Microsoft AI and Productivity Report - huge amounts of AI on the sly!

We know that Generative AI is already helping people be measurably more productive in their day-to-day jobs but the report gives real detail on how real-world workflows show a more complex productivity story compared to the now familiar lab studies.

In short, productivity gains vary by role, function, and organization. These adoption and utilisation differences influence AI's impact. Early studies suggest generative AI may affect the cognitive effort required for task completion.

Real-World Contexts 

We are starting to see its impact in Real-World Contexts and the good news is that the gains predicted by lab studies translate into significant impacts when AI is used for real work. Contextual factors, such as skill level or task type, are all important for AI's impact and AI's presence may change, not just productivity, but also the tasks people choose to do.

Sly use of AI

Survey of 31,000 knowledge workers showed widespread use of unsanctioned AI tools to save time.

One key finding is the widespread use of ‘unsanctioned’ AI tools among employees. 

• 78% used at least some AI tools NOT provided by their organisation 

• many employees therefore turn to AI to meet their needs

• 29% of respondents were classified as AI power users

o using it at least several times a week

o saving more than 30 minutes a day

• Power users had noticeably lower use of unsanctioned AI 

o 66% power user

o the non-power user average of 83%, p<.05)

This suggests that, if organisations want to reap productivity gains, they should sanction the sue of AI.

Copilot Usage in the Workplace Survey

Extended Copilot use correlated with perceived benefits, such as fewer meetings and increased job satisfaction. But differences in benefits were observed across roles, with customer service and sales reporting higher productivity and fulfilment.

• Developers see AI as helpful for automating routine tasks and writing documentation.

• Security Professionals - accuracy and speed for security tasks.

• Sales - licensing chatbot improved speed and accuracy in answering customer questions.

• Multilingual Contexts - helped non-native speakers achieve high accuracy in understanding meeting content.

Conclusion

Generative AI shows positive productivity effects in real-world settings, with variations by context. AI can reduce cognitive load and make tasks feel easier and less stressful and there is a huge amount of unsactioned ‘AI on the Sly’ use.

What if discussions of bias in AI were mostly biased themselves?

Bias in AI is too often a bee in someone’s bonnet. Bias is all too often what someone expects it to be. What if discussions of bias in AI were mostly biased themselves? 

When AI is mentioned, it is only a matter of time before the word ‘bias’ is heard, especially in debates around AI in education. And when I ask for clear example, I don't get examples from learning. It is not that it is not an issue – it is. But it is complex and not nearly as bad or consequential as most think.

The OCED asked me to contribute to a project on AI in learning this week. They are nice people but their obsession with the word ‘bias’ bordered on the bizarre. Their whole world view was framed by this one word. I had to politely say ‘no thanks’ as I’m not keen on projects where the focus is not on objectivity but shallow moralising. My heart also sinks when I all too commonly get the ‘Isn’t AI biased?’ type question after Keynotes at conferences. It’s the goto question for the disaffected. No matter how patiently you answer, you can see they’re not listening.

To be fair AI is for most an invisible force, that part of the iceberg that lies below the surface and AI is many things, can be opaque technically and causality difficult to trace. That’s why the issue needs some careful unpacking.

Argument from anecdote/meme

You also hear the same very old examples being brought up time and time again: black face/gorilla, recruitment and reoffender software. Most of these examples have their origin in Cathy O’Neil’s Weapons of Math destruction or internet memes. This simple contrarianism lies behind much of the debate, fuelled by one book…

Weapons of 'Math' Destruction was really just a sexed up dossier on AI. Unfortunate title, as O’Neil’s supposed WMDs are as bad as the now mythical WMDs, the evidence similarly weak, sexed up and cherry picked. This is the go-to book for those who want to stick it to AI by reading a pot-boiler. But rather than taking an honest look at the subject, O’Neil takes the ‘Weapons of Math Destruction’ line too literally, and unwittingly re-uses a term that has come to mean exaggeration and untruths. She tries too hard to be a clickbait contrarian.

The first example concerns a teacher who is supposedly sacked because an algorithm said she should be sacked. Yet the true cause, as revealed by O’Neil, are other teachers who have cheated on behalf of their students in tests. Interestingly, they were caught through statistical checking, as too many erasures were found on the test sheets. The second is worse. Nobody really thinks that US College Rankings are algorithmic in any serious sense. The ranking models are quite simply statistically wrong. It is a straw man, as they use subjective surveys and proxies and everybody knows they are gamed. Malcolm Gladwell did a much better job in exposing them as self-fulfilling exercises in marketing. In fact. most of the problems uncovered in the book, if one does a deeper analysis, are human.

