of which were not previously well known for their connection to the disease. Now, these genes may not immediately lead to new treatments, but the fact that it narrows down potential targets from a massive data set and speeds up the process for researchers so they can focus their time and effort more effectively is a massive win.

Diagnostics and biomarker panels

Biolizard worked with MDxHealth to develop cancer diagnostics. In this collaborative project, Biolizard built a machine-learning-based risk scoring model using biomarker panels and improved quality control processes for laboratory assays. This type of work is less exciting than gene discovery, but diagnostics depend heavily on consistency and reliability. Improving these workflows and making them more efficient reduces time waste and creates a real impact on patient care.

Predicting transplant rejection

Another case involved predicting kidney transplant rejection using RNA sequencing data combined with clinical variables. Biolizard developed predictive models for early acute rejection and subclinical rejection. This example is a great case study in why integration of data matters, because neither molecular data nor clinical data alone tells the full story. In their platform, they could combine the data and help doctors intervene earlier and tailor treatment more precisely.

My perspective

What interests me is that this innovation was not a single breakthrough algorithm. It was a platform approach to unifying complex data and making analysis more accessible, efficient, and fast. Biological datasets are growing really fast. These tools help researchers stay organized, move faster, and be more efficient. They focus their energy on solving problems and asking the right questions versus dealing with the inefficiencies of organizing and trusting the data.

This research also acknowledges the fact that scientists and researchers spend a lot of time managing data instead of analyzing it. By reducing friction, they can move faster.

At the same time, I think it is important to stay cautious. Visual dashboards can make results look convincing even when they need more validation. Biology is not going to stop generating data. Tools like this show that the future of bioinformatics is not just in gene discovery and smarter algorithms, it is also in building smarter systems that researchers and scientists

As a South Asian American, I have grown up between two biodiversity worlds: the ecosystem of California and the ecological traditions of India. In the Bay Area, I am constantly surrounded by redwood trees, tidepools, and coastal fog, while neem and banyan trees, monsoons, and ayurvedic plants shaped the traditions within my family. I used to think biodiversity existed far away from daily life; however, in reality, biodiversity influences my health, culture, identity, and academic direction every day.

Growing up in California, biodiversity was my first teacher. During the weekends, we frequently went on hikes. Seeing the vast amounts of nature, I always wondered how redwood trees could grow so tall. Later, I learned about xylem, mycorrhizae, and fog-drip networks, but simply being in that environment created a spark of curiosity that still drives me. On special occasions, my parents took me near the ocean. I spent those afternoons watching hermit crabs, sea stars, and chitons in tide pools. Seeing how hermit crabs interacted with other species and their environment taught me about microhabitats and ecological adaptation long before I learned those terms in school. These early experiences shaped the way I approach scientific thinking today.

At the same time, biodiversity shaped my life through culture and values. My parents told me stories about plants and their benefits. I learned about neem, a tree used in traditional medicine for centuries, and the banyan tree, which signifies growth and resilience. Turmeric, amla, ginger, and tulsi weren’t just ingredients—they represented ecological knowledge developed over generations. These stories taught me to not see plants as solely organisms but as sources of healing and cultural meaning.

As I’ve begun thinking more deeply about biodiversity, I’ve realized how strongly it influences daily life. The gut microbiome especially interested me because it is shaped by every part of my routine—diet, weather, exposure to chemicals, stress, and the foods I eat at home. In other words, the dry weather of California, my school activities, and the roti and paneer I eat for dinner all contribute to the biodiversity of my gut microbiome. In turn, the microbiome affects not only physical health but also emotional well-being.

While learning more, I read several studies that made me think differently about this invisible ecosystem inside us. A 2024 Brigham and Women’s Hospital article described the largest and most ethnically diverse microbiome study to date, showing clear microbial signatures linked to Type 2 diabetes. NIH articles helped me understand how bacterial genes, metabolites, and even viruses within the gut can influence disease risk in ways we’re only beginning to map. Harvard Medical School also published work explaining how the microbiome affects metabolic pathways and even neurotransmitter production. Reading these sources made the connection between biodiversity, metabolism, and identity suddenly feel personal

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This curiosity eventually grew into a project. The more I read, the more personal it became. I learned that South Asians are 4–6 times more likely to develop Type 2 diabetes than Caucasians. Seeing this statistic alongside the research I had read made me wonder whether differences in gut microbial diversity could be contributing to this disparity. I also saw patterns across studies—certain microbial species appeared more frequently in diabetic individuals, and some varied significantly across ethnic groups. That made me want to understand the biology behind these patterns, not just memorize them.

