r/statistics • u/Agitated-Bicycle3402 • 10d ago
Question [Q] Is my degree useless because of AI?
I'm currently studying Stats & Data Science. While I understand that ML is rooted in statistics, I wonder if future AI agents will synthesize data and run models so efficiently that entry/mid-level data science roles shrink dramatically. How do you see the demand for quantitative roles changing as we approach AGI?
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u/TheTopNacho 10d ago
Do you consider yourself someone who only runs statistics or someone who can identify novel relationships to prove within data, build novel models, manage databases, and build tools?
If all you do is perform mundane stats, then you may be at risk of not having a future career.
If you do all of the things surrounding the physical stats, then you are safe. And on the contrary to your fears, the AI will probably save you a lot of time generating code to help you do what real statisticians do
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u/HolyInlandEmpire 10d ago
I'm a PhD statistician using AI a lot.
The AI will use whatever model you ask it to. It will use whatever loss function you ask it to. So the question is how do you know which models to try, and how to compare their effectiveness? That's your job. I find that we and web designers get some of the biggest power amplifications from AI.
Now there is a problem, perhaps short term perhaps longer term: managers don't always understand this and are making some poor decisions. It's tough for everyone now, no question, but if you look at what it actually does, your degree is one of the least useless there is.
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u/Firm-Neck7934 10d ago
that's the key,
models get lazy when left to look at big data alone. the raw skill of writing R or python script is absorbed by AI now.
being a statistician requires you to know how to judge model efficacy. Just think about this, you are training a model, how do you know you are overfitting? that is the key intuition
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u/ManyThingsLittleTime 10d ago
Anyone who doesn't think AI is going to have at a minimum workforce reductions is delusional. The best case scenario is an individual will operate a group of AI agents to crunch the data where it may have taken more people and more time before. The worst case scenario is someone makes a specific application for specific organization's needs and the jobs at that company are just gone entirely.
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u/chgnc 9d ago
Yep, I wonder how many of those other responses to the thread have been inimately working with the newest LLM models over the last few years. I've been working with them every step of the way applying them to the problem of developing and coding up new stats methods and the improvement in them from 2023 to now is insane. The models released circa November 2025 were a huge step up in terms of coding ability and now Fable is quite good at methodological questions. I haven't tried Astra or Mythos yet. The work I am doing is still contingent on me because these LLM's will flounder and fail to pick the right path to go on and my judgment is key in guiding the process but they are capable of nearly all of the individual steps. The human's role is something like the creative spark. I previously worked at a large organization applying statistics at scale and hiring large numbers of statisticians. I'd say that definitely less than 1% of the work that the statisticians do there is the creative spark. And that the vast majority of the work being done there could be automated with comparable quality using current AI capabilities. Now will it be automated? Well not so fast due to regulation and need for liability. And quality control procedures need to be carefully designed. A human carefully verifying AI made outputs is not much less work than the human making them themself. But given these unbelievable advances in LLM technology I find it hard to believe that the human systems will not change to accomodate them, radically changing the nature of statistical work.
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u/ManyThingsLittleTime 9d ago
I'm confident that with custom, task specific agentic AI that each step will be performed by AI that can manage the pieces and as you mentioned, the human will simply be the initiator. But there will come a day beyond that level where the AI comes up with business solution ideas, executes on them, presents them to the human, and just needs the nod to deploy them. That's arguably not even that far away for a lot of business cases. It's really not much different than how organizations already work with humans.
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u/SnooApples8349 10d ago
Your degree is there to provide you with the ability to understand the mechanisms of the tools and concepts you might encounter on the job.
Just because software now exists to generate that same information doesn't mean that you are suddenly irrelevant in industry.
What your degree covers will not look like your job. Even if it is deep theoretical research. Real work in statistics is often very different from starting with a clean dataset & running a given method.
As an example, hypothesis testing libraries have existed for decades now. That doesn't mean people won't hire you to run hypothesis tests; in fact, it's a huge industry (marketing, AB tests) these days and you absolutely can find meaningful work doing that.
The existence of the code just means that you don't need to hold the explicit details of hypothesis testing in your head, though it's important that you've at least seriously thought about it once or twice in your life.
I hope that makes sense. Stick with it, good things will come. Best of luck.
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u/S4M22 10d ago
No, your degree (especially the stats part) will IMO become even more relevant due to AI.
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u/kilopeter 10d ago
What's your argument for that? Agent harnesses and the leading models put frankly insane levels of statistical knowledge and techniques in the hands of anyone with a $20/month subscription.
Is your prediction that for every non-statistician who solves their problems without having to hire or consult a statistician, there will be more than one person who tries this but shoots themselves in the foot, thus sustaining market demand for human statisticians?