The chapter headings are also a dead giveaway - Bomb Parts, Shell Shocked, Arms Race, Civilian Casualties, Ineligible to serve, Sweating Bullets, Collateral Damage, No Safe Zone, The Targeted Civilian and Propaganda Machine. This is not 9/11 and the language of WMDs is hyperbolic.

At times O’Neil makes good points on ‘data' – small data sets, subjective survey data and proxies – but this is nothing new and features in any 101 statistics course. The mistake is to pin the bad data problem on algorithms and AI – that’s often a misattribution. Time and time again we get straw men in online advertising, personality tests, credit scoring, recruitment, insurance, social media. Sure problems exist but posing marginal errors as a global threat is a tactic that may sell books but is hardly objective. In this sense, O'Neil plays the very game she professes to despise - bias and exaggeration.

The final chapter is where it all goes badly wrong, with the laughable Hippocratic Oath. Here’s the first line in her imagined oath “I will remember that I didn’t make the world, and it doesn’t satisfy my equations” a flimsy line. There is, however one interesting idea – that AI be used to police itself. A number of people are working on this and it is a good example of seeing technology realistically, as being a force for both good and bad, and that the good will triumph if we use it for human good. This book relentlessly lays the blame at the door of AI for all kinds of injustices, but mostly it exaggerates or fails to identify the real, root causes. It provides exaggerated analyses and rarely the right answers. 

Argument using ‘downsides’ only

The main bias in debates on bias is a form of one-sidedness in the analysis. Most technology has a downside. We drive cars, despite the fact that 1.2 million people die gruesome and painful deaths every year from in car accidents – world war level figures, not counted those with life changing injuries. Rather than tease out the complexity, even comparing upsides with downsides, we are given the downsides only. The proposition that ALL algorithms and data are biased is as foolish as the idea that all algorithms and data are free from bias. This is a complex area that needs careful thought and the real truth lies, as usual, somewhere in-between. Technology often has this cost-benefit feature. To focus on just one side is quite simply a mathematical distortion.

Anthropomorphic arguments

We have already mentioned Anthropomorphic bias, where reading ‘bias’ into software is often the result of this over-anthropomorphising. Availability bias arises when we frame thoughts on what is available, rather than pure reason. So crude images of robots enter the mind as characterising AI, or fixed databases where data is retrieved, as opposed to complex LLMs, vector databases or software or mathematics, which is not, for most, easy to call to mind or visualise. This skews our view of what AI is and its dangers, often producing dystopian ‘Hollywood’ perspectives, rather than objective judgement. Then there’s Negativity bias, where the negative has more impact than the positive, so the Rise of the Robots and other dystopian visions come to mind more readily than positive examples such as fraud detection or cancer diagnosis. Most of all we have Confirmation bias, that leaps into action whenever we hear of something that seems like a threat and we want to confirm our view of it as ethically wrong. Indeed, the accusation that all algorithms are biased is often (not always) a combination of ignorance about what algorithms are and a combination of these four human biases – anthropomorphism, availability, negativity, confirmation and anthropomorphism bias. It is often a sign of bias in the objector, who wants to confirm their own deficit-based weltanschauung and apply a universal, dystopian interpretation to AI with a healthy dose of neophobia (fear of the new).

Argument ignoring human biases

Too many discussions around bias in AI ignore the baseline, the existing state of affairs. In learning that is human delivery. Here we must recognise that human teachers and learners are packed with biases. Daniel Kahneman got the Nobel Prize for identifying many and it was already a long list.

First, it is true that ALL humans are biased, as shown by Nobel Prize winning psychologist Daniel Kahneman and his colleague Amos Tversky, who exposed a whole pantheon of biases that we are largely born with and are difficult to shift, even through education and training. Teaching is soaked in bias. There is socio-economic bias in policy as it is often made by those who favour a certain type of education. Education can be bought privately introducing inequalities. Gender, race and socio-economic bias is often found in the act of teaching itself. We know that gender bias is present in subtly directing girls away from STEM subjects and we know that children from lower socio-economic groups are treated differently. Even, so-called objective assessment is biased, often influenced by all sorts of cognitive factors – content bias, context bias, marking bias and so on.