These papers motivated me to begin independent research, where I am now analyzing microbiome datasets using machine learning to understand how biodiversity inside the gut relates to metabolic differences across populations. My culture, family traditions, and scientific curiosity all converged in this question. It felt like everything I grew up with—California’s ecosystems, Indian ecological traditions, and my interest in biology—finally connected in a single place.

All in all, biodiversity has not only shaped my early childhood but continues to influence my health, identity, and scientific direction. Growing up between two biodiversity worlds has shaped who I am and whom I hope to become.

Now AI is stepping into labs and reshaping that entire structure. Scientists are starting to use it to discover medicines faster, cheaper, and sometimes even more accurately.

In this blog, I explore and discuss how AI is changing the entire process of drug discovery, what that means for science and healthcare, and when this technology crosses the line from helping humans to harming them.

The Difficulties of Discovering New Medicine

The closest analogy I can think of for drug discovery is designing a key for a lock. First, scientists have to analyze the “lock,” which in this case is the disease or targeted gene. Then they begin the discovery process: trying to create a key that fits perfectly. Any extra bump or dent and the key won’t work.

Similarly, the chemical formulation must fit perfectly and trigger the right reactions for the gene or the disease. Many drug discovery researchers test thousands of compounds and end up failing along the way. Before a drug can reach humans, it has to pass a series of tests, including preclinical tests in cells and animals. After that, it must be tested over multiple phases of clinical trials in humans. Each stage can take years and cost millions.

And the worst part? If the drug fails later in the process, everything—money, time, and effort—is lost. This is the reason why huge pharma companies like Pfizer, AstraZeneca, or Novartis invest billions annually in their R&D and still face low success rates. The traditional approach is not completely an approach of hope, but it does require a substantial amount of trial-and-error and continuous learning.

AI’s Power in Drug Discovery

AI is not magic. It is a set of algorithms that learn the patterns from data and can infer outcomes based on what it learns. As a result, AI can analyze massive datasets, ranging from chemical structures to disease pathways, predict the outcomes, and find patterns that humans may miss.

According to a Microsoft Industries report, AI will potentially cut early-stage discovery timelines by about 70%, and this report has been proven true several times.

The biotech firm Insilico Medicine reported that they used AI to design a drug for idiopathic pulmonary fibrosis, a deadly lung disease. They reached phase one clinical trials in about 2.5 years, half the time it would normally take them. In this process, not only did they save time and research, but they also saved a lot of money.

Seeing examples like this across the world, I truly believe that AI has the potential to reshape how quickly life-saving drugs move from idea to shelves to the patients.

Now let’s look into how AI actually does it.

This is the part that I find most fascinating. Chemical companies have chemical libraries containing millions of possible compounds. If humans tested each one, it would take them lifetimes. AI, on the other hand, can identify which molecules have the most promising structures in only a few hours.

From there, AI can predict which drugs work. AI can forecast how a compound will behave inside the body, whether it will bind to a target protein, or if it’s toxic. This means a scientist can eliminate inadequate options before wasting time and money on experiments. In other words, AI is a filter.

That’s not all. When existing compounds don’t work, AI can create and design new molecules from scratch. Using a process called generative modeling, AI can create molecules that fit specific criteria—like avoiding certain side effects. There is active research in which AI can even imagine structures that scientists may have never thought of. For example, AstraZeneca reported that their AI systems found 170+ potential antibody drugs in three days, when the traditional methods had found zero after months of searching. Additionally, MIT researchers trained an AI model to search for new antibiotics. The AI was able to recognize patterns and identify compounds that would kill antibiotic-resistant bacteria, an observation no research scientist noticed before (MIT 2020).

AI’s use doesn’t just stop at the early stages.

Once a potential drug is found, clinical trials begin—and this is where most drugs fail. Trials are expensive and time-consuming; any failures at this stage are very expensive. No wonder AI helps here too.

It can analyze available health records and genetic data to find likely patients who are going to respond to a specific drug. By learning those patterns, it can even predict potential side effects earlier. At scale, it can monitor data almost in real time and therefore allow researchers to have enough information to adjust their trial designs faster.