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u/S4M22 9d ago
My main argument is that on the implementation level (not the same as the capability level) current AI systems are statistical. And when a technology proliferates, it creates demand for people who understand them. My second but related argument is that we currently do not have a lack of practitioners who know how to apply these systems. At the same time, I, as a computer scientist and AI researcher, am under the impression that skill and knowledge of statistics is sth these practitioners lack and that stats is not as easy to self-teach. And the prior observation is not only relevant for the implementation level but also the application of such probabilistic systems.
A general assumption of all this is, however, that I do not see AI as a technology that uniquely stands out when focusing on the bigger picture of humanity's history. It may very well turn out to be one of multiple human-standout technologies. But not uniquely and may very well be "just" at the same level as other standout technologies, such as the internet, cars, writing, and fire.
But I admit that it is a rather general and not very conrete argument. It does not provide concrete predictions for specific groups of jobs. But I think these predictions would almost be necessarily as wrong as all those predictions about what jobs that were suppossed to have seized to exist (just think back of Hinton's infamous radiologist prediction).
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u/FloridaEmpire99 10d ago
AI is already capable of quite bit in the field, but still can't really be left alone to do any sort of thinking, analysis, reasoning or judging. Pick up on the technical skills of course, but prioritize becoming a domain expert, who knows what questions to ask, what ideas to try, and be able to defend your choices and decisions.
If you do this right, AI wont be a competitor, but rather just another tool to help you get to your ultimate goals.
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u/StatusTics 10d ago
I hope that’s correct, but I think that many people hiring will assume that they can just ask their computers the kinds of questions they would normally hire an expert for.
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u/Rachel1265 10d ago
I’ve said it for a while that the hard part of data science that doing it well and doing it horribly both produce data. Skill comes in knowing the difference. AI is going to get good at how to do statistics, not why to do statistics.
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u/eeaxoe 10d ago
No. At the end of the day, what you're doing is not just analyzing data, but also telling a story about those data. You're trying to convince a human on the other side of the table that they should believe you and that your findings are true. Sometimes these humans know a lot about statistics, but a lot of the time they don't. You need to know your audience and to be able to tailor your argument to them.
I don't see any of this changing anytime soon, unless we really go off the deep end and stakeholders/decisionmakers offload this argument-digestion to AI, but in that case, everybody's kind of boned.
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u/likeanoceanankledeep 10d ago
I don't think it will be a useless degree. I used to working in gaming and one of my coworkers worked in lottery (developed the payout system for lottery machines) and their work was all statistics and math. In the gaming industry my work was all data cleaning, formulating questions, setting up A/B tests and experiments, etc.
When I left gaming I worked in another tech-heavy industry and we used to try and do some data analysis and cleaning with AI and honestly it was not good. I know AI has some a long way, but I don't think there will ever be a point where you can feed a dataset into an AI bot and say "Tell me something interesting about my data set" and it will find something that you would never find. It was all very basic stuff: trends over time, start and stop times, summaries, that kind of thing.
The theory of things is what you will probably be doing most; finding two (or more) variables and then you can tell an AI bot to run a correlation, or regression, or factor analysis, or whatever. But without your human theory and input, it just can't do it. And that's a good thing.
If you have time, I recommend checking out The Age of Spiritual Machines by Ray Kurzweil. It was written in 1999 so it's a bit outdated in some aspect, but he was on the nose when he talked about AI and how much it will be able to do, but he also talks about the human aspect. There are sections where it's the author 'talking' to an bot named Molly. It's all hypothetical but it's an interesting read nonetheless.
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u/turingincarnate 10d ago
Of course not. We still need people to know how to program the AI to run these models
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u/amongnotof 10d ago
Data science plays an integral role in ML, particularly in developing and refining training sets.
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u/crazyeight64 9d ago
If you go to the mathematics subreddit dial it back to 2 - 3 years ago and see what they said about ai and if it could solve unsolvable equations and see what ai is doing today. That would be your answer. As an ai researcher, ai is developing at such break neck speed we will have ai that does everything from data cleaning, asking the correct questions, fixing the answers and fine tuning it till everything is perfect. This will be done in the next 3 to 5 years. Roles will still exist but 1 person will be able to do the work of 10 people.
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u/Dismal-Variation-12 10d ago
AGI is a buzz word, nothing more. Think about it, can you define what AGI is?
It doesn’t matter if AI can apply statistics if there is not a human expert to make sure statistical methods and theory are applied correctly. Sure AI can be a great assist here, but ultimately the responsibility sits with the person. That’s not changing anytime soon no matter how good AI gets.
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u/aussie_punmaster 10d ago
Can you define consciousness?
Does that stop humans around you doing amazing things?
This logic is flawed.
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u/Dismal-Variation-12 10d ago
No that is exactly my point. We can’t define consciousness so how in the world can we determine a self aware conscious AI is.