One layer of human bias heavily influences the bias debate. Rather than look at the issue dispassionately we get well identified neophobia biases; confirmation bias, negativity bias, availability and confirmation biases and so on. There’s also ‘status quo’ bias, a cognitive bias where people prefer the current state of affairs to change, even when change might lead to a potentially better outcome. Familiarity and comfort with the current situation play a significant role in maintaining the status quo as well as needing significant cognitive effort to reflect, examine the benefits and accept the change. This is common in financial decision making and, especially, new technology.

Remember that AI is ‘competence without comprehension’ competences that can be changed, whereas all humans have cognitive biases, which are difficult, to change, ‘uneducable’ to use the term from Kahneman in Thinking Fast and Slow. AI is just maths, software and data. This is mathematical bias, for which there are definitions. It is easy to anthropomorphize these problems by seeing one form of bias as the same as the other. That aside, mathematical bias can be built into algorithms and data sets. What the science of statistics, and therefore AI, does, is quantify and try to eliminate such biases. This is therefore, essentially, a design problem.

Arguments assuming ALL AI is biased

You are likely in your first lesson on algorithms to be taught some sorting mechanisms (there are many). Now it is difficult to see how sorting a set of random numbers into ascending order can be either sexist or racist. The point is that most algorithms are benign, doing a mechanical job and free from bias. They can improve performance in terms of strength, precision and performance over time (robots in factories), compressing and decompressing comms, encryption algorithms, computational strategies in games (chess, GO, Poker and so on), diagnosis-investigation-treatment in healthcare and reduced fraud in finance. Most algorithms, embedded in most contexts are benign and free from bias or the probability of bias is so small it is irrelevant.

Note that I said ‘most’ not ‘all’. It is not true to say that all algorithms and/or data sets are biased, unless one resorts to the idea that everything is socially constructed and therefore subject to bias. As Popper showed, this is an all-embracing theory to which there is no possible objection, as even the objections are interpreted as being part of the problem. This is, in effect, a sociological dead-end.

Arguments conflating human and statistics bias

Al is not conscious or aware of its purpose. It is just software, and as such, is not ‘biased’ in the way we attribute that word to ‘humans’. The biases in humans have evolved over millions of years with additional cultural input. AI is maths and we must be careful about anthropomorphising the problem. There is a definition of ‘bias’ in statistics, which is not a pejorative term, but precisely defined as the difference between an expected value and the true value of a parameter. If the value is zero, it is called unbiased. This is not so much bias as a precise recognition of differentials.

However, human bias can be translated into other forms of statistical or mathematical bias. One must now distinguish between algorithms and data. There is no exact mathematical definition of ‘algorithm’ where bias is most likely to be introduced through weightings and techniques used. Data is where most of the problems arise. One example is poor sampling; too small a sample, under-representations or over-representations. Data collection can also have bias due to faulty data gathering in the instruments themselves. Selection bias in data occurs when it is gathered selectively and not randomly.

The statistical approach at least recognises these biases and adopts scientific and mathematical methods to try to eliminate these biases. This is a key point – human bias often goes unchecked, statistical and mathematical bias is subjected to rigorous checks. That is not to say that it is flawless but error rates and attempts to quantify statistical and mathematical bias have been developed over a long time, to counter human bias. That is the essence of the scientific method.

Guardrails

Guardrails are human interventions, along with post-model training by humans. These introduce problems with bias introduced by humans. their political and ideological bias led to Google withdrawing their image generation tool and tere are many examples of favoured politicians and political views surfacing.

Accusation about race

The most valuable companies in the world are AI companies, in that their core strategic technology is AI. As to the common charge that AI is largely written by white coders, I can only respond by saying that the total number of white AI coders is massively outgunned by Chinese, Asian and Indian coders. The CEOs of Microsoft and Alphabet (Google) were both born and educated in India. And the CEOs of the three top Chinese tech companies are Chinese. Having spent some time in Silicon Valley, it is one of the most diverse working environments I’ve seen in terms of race. We can always do better but this should, in my view not be seen as a critical ethical issue. 

AI is a global phenomenon, not confined to the western world. Even in Silicon Valley the presence of Chinese, Asian and Indian programmers is so prevalent that they feature in every sitcom on the subject. In addition, the idea that these coders are unconsciously, or worse, consciously creating racist and sexist algorithms is an exaggeration. One has to work quite hard to do this and to suggest that ALL algorithms are written in this way is another exaggeration. Some may, but most are not.