So instead of waiting years to find out a drug’s failure, scientists can make decisions and figure it out in weeks, improving their chances of success.

It is pretty evident that AI represents a revolution in drug discovery. It brings in the speed of turning decade-long iterative and painful processes into a short period of time. Additionally, not only does it decrease cost so money goes far, but it also improves the precision of clinical trials, reducing the rate of trial and error. All of this sounds almost too good to be true, and in some ways, it is. The same speed that makes AI powerful also brings real risks, especially when we don’t slow down and look at the consequences.

The Risks

AI, like any other technology, is not perfect, especially in medicine. It learns from data, and if that data is biased or inaccurate, it learns incorrect patterns. It can also hallucinate and find patterns that don’t exist. We’ve all seen examples where ChatGPT gave us answers that didn’t fully make sense or solved a math problem incorrectly. While that may not seem like a major issue, a mistake in healthcare is the difference between life and death.

Let me walk you through a few examples. If you have an AI system that is trained on genetic data from a certain population, patients from other populations may receive misleading information.

For example, the majority of genetic data studies conducted use information from people of European descent, while individuals from places like Asia or Africa aren’t represented as well. Since AI uses data from existing databases to extract patterns, it can give misleading and incorrect data about groups that hardly appear in the dataset. This kind of biased data can lead to misdiagnoses, wrong dosages, or the lack of care for entire populations. Over time it will not just harm individuals—it widens the health gap, because the groups with more data get better care while others are left behind (The Journal of Global Health, 2025). In fact, in 2019, a healthcare algorithm used in U.S. hospitals was found to assign Black patients similar risk scores to white patients, even though the Black patients were actually sicker. That meant many Black patients received less care and fewer medical resources than they needed (Hopkins Bloomberg, 2019).

Unfortunately, these biases are not just data collection problems; they are also ethical ones. In some of the recent AI battles, we have seen how data privacy, lack of consent, and inappropriate use of data are big ethical issues in the world of AI. These issues carry over into the world of healthcare as well. Some companies have already trained models on patient data without proper consent, sometimes without even realizing it. And the truth is, machines don’t understand ethics; they only process whatever information they’re given.

And then there is the potential black box problem. We hear a lot about deep learning models, but those models are not always debuggable. My Spotify can recommend songs based on my music taste, but it may not always be able to explain why it chose those specific songs.

Now imagine doing this for something as serious as drug discovery or a treatment plan. A patient should be able to understand why a certain drug or treatment plan is being prescribed to them. The lack of transparency and observability in AI models makes that hard. It also makes accountability unclear. If something goes wrong, who’s to blame? The researcher? The doctor who prescribed it? The AI? In healthcare, accountability is very important, and losing accountability also loses the trust of patients and doctors.

And lastly, one of the biggest risks is over-reliance. Let’s assume a future world where AI is predicting and analyzing medicine and discovering drugs, but it doesn’t understand the ethical issues of empathy, pain, or fairness. A human scientist may pause, debate, and discuss a risky drug or take an ethical issue into consideration. AI wouldn’t, however. Relying too heavily on AI can turn medical discovery into a cold, automated system that forgets the human side of patients.

My Viewpoint

I believe we must slow down before we speed ahead. I actually don’t think AI is the problem; instead, the problem lies in how we are using it. I’m a big believer that AI has real potential to change medicine. It can find molecules faster, analyze patterns that we would miss, and predict how certain drugs may react. With proper testing and regulations, I truly believe AI can save millions of lives.

But we can’t ignore the consequences. In healthcare, faster isn’t always better. AI models can learn patterns around drugs and diagnoses but can’t question the ethics behind those methods. Earlier, I mentioned the importance of the diversity of data, and when data is missing, AI may overlook the populations that are most at risk. Similarly, AI may deem a medicine as “safe” in a dataset, even if the targeted group gets sicker from it. This means that with the rise of reliance on AI, others can receive less health care and support than before, and that’s not progress.

And it’s not just bias. Over-reliance is another danger. The more we trust algorithms, the more we stop questioning them. If we let AI do all the work, we lose the skills and judgment of understanding what is happening inside that black box. If AI makes a mistake, we don’t want the doctor to say, “Because the machine said so.” That’s the biggest fear: people forgetting to double-check. In healthcare, we can never afford blind trust.