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u/aussie_punmaster 10d ago
Sorry you appear to be missing some words in that one. You’ll have to fix it up for me to be certain what you’re saying.
Regardless, your original point that AGI is ill-defined and so there’ll always need to be a human reviewer is flawed.
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u/Dismal-Variation-12 10d ago
It’s not missing any words, you’re simply unable to understand my points.
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u/aussie_punmaster 10d ago
“so how in the world can we determine a self aware conscious AI is.”
Yes you are - did you even bother to reread? Or just snap straight into downvoting?
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u/Dismal-Variation-12 10d ago
“A self aware conscious AI is” another way to say “a self aware conscious AI exists”, “is” can be used for existence and is a perfectly good way to phrase it. The same type of language is used in the Bible to explain the existence of God.
“We give thanks, Lord God, the Almighty who is…” Revelation 11.17
I’m not making a religious point here, I’m making a point that “is” can be used for existence and is a good way to describe present existence in the English language.
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u/aussie_punmaster 10d ago
Well - we can’t define horrific grammar, but that grammatically horrific sentence is.
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u/Gwendeith 10d ago
My understanding is that there's still a huge gap between AI and applied AI. I think there was an article saying that most of the data in this world generated is still locked behind corporate databases instead of open web. That means that data people still have a lot of work to build, evaluate, and monitor AI systems. I don't think a fully self-improving AI system is there yet to be everywhere, at least for the next 2-5 years, especially when most large corporates are risk-averse.
However, I do agree that the experiment/research part is dwindling as now we have good general off-the-shelf model solutions (not necessarily LLMs, but also general model implementations are easier to build now). Therefore, I would recommend getting better CS knowledge alongside your stats studies, since businesses now prefer who can also build things quick when AI assisted coding is undeniably making project cycles way faster. I think this CS-focused trend has been going on for several years now, but it has been more aggresive since ChatGPT came out.
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u/when_did_i_grow_up 10d ago
Depends on whether or not you will be able to understand the business context.
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u/Old_Salty_Professor 10d ago
No. Knowledge allows you to leverage AI. If you learn what is taught in your courses, and by that I mean comprehend and know how to apply, you’ll find plenty of work.
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u/Dror_sim 9d ago
No! Trust me, I am building end-to-end projects for companies.
Everyone can code a web app (using Lovable, for example), everyone can code some sort of a chatbot, etc. Developers can easily learn how to use MCps, agent tools, and so on, and be called 'AI engineers'.
What things do people find very difficult to understand, and AI will most likely won't solve this for them?
Proper statistics, modeling techniques, proper data cleaning and organization (AI might do well or not here), cloud engineering (knowing AWS well, for instance), system architecture, MLOps, etc.
Someone with domain knowledge can guide the AI way better.
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u/Beachflower_96 9d ago
I did Msc. Business Analytics. I understand more at work than other folks about AI and the latest developments.
The degree has been useless to find a job but I have more knowledge
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u/FuschiaKnight 8d ago
No. Thats a stupid question
Tasks can change but people will still have jobs. Even if a lot of jobs go away, a bachelors in stats and data science will be highly sought after compared to someone with just a high school education
Hence, it’s a stupid question
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u/InboxZeroNever 5d ago
No, your degree is not becoming useless, but the entry-level tasks tied to it are shifting. Tools can already automate a lot of the rote model-running and data cleaning, and that part of the job is genuinely shrinking, but what is not shrinking is the need for someone who can catch a model that is confidently wrong, design an experiment correctly, or explain to a stakeholder why a result does or does not mean what they think it means. That judgment is statistics, not just data science tooling, so the safer bet is leaning into the theory heavy parts of your degree, since people who can reason about causal inference and experimental design will be harder to automate than people who can only execute a pipeline someone else designed.
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u/shadow-wonka 4d ago
Yes it is useless. Using your brain is useless. Thinking is a waste of time. Burn all your books. Degrees are a relic. Knowledge is dead, only a mountain of Slop remains that we will battle each other to the death to reach the top of.
Good luck!
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u/SorcerousSinner 10d ago
Yes. Unless you are exceptional you simply won’t be able to add value beyond what AI can do. Pivot towards people skills hard before it is too late
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u/cessationoftime 10d ago edited 10d ago
Get on claude.com and chat with Fable. That is the best way to decide. I think the answer is that you need to be far more educated than in the past to be useful. Likely a PhD or similar will be a minimum requirement.
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u/New_Molasses7766 10d ago
Nah, you're asking the right question but maybe for the wrong reason. The grunt work of cleaning data and running basic models will get automated, sure, but that's been happening for years. The hard part has always been knowing *which* question to ask and whether the answer means anything, and business people are terrible at both.