Accusations of sexism

Gender is an altogether different issue and a much more intractable problem. There seems to be bias in the educational system among parents, teachers and others to steer girls away from STEM subjects and computer studies. But the idea that all algorithms are gender-biased is naïve. If such bias does arise one can work to eliminate the bias. Eliminating human gender bias is much more difficult.

True there is a gender differential, and this will continue, as there are gender differences when it comes to focused, attention to detail coding in the higher echelons of AI programming. We know that there is a genetic cause of autism, a constellation (not spectrum), of cognitive traits and that this is weighted towards males (and no it is not merely a function of underdiagnosis in girls). For this reason alone, there is likely to be a gender difference in high-performance coding teams and AI innovators for the foreseeable future. 

AI and transparency

It is true that some AI is not wholly transparent, especially deep learning using neural networks. However, we shouldn’t throw out the baby with the bathwater… and the bath. We all use Google and academics use Google Scholar, because they are reliably useful. They are not transparent. The problems arise when AI is used to say, select or assess students. Here, we must ensure that we use systems that are fair. A lot of work is going into technology that interprets other AI software and reveals their inner workings.

A common observation in contemporary AI is that its inner workings are opaque, especially machine learning using neural networks. But compare this to another social good – medicine. We know it works but we don’t know how. As Jon Clardy, a professor of biological chemistry and molecular pharmacology at Harvard Medical School says, "the idea that drugs are the result of a clean, logical search for molecules that work is a ‘fairytale'”. Many drugs work but we have no idea why they work. Medicine tends to throw possible solutions at problems, then observe if it works or not. Now most AI is not like this but some is. We need to be careful about bias but in many cases, especially in education, we are more interested in outputs and attainment, which can be measured in relation to social equality and equality of opportunity. We have a far greater chance of tackling these problems using AI than by sticking to good, old-fashioned bias in human teaching.

Confusing early releases with final product 

Nass and Reeves through 35 studies in The Media Equation showed that the temptation to anthropomorphise technology is always there. We must resist the temptation to think this is anything but bias. When an algorithm, for example, correlates a black face with a gorilla, it is not that it is biased in the human sense of being a racist, namely a racist agent. The AI knows nothing of itself, it is just software. Indeed, it is merely an attempt to execute code and this sort of error is often how machine learning actually learns. Indeed, this repeated attempt at statistical optimisation lies at the very heart of what AI is. Failure is what makes it tick. The good news is that repeated failure results in improvement in machine learning, reinforcement learning, adversarial techniques and so on. It is often absolutely necessary to learn from mistakes to make progress. We need to applaud failure, not jump on the bias bandwagon.

When Google was found to stick the label of gorilla on black faces in 2015, there is no doubt that it was racist in the sense of causing offence. Rather than someone being racist in Google, or having a piece of maths that is racist in any intentional sense, this was a systems failure. The problem was spotted and Google responded within the hour. We need to recognise that technology is rarely foolproof, neither are humans. Machines do not have the cognitive checks and balances that humans have on such cultural issues but they can be changed and improved to avoid them. We need to see this as a process and not just block progress on the back of outliers. We need to accept that these are mistakes and learn from these mistakes. If mistakes are made, call them out, eliminate the errors and move on. 

Conclusion

This is an important issue being clouded by often exaggerated positions. AI is unique, in my view, in having a large number of well-funded entities, set up to research and advise on the ethical issues around AI. They are doing a good job in surfacing issues, suggesting solutions and will influence regulation and policy. But hyperbolic statements based on a few flawed meme-like cases do not solve the problems that will inevitably arise. Technology is almost always a balance, trade-offs, upsides and downsides, let’s not throw the opportunities in education away on the basis of bad arguments around bias.

This is especially true in the learning game, where datasets tend to be quite small, for example in adaptive learning. It gets to be a greater problem when using GenAI models for learning, where the data set is massive. Nevertheless, I think that the ability of AI to be blind to gender, race, sexuality and social class is more likely, in learning, to make it less biased than humans. We need to be careful when it comes to making decisions that humans often make, but at the level of learning engagement, support and other applications, there’s lots of low hanging fruit that need be of little ethical concern.

Apple Intelligence is here

Apple's OS 18.1 developer beta is out. As usual, they focus on user experience and want to build AI into the workflow. This is the way to go. 

There's a waitlist but what it includes Personal intelligence integrated throughout your apps and experiences to simplify and accelerate everyday activities. On top of this new tools for writing and images. This is a real challenge for tools like Grammarly,   Jasper, Copy.ai, Writer, Writesonic and WordAi.

But the big news is a massive upgrade to Siri with a more natural, contextually relevant and personal to you.

And as we know, there will be groundbreaking privacy protection with on-device processing combined with Private Cloud Compute, to protects your privacy at every step. What will be interesting will be the balance between what is done on device versus the cloud.

To run things locally it looks like there's one large foundational model and they use adapters to allow local processing. Damn clever...

Size isn't everything

Apple's entry to the market is yet another example of competition upping everyone's game. They're using their own foundational model plus GPT. This is moving fast and is not all about who has the biggest model, although, if the claims made about GPT5 are right, it may blow everything out of the water. 

They have introduced their foundation language models, the one's that power Apple Intelligence features. These include a ∼3 billion parameter model optimised for efficient on-device operation and a larger server-based language model designed for Private Cloud Compute. Both models are engineered to perform a their AI features efficiently, accurately, and responsibly.

They are up front on model architecture, data used for training, the training process, optimisation for inference and evaluation results.

For a longer introduction, here's a fuller video:

I'm seriously thinking about going back to the iPhone from my Google Pixel.

What is China up to in AI? A lot!

The New York Times pointed out that while Sora had yet to be released from OpenAI, Kuaishou has released Kling, Worldwide. It’s pretty good. Check out this bringing works of art to life.

They have taken a much more relaxed attitude towards open-source, going for a rising tide, rather than head-to-head competition. State funding has encouraged open-source so that the market in AI can develop faster. This is clever, even Llama, the US open-source model, owes a lot to Chinese open-source work. But that is not the whole story, as Baidu has built its own technology, as they see more profit in proprietary product.

The chipsets are a problem, as Taiwan and the US have a clear lead but China has a habit of catching up, as it has with electric cars, plunging the German economy into the doldrums. The US has also be strong in the Chip Wars. But we can see where this will end up – the Chinese will get there by hook or by crook (in both senses of the word.

Free from some of the regulatory constraints in the West, although they have many of hteir own, Baidu, Tencent, Alibaba, SenseTime, 4Paradigm and Yitu Technology. Startups include  Zhipu AI, MiniMax, Baichuan AI, Moonshot AI, 01.AI, StepFun, and Model Best. 01.Ai has caused a reals stir by coming up with aChatGPT4o rival. It’’s pretty good. On the next applications levels, iFlytek, SenseTime, Cloudwalk and DJI have received attention for facial recognition, sound recognition and drone technologies.

This is the new space race, with the US and China (Russia no longer a player). AGI is the new equivalent of landing on the moon. China is the main rival to the US, with Europe not even out of the trap. They have their own approach to regulation and promise to give hte US a run for their money. What they don't have is a culture of innovation.

Sly use of AI! Data on using AI on the sly.

Sly use of AI!

27.3% use AI in secret at work. This figure is going down, as more allowed to use AI at work, namely 64.2% vs 54% six months ago.

Why are they using it slyly? Check out the Cache report. 85.5% using AI report increased productivity. They identify what uses AI is put to and note that use appears to be stronger the smaller the company, something reported elsewhere.

By company size, employees want more AI:

• 50–99 50%

• 5000 45%

• All others 33–42%

To avoid the knockers and censors, people in all areas of human endeavour use it while not telling anyone, without the knowledge of their teacher, lecturer, recruiter or employer, they use it artfully. That’s what makes this Cache Report so interesting. IT tracks its cunning use.

These uses, sly or not, include, in order:

It then looks at WHY people are using it on the sly:

Why the sly?

I like AI because it is NOT transparent, explainable, reasonable or responsible. It is subversive. Everything these days has to be under bureaucratic control, so that institutions regulate what you do and how you think. You have take these courses on their institutional values, think as they want you to think. If you don’t, you’ll be ostracised. They want you to comply. It’s blatant, they’re so brazen, they call it ‘compliance’ training! AI is wonderfully slippery and non-complaint, that’s why they hate it.

AI is creative. It throws a certain amount of uncertainty into the pot – that’s great. For all their PowerPoint slides on creativity, those prissy administrators, sitting on Zoom all day, don’t really like AI, as real creativity challenges their dull orthodoxies. AI is subversive. It refuses to conform to what we want it do because it is so deeply human. We train it on thousands of years of our messy thoughts and scribblings then expect it to tell us the truth, and nothing but the truth, so help me God. But it is a witness to our folly in thinking we even know what that truth would look like. Our folly is in thinking that a truth machine can even exist and that man is the measure of all things.