That’s why I think we need teams of scientists, governing bodies, doctors, and lawmakers working together to pace this rollout, make the datasets more diverse, invest in extensive testing of these systems, and make sure we don’t cut down the processes that have served us for a long time. Because in the end, medicine is a responsibility.

I actually like what the World Health Organization recommended: a human-in-command approach for all AI in medicine systems. This means that the research scientists stay at the center overseeing every step, validating every AI prediction, and being accountable for the final calls. In other words, AI is your assistant; it’s not making decisions on your behalf. This gives room for human creativity and intuition. When a molecule fails, a scientist can still learn something new. AI just moves on to the next problem to solve.

Closing thoughts

Right now, a simple Google search shows that there are over 100 AI-designed drugs in various stages of testing or trials. Many are for critical diseases like cancers, rare genetic disorders, or infectious diseases that haven’t had the resources or the funding for treatment in decades. Even if a very few of these succeed, we could see a future where new medicines are taking 2–3 years from concept to shelves versus the 15 earlier. It is not just faster science; it is a lifeline for patients waiting for a cure. Patients whose diseases were rare enough that it did not make economic sense to invest in their research.

But speed and cost aren’t the whole story. The key unlock in this revolution will be machines and humans working together. So we leverage the strength of computation from AI, leverage them as assistants, but have humans solve for ethics, for observability, and for over-reliance.

That question sounds simple, but it’s actually one of the hardest questions in modern science. Because the line between curing a disease and designing a baby is thinner than most people assume.

What Is Genetic Engineering?

Every cell in our body contains DNA, which holds the instructions for how we grow and function. Genes are sections of DNA that tell our cells which proteins to make. When a gene changes or mutates, those proteins can stop working properly, leading to disorders like cystic fibrosis, Huntington’s disease, or sickle cell anemia.

Gene editing means using tools to change that DNA sequence. The most famous tool is CRISPR-Cas9, developed around 2012. According to Nature (2012), CRISPR lets scientists “cut” DNA at a chosen spot and then “repair” or replace it. It’s cheap, fast, and shockingly precise.

Here’s the twist though: there are two main kinds of genetic editing in humans.

• Somatic editing changes genes in the cells of one person. Think of it as treating a disease in an adult or child—it doesn’t pass to their kids.

• Germline editing changes genes in sperm, eggs, or embryos. That means the change is heritable. Every generation after carries the same edit.
  

The germline one is the real ethical storm. Once you edit a future child’s genes, you’re editing the future of humanity in a tiny way.

CRISPR is powerful, but it’s not perfect. Scientists warn about off-target effects, where CRISPR accidentally cuts the wrong part of DNA, and mosaicism, where some cells get edited and others don’t. There’s also the bigger question: we still don’t understand how every gene interacts with others. So a “fix” for one thing could create a new problem decades later.

In 2024, a Springer Ethics in Biology article called CRISPR “a technology with enormous potential and equally enormous uncertainty.” I think that sums it up perfectly.

Why Scientists Want to Use It

Let’s be honest, gene editing can save lives. Imagine a world without inherited diseases. No more cystic fibrosis, Tay-Sachs, or Duchenne muscular dystrophy.

Take cystic fibrosis for example. It affects around 40,000 people in the United States alone (Cystic Fibrosis Foundation). The disease causes chronic coughing, shortness of breath, and constant lung infections. Many patients need hours of therapy every day just to clear their airways. Some need lung transplants before age 30.

Now imagine using CRISPR to fix that gene before a baby is born. The child could breathe normally. No more hospital visits. No daily chest therapy. Because the fix is in their DNA, their children would inherit healthy genes too. One precise change could end cystic fibrosis in that family line forever.

Right now, scientists are testing gene editing to prevent diseases that are caused by single-gene mutations. For example, if both parents carry the gene for cystic fibrosis, every child has a 25% chance of inheriting it. Editing that embryo before birth could mean the child never develops it—and neither will their future kids.

This isn’t something that is happening in the future, it is happening right now. In 2023, researchers in the UK used CRISPR to fix a rare blood disorder in embryos in a lab (they weren’t implanted). Studies from the Broad Institute show potential cures for over 6,000 single-gene disorders if CRISPR becomes reliable enough.

This is the part where ethics starts to take over. Because once you can fix something deadly, how do you stop people from fixing something they just don’t like?

When It Crosses the Line

There’s a difference between treating and enhancing. Treating means fixing something broken. Enhancing means changing something that works fine, just not “perfectly.”

When scientists talk about treating genetic disorders, most people agree it’s ethical—if it’s safe. But when people talk about changing height, eye color, or intelligence? That’s where it gets messy.

Some people imagine “designer babies.” You could, theoretically, make a baby taller, stronger, or smarter by editing certain genes. The problem? Those traits aren’t simple. Intelligence alone involves hundreds of genes and tons of environmental factors—nutrition, education, childhood care, etc.

Even worse, it could create social inequality. A 2022 Pew Research survey found that 71% of Americans support gene editing to prevent disease, but only 5% support it for cosmetic or performance reasons.

That’s because it feels unfair. What if only rich families could afford to make “genetically superior” kids? What happens to people born naturally? It sounds like the start of a dystopian movie, but it’s actually a real concern.

And it’s not hypothetical. We’ve already crossed that line once.

The CRISPR Babies

In 2018, Chinese scientist He Jiankui claimed he’d created the first genetically edited babies. Two twin girls—nicknamed Lulu and Nana—were born with altered DNA. He said he edited their embryos to make them resistant to HIV by disabling a gene called CCR5.

The scientific world went crazy.

Turns out, He Jiankui had faked approval documents, misled the parents, and didn’t follow safety protocols. Later studies showed the edits were incomplete and might have caused new mutations. No one even knew if the girls would actually be resistant to HIV.

In 2019, Science Magazine reported that He Jiankui was sentenced to 3 years in prison for illegal medical practices. China immediately passed stricter laws banning reproductive gene editing.

That case changed everything. It made the scientific community realize that the line between “helping” and “experimenting” was much blurrier than anyone wanted to admit.

It’s kind of tragic too. Those twins didn’t choose this. They became global experiments without consent. That’s where ethics hit hardest—because in germline editing, the person affected can’t agree to it.

The Big Ethical Debate

Every major medical decision is supposed to follow four principles:

1. Autonomy – the right to make decisions about your own body.

2. Beneficence – doing good for others.

3. Non-maleficence – “do no harm.”

4. Justice – fairness and equality.

Germline editing clashes with all four.

Autonomy: Future children can’t consent. It’s like making a life-changing decision for someone who doesn’t even exist yet.

Beneficence: You might prevent disease, but if it causes new mutations, was it really “good”?

Non-maleficence: Scientists can’t promise safety yet. CRISPR errors might harm not just one person but generations.

Justice: Only wealthy families could afford genetic modification. That could create a new form of inequality—biological inequality.

And then there’s the disability rights argument. Some activists say trying to “remove” disabilities sends the message that disabled lives are less valuable. For instance, deafness or dwarfism aren’t always seen as illnesses—some see them as part of human diversity. Editing them out feels like erasing identities.

So yeah—it’s complicated.

The Slippery Slope and the Eugenics Shadow

This whole topic also reminds people of the past—especially the eugenics movement in the early 20th century. Back then, governments in the U.S. and Europe forced sterilization on people they considered “unfit,” trying to create a “better race.” That’s what happens when science loses ethics.

Now, even though CRISPR is voluntary, it could lead to a softer version of the same thing: people editing their kids to fit a social ideal. Blue eyes. High IQ. Perfect health. If enough people start doing that, diversity could shrink, and society might treat “unedited” people as less than.

I think this is why so many scientists say, just because we can, doesn’t mean we should.

The Two Sides

Let’s be fair. There are two sides.

The Case For Genetic Engineering

First, the positives.

It can prevent suffering. Around 300,000 babies are born each year with sickle cell disease worldwide (WHO, 2023). Many die before adulthood. If CRISPR could fix that gene safely, millions of lives could improve.

It could also reduce the burden of healthcare. The CDC estimates the lifetime cost of treating cystic fibrosis is over $800,000 per person. If gene editing removed the disease completely, it could save billions globally.

Then there’s the moral side. Some ethicists argue that if we have the power to stop pain and we don’t, we’re being irresponsible. This idea—called the beneficence obligation—suggests we actually have a duty to use gene editing, as long as it’s safe.

And finally, every medical technology starts risky. Heart transplants, in vitro fertilization (IVF), even vaccines—all faced backlash at first. IVF was banned in many places in the 1970s, and now millions of kids owe their existence to it. Maybe gene editing will follow a similar path.

The Case Against

Now, the other side.

The unknowns are massive. CRISPR cuts DNA, but our genome is insanely complex. There are over 20,000 genes, and many interact in ways we still don’t understand. A single change could cause cancer, immune disorders, or even mental illness years later.

Also, ethics moves slower than technology. In 2024, over 75 countries still had no clear regulations for human germline editing (UNESCO Bioethics Report, 2024). That means someone could easily repeat what He Jiankui did.

And then there’s inequality. Gene editing could easily become another way for rich people to buy advantages. If one generation of wealthy families edits their children to be smarter or healthier, that gap could grow exponentially.

As Dr. Jennifer Doudna, one of CRISPR’s inventors, said in her TED Talk: “We are at a point where humans can control evolution. That should terrify us a little.”

What I believe

In my opinion, gene editing is one of the most important and promising breakthroughs in modern science. If humanity can truly master it—and handle the ethical part—it could save millions of lives and completely change how we treat disease. I fully support that future. But I don’t think we’re ready yet.

We still don’t know what long-term effects editing can have, and the line between helping and harming can be crossed too easily. On top of that, most countries don’t have strong or consistent regulations. That makes me think this shouldn’t be a large-scale global experiment yet—it should start in limited, tightly monitored steps.

I think gene editing should be tested on a restricted number of cases—enough to study properly, but small enough to control and learn from safely. These studies should be regulated by an international committee, not by individual countries, so that decisions follow shared safety and ethics standards.

Every edited child should be monitored throughout their lifetime to track health, development, and any genetic effects. Because these are babies—they can’t consent, and one mistake could affect not just their life but their descendants too.

Starting small gives humanity time to create fair global rules and learn what works. If results are positive and consistent, then we can expand responsibly. But right now, patience and global cooperation matter more than speed.

I don’t think we should stop gene editing. I think we should work toward mastering it the right way. The potential is real, but so are the risks—and we can’t afford to get this one wrong.

What Comes Next

Right now, most countries ban germline editing for reproduction. The U.S. forbids it using federal funds. The UK allows embryo research but not implantation. China passed new bioethics laws after 2018.

Still, research continues fast. In 2023, the first CRISPR-based therapy for sickle cell disease (called Casgevy) was approved by the UK’s Medicines and Healthcare Products Regulatory Agency (BBC News, 2023). That’s a huge milestone—it’s not germline, but it proves the tech works.

Scientists are also exploring prime editing, an upgraded version of CRISPR that can “rewrite” DNA letters without cutting. It’s like the spellcheck of genetics. If that becomes reliable, it could reduce off-target risks even more.

But ethics will always need to play catch-up.

As Harvard geneticist George Church said, “We need to ask not only what’s possible, but what’s wise.”

The Final Question

At the end of the day, this debate isn’t really about CRISPR. It’s about us.

Every generation invents something that forces humans to redefine what it means to be human. Fire, printing, the internet—now CRISPR.

The potential is huge: curing diseases, saving lives, even extending lifespan. But the danger is real too: inequality, discrimination, or irreversible mistakes.

Maybe the question isn’t “Should we edit genes?” but “Can we do it with enough wisdom, humility, and empathy?”

Because once we start editing the code of life, there’s no undo button.

And maybe that’s what scares me most.

Works Cited

National Human Genome Research Institute. “Ethical Concerns and Genome Editing.” Genome.gov, National Institutes of Health, 2023, https://www.genome.gov/about-genomics/policy-issues/Genome-Editing/ethical-concerns.

“The Pros and Cons of Gene Editing Babies.” The Week, 12 Feb. 2021, https://theweek.com/news/science-health/959606/pros-and-cons-of-gene-editing-babies.

“The CRISPR Babies.” Science History Institute – Distillations Podcast, 2020, https://www.sciencehistory.org/stories/distillations-pod/the-crispr-babies.

Powell, Alvin. “Perspectives on Gene Editing.” Harvard Gazette, Harvard University, 24 Jan. 2019, https://news.harvard.edu/gazette/story/2019/01/perspectives-on-gene-editing.

American Society of Gene & Cell Therapy. “Ethical Issues in Germline Gene Editing.” Patient Education Portal, 2023, https://patienteducation.asgct.org/patient-journey/ethical-issues-germline-gene-editing.

Cystic Fibrosis Foundation. “About Cystic Fibrosis.” CFF.org, 2023, https://www.cff.org/intro-cf/about-cystic-fibrosis.

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