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Home Artificial Intelligence

Generative AI, Discriminative Human | In direction of Knowledge Science

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February 28, 2026
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Doom scrolling by means of social media to learn AI information immediately is sort of a Rorschach Inkblot check: no matter you might be on the lookout for, you can find.

In case you suppose AI is a large waste of cash, you can find that angle nicely coated. If you’re vested within the trade and anxious about whether or not AI is a bubble there are numerous breathless takes on the subject. If you’re on the lookout for proof that AI will finish the world, ‘breaking information’ that may affirm that viewpoint abounds. 

Amid the recursive, agentic AI-generated summaries of hallucination-ridden summarized slop, I had the great fortune to talk with some good folks from Praxis who had been doing nice work with college students in regards to the pressing want for important pondering expertise.

That chat impressed this publish. 

The next is a synthesis of the highest 10 issues I might share with somebody eager to critically suppose by means of how AI is impacting our world. 

1. Generative AI with discriminative people is the brand new state of the world.

Exterior of information science and AI teams, it might shock some that till just lately, most machine studying fashions had been “discriminative” in nature, doing issues comparable to anomaly detection, information evaluation, and classification (with well-known examples of AI fashions within the early 2010s targeted on differentiating between cats and canine). 

Knowledge analysts and information scientists then used these outputs to generate compelling narratives (a talent referred to as ‘information storytelling’), delivered by means of engaging studies and shows.

Right now, this dynamic has reversed — generative AI can produce these polished studies and shows, however people have to carry important pondering — shaping the course of content material technology, discerning high quality and offering true context (past the intelligent misnomer of the ‘context window’ in AI apps).

Put merely: 

The necessities of a superb piece of labor haven’t modified — however the roles are reversing. 

In case you take nothing else away from this publish, take away this — we’re shifting away from work characterised by discriminative AI and generative people, to a world of generative AI, that wants discriminative people.

2. Assume critically about what forms of AI to make use of, and whether or not to make use of AI in any respect.

Earlier than we go too far, it’s price acknowledging that ‘AI’ is commonly an unhelpful time period. Whereas it’s a well-established discipline of educational research, at current it’s getting used so loosely that it’s changing into unmoored from its fundamentals. 

Virtually, AI encompasses an unlimited array of strategies and applied sciences, and utilizing AI as an umbrella time period muddies the dialogue and supplies fertile floor for misunderstandings, buying and selling nuanced and grounded discussions on the strengths and limits of various AI approaches for hype, name-dropping and pointless obfuscation. 

As an illustration, classical machine studying methods are extremely environment friendly on small datasets, statistical strategies are the appropriate software if you find yourself interested by relationships between options, and symbolic strategies which explicitly symbolize issues and data clear up for explainability. Every of those sub-branches of AI represents a sturdy and well-developed toolkit that solves issues that plague present massive language fashions.

In that sense, AI is much less like a hammer to throw at each downside, however extra like a toolbox with quite a lot of instruments, and making use of the appropriate kind of AI to the appropriate downside goes a great distance in eradicating its mystiques and dangers. Pushing for particular language the subsequent time you hear ‘AI’ will carry you readability.

I’ll outwardly smile however die somewhat inside in the event you ever use a big language mannequin as a calculator.

3. Assume critically about designing AI programs to help you, or it’s possible you’ll discover them controlling you.

A well-cited paper characterizes two methods totally different folks successfully work together with AI by likening them to ‘cyborgs’ and ‘centaurs’. Centaurs create a transparent division of labour and deal with AI as instruments, whereas cyborgs combine AI deeply into their thought and work processes in additional versatile and dynamic methods. 

Each are legitimate patterns of human-AI teaming, however what’s most harmful and insidious is the ‘reverse centaur’, coined by Cory Doctorow, the place AI programs lead and direct, and AI treats people as instruments. An instance is his description of supply staff on the mercy of AI programs that optimize outcomes for the corporate by monitoring them to the nth diploma, right down to video cameras in autos monitoring the motion of their eyeballs.

A associated level on the ‘darkish patterns’ of AI that proceed to unfold at tempo is the belief that the targets that an AI system has are sometimes the targets of AI corporations, not AI shoppers. Suggestion engines that energy social media feeds to maximise engagement are a first-rate instance, basically arraying a pressure of engineers, psychologists, and designers to focus their expertise in opposition to you to gas promoting income machines. With dependancy, misinformation, and different second-order ills an inconvenient however largely ignored reality.

That is significantly insidious as corporations can even conceal behind the narrative that ‘we’re solely giving prospects what they need’. However on this case, corporations are preying on our baser ‘system 1’ lizard brains (usually successfully hijacking our minds by design), versus serving the higher intentions of deliberative ‘system 2’ brains. 

Actively design AI programs such that they serve your finest self.

4. Assume critically about how Generative AI blurs out uniqueness and the way to protect your distinctive self.

A latest research confirmed one of many unintended penalties of huge numbers of individuals utilizing generative AI to supply content material is that on-line content material is more and more trying the identical. And this persists regardless of variations in programs, prompts and utilization.  

The identical research additionally suggests folks want content material with out generative AI — whereas the research discovered that not utilizing AI results in fewer posts on-line, content material posted with out generative AI has extra optimistic engagement. That is unsurprising, and encapsulated nicely with the quote:

“Why would I trouble to learn one thing somebody couldn’t be bothered to write?” 

— BBC Function

This means that each for maximising your exterior affect and for growing your inner id, there has by no means been a extra essential time to seek out and keep true to your personal voice.

5. Assume critically about how utilizing Giant Language Fashions have an effect on our brains and psychological health.

A research by the MIT Media Lab in contrast mind exercise on a job between folks utilizing 1) simply their brains, 2) serps, and three) massive language fashions, and their outcomes current strong proof that our brains work in a different way when assisted by expertise.

The Mind‑solely group exhibited the strongest, widest‑ranging mind exercise; the Search Engine group confirmed intermediate engagement, and the LLM-assisted group elicited the weakest general mind response.

Moreover, LLM customers had much less possession and had bother quoting their very own work. And over time, the LLM customers “persistently underperformed at neural, linguistic, and behavioural ranges”.

As we select to make use of AI to assist us with cognitive duties, we lose our connection to the duty and the advantages of finishing the duty ourselves, with long-term implications.

Simply as shifting away from guide work in direction of sedentary life introduces dangers to our bodily well being, necessitating suggestions for deliberate bodily exercise to compensate, LLMs are already quietly endangering our psychological health.

6. Assume critically about how AI is impacting our worldview.

The earlier level brings us to how we take into consideration the impacts of AI.  A lot dialogue centres across the affect that AI impacts our work and threatens to automate away our jobs, however that’s solely part of the story.

Firstly, simply because a job is ‘uncovered to AI’ doesn’t imply it needs to be automated, and jobs are greater than a group of duties. There are relationships, accountability and moral judgement, to not point out human presence.

One irony of agentic AI is how little we discuss how we as people have company to design the place are how we implement AI and level it in the appropriate course.

what the largest downside with pushing all-things-AI is? Mistaken course.
I would like AI to do my laundry and dishes in order that I can do artwork and writing, not for AI to do my artwork and writing in order that I can do my laundry and dishes.

— Joanna Maciejewska (@AuthorJMac) March 29, 2024

A extra helpful strategy to suppose by means of the impact of AI on any given space is thru the ‘4 Ws’ — Workbench, Work, Employees, Worldview. Workbench is the software or expertise that’s getting used for work. Work is in regards to the duties and actions being carried out and the buildings that help them. Employees discuss with the folks doing the work and different stakeholders, and Worldview is in regards to the unstated assumptions and the best way issues work in a website.

To take an instance from training, the place there are discussions on college students utilizing ChatGPT and related AI programs for his or her homework and exams. There may be a number of hand-wringing on how new generative AI instruments like ChatGPT (workbench) are used to do assignments (work). However reasonably than fixating on the way to detect use of generative AI in isolation, a greater method can be to consider how college students (staff) are altering when it comes to them studying much less of the subject material whereas choosing up AI literacy, and the way the training system must adapt (worldview) to the brand new actuality.

7. Assume critically in regards to the AI tales being informed and search for the lacking tales.

There’s a large sum of money at stake to the tune of over a trillion {dollars} for most of the world’s largest AI corporations. This creates immense strain for these corporations to speed up their flavour of AI adoption, and this drives AI ‘hype’ by means of advertising spend, high-profile media interviews, and PR machines that may spin info in self-serving methods. Most just lately, information broke of AI corporations paying influencers $400,000-$600,000 to publish about AI.

It is very important realise that most of the tales we’re being informed about AI overwhelmingly symbolize the views of individuals promoting AI, reasonably than folks genuinely experiencing it.

This has been referred to as the AI story disaster, the place the dominant narratives that form the general public discourse on AI are formed by a skewed pattern of storytellers, which can distract and mislead public understanding and conceptions about AI.

I might go additional to level out that narratives form greater than ‘the general public’, however extends into governments and corporations, which raises the stakes.

AI can’t do your job, however an AI salesman can 100% persuade your boss to fireplace you and exchange you with an AI that may’t do your job. 

— Cory Doctorow

On this setting, be discerning and look past content material to contributors. Assume by means of who’s behind every AI story, and what drives them: is what you might be studying coming from somebody’s genuine opinion, or from somebody being incentivised to border the story a sure method? Query the framing of the story, and take into consideration the stakeholders whose voices usually are not being heard.

And so far as genuine opinions go – top-of-the-line methods to verify the tales… is to expertise AI for your self first hand.

8. Assume critically in regards to the provide chain behind the AI trade. 

As an information scientist, three essential inputs to an AI mannequin are a mannequin’s coaching information, the labour used to annotate and course of it, and the compute utilized in mannequin coaching and utilization (referred to as ‘inference’). Sadly, a big a part of generative AI is constructed on a provide chain the place every of those three elements is much from best.

Karen Hao’s well-written e-book Empire of AI does a greater job than I can in spelling out the dysfunctions. However in short:

  • Knowledge used for the coaching of huge language fashions is at present the topic of a number of lawsuits the place AI corporations are accused of illegally copying hundreds of thousands of articles to coach AI fashions. 
  • Environmental points abound with the present technology of AI fashions. Coaching actions are extremely energy-intensive, and so is the vitality utilized in operating person queries (referred to as ‘inference’). Disclosure is commonly problematically sketchy, however factors to a hefty local weather footprint, with prices doubtlessly being handed on to shoppers.
  • Labour within the AI trade might recall to mind well-paid information scientists and software program engineers in slick metropolis workplaces with free lunches, however in actuality, massive language fashions are additionally powered by massive offshore workforces whose work includes flagging, annotating, and processing disturbing content material, together with poisonous and dangerous content material, graphic violence, and worse. A lot of this exercise happens at low-cost nations in exploitative circumstances at an amazing value to psychological well being. 

There are higher methods to create AI programs, and we should always resist this from changing into the norm.

9. Assume critically about adoption time horizons to parse the true affect of AI.

Coming full circle to our doom-scrolling, one lens means that the world is altering in a single day, with the most important suppliers asserting a median of two mannequin releases a month in 2025.

Nonetheless, the discharge of a brand new mannequin is a far cry from altering the world. I discover it helpful to tell apart between invention (a brand new mannequin breakthrough and its launch), adoption (the stated mannequin being carried out in a usable product), and, most significantly, diffusion (when it slowly spreads by means of organisations and households over time). 

Taking the narrative of AI changing jobs for instance, jobs are way over the sum of their duties, with deep context, accountability, and relationships. As well as, whereas new basis fashions are performing nicely in tough exams comparable to in finance and medication, there are important lags between the invention of those fashions and their being broadly subtle into organisations and society.

Generally, my expertise within the context of huge corporations means that whereas invention could also be measured in days because the data sweeps by means of the organisation, the adoption of fashions into AI programs and merchandise tends to take weeks, and diffusion is a a lot slower course of that may stretch into years as habits kind, work processes slowly reconfigures and expertise slowly grinds by means of a number of particular person, cultural and organisational boundaries.

AI has been in comparison with tractors in its means to displace staff in an analogous method that tractors ultimately displaced the usage of horses for agriculture. With the good thing about hindsight, it’s instructive that tractors took a full technology to overhaul horses. And whereas there are arguments that within the digital world issues transfer extra rapidly, it’s probably that true diffusion will take years.

10. You may make a distinction in the best way we expertise AI.

And within the meantime, regardless of widespread narratives sounding like AI is one thing that occurs to us in an inevitable method, the best way we expertise ‘AI’ is just not like a prepare on rails with humanity tied to the prepare observe and awaiting the proverbial prepare wreck. 

It’s extra helpful to consider AI just like the early days of recent transportation itself. On one hand, now we have a way that it’s a elementary system that may form our lives far into the longer term. However however, it’s sobering to notice that whereas the primary fashionable automotive was invented round 1885, automotive door keys solely got here in 1908, the 3-point seat belt was solely invented in 1958, and worldwide street indicators solely turned standardised in 1968.

This time hole between the preliminary adoption of recent vehicles and having the efficient and widespread guidelines of the street is the place we’re at immediately for AI. 

We now have work to do — vehicles and their engines (AI functions and their fashions) must be examined, automotive locks (AI safety features) must be put in, drivers want seatbelts and driving licenses (customers want AI security and accreditation), and street indicators (AI rules) must be harmonized.

The long run is one that you may steer immediately.


All photos displayed above are solely for non-commercial illustrative functions. This text is written in a private capability and don’t symbolize the views of the organizations I work for or I’m affiliated with. No generative AI was used within the drafting of this text. Nonetheless, Grammarly was used as a spelling and grammar checker.  

References:

Randazzo, Steven, Hila Lifshitz, Katherine C. Kellogg, Fabrizio Dell’Acqua, Ethan Mollick, François Candelon, and Karim R. Lakhani. “Cyborgs, Centaurs and Self-Automators: The Three Modes of Human-GenAI Data Work and Their Implications for Skilling and the Way forward for Experience.” Harvard Enterprise College Working Paper, №26–036, December 2025.

Liu, Chaoran and Wang, Tong and Yang, S. Alex, Generative AI and Content material Homogenization: The Case of Digital Advertising and marketing (July 26, 2025). Obtainable at SSRN: https://ssrn.com/summary=5367123 or http://dx.doi.org/10.2139/ssrn.5367123

Patel, Jaisal & Chen, Yunzhe & He, Kaiwen & Wang, Keyi & Li, David & Xiao, Kairong & Liu, Xiao-Yang. (2025). Reasoning Fashions Ace the CFA Exams. 10.48550/arXiv.2512.08270.

Kasagga A, Sapkota A, Changaramkumarath G, Abucha JM, Wollel MM, Somannagari N, Husami MY, Hailu KT, Kasagga E. Efficiency of ChatGPT and Giant Language Fashions on Medical Licensing Exams Worldwide: A Systematic Evaluate and Community Meta-Evaluation With Meta-Regression. Cureus. 2025 Oct 10;17(10):e94300. doi: 10.7759/cureus.94300. PMID: 41230320; PMCID: PMC12603599.



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