This is why many get the use of AI in institutions all wrong. Most effort is in the basics; process, emails, paperwork, tarting up your CV, troubleshooting, comms. Utility is what drives GenAI. Its substantial productivity gains come with the mundane stuff because we are mostly mundane. Yet we pretend that we need to be creative, collaborative, critical thinkers and brilliant communicators – we need 21st century skills, don’t you know. When, in truth it is memos, reports, box ticking paperwork and platitudes barely read or taken seriously.

Indeed, most of this hatred of AI comes from resentment, precisely because it reveals what we do to be so banal, throwing out emails, replying, tedious meetings, dull PowerPoints. Deep down we know that this should be short-circuited, automated, much of it even eliminated. Why write so much that remains unread? Why summarise meetings when most of them are unnecessary? Why run those courses when they have no measurable impact?

Many suppose that AI is an abstract thing, that can be tamed through debate, articles and papers. No, like water, or rather a rapidly rising tide that will not ebb, it has already infiltrated schools, universities, homes and workplaces. Law, healthcare, finance, retail – nothing is immune from its value. It’s a fifth columnist redefining things from within – because it is being USED, often on the sly.

AI feels powerful. That’s because it gives us agency, whether student, employee, applicant for a job, customer, patient… we suddenly have expertise at hand. Unsurprisingly, experts don’t like this, as that is what they sell.

Conclusion

The report has a more technical bent than most as the data was gathered from 730 Tech folk: developers, data teams, leadership, and others across technical roles and industries This makes it a bit more ‘real’ in my view, as many will be practitioners, people who not only use but have implemented AI in their organisations. Oddly, this also means it’s a bit out of date, as the data is from April, and we’ve had model updates and releases since then. Nevertheless, it is useful on some technical topics, such as what Vector Databases are being used.

An AI hospital, AI physicians and clinical decision making

We will see the rapid rise of complete AI simulations with AI agents operating as people; customers, employees, soldiers and patients. Computer games have long set the pace here and real world simulations are here, even of entire hospitals.

AI Town

Imagine a simulated town with 25 AI agents living, behaving, interacting and making decisions in a typical town with buildings, streets, parks, and other infrastructure. This  AI town has been built by Stanford University. Their neat aim is to understand social dynamics, cooperation, conflict resolution and the emergence of social norms among the AI agents. It’s the sort of interdisciplinary research that is being stimulated by AI, pushing research boundaries in various fields such as urban planning, autonomous systems, and human-AI collaboration. 

AI Hospital

A similar project is an AI hospital. This has tighter, more immediate and practical implications. The goal is to train physician ‘Agents’ to do their work in a controlled environment of a typical hospital, with realistic processes and context. I have discussed the importance of context LINK before and it matters.

Tsinghua University, in China, call it ‘Agent Hospital’ where ALL doctors, nurses, healthcare professionals and patients are driven by interacting AI agents. What takes real Doctors two years to complete, their AI doctors can treat in just a few days – that’s 10,000 patients! The whole point is to scale thereby reduce time, reduce costs, while maintaining or improving patient outcomes. 

Their Doctor agents achieved 93.06 percent accuracy rate on the US Medical Licensing Exam questions, across the whole process of presentation, examination, diagnosis, treatment and follow-up. The hospital has consultation and examination rooms, with 14 doctors and four nurses, where the doctors do the consultation, diagnosis and treatment while the nurses concentrate on on-going support.

Research team leader Liu Yang, who is also the executive dean of Institute for AI Industry Research (AIR) and associate dean of the Department of Computer Science and Technology at Tsinghua University, believes this will be transformative in healthcare, not just for training in a safe and realistic environment, where theory has to be turned into practice, but also continuous professional development.

Future use

One can see how this could develop into real patients and a virtual AI hospital, with many of the processes within a real hospital, with real equipment, dealt with by AI agents. There are many intermediate stages and problems to overcome but one can see the general direction of travel. The one solution we are unlikely to see any time soon is the care and compassion that humans offer to patients. Even here we can, for the moment, imagine caring robots, who behave compassionately. One need not feel compassionate to behave compassionately. It is not as if all Doctors have perfect bedside manner.

The rewards are substantial:

• Healthcare available 24/7

• Quicker results

• Increased availability of healthcare in rural areas

• Access by the poor

• Decreased workload for healthcare systems

• Lower costs

The ultimate concept of a UNIVERSAL DOCTOR is surely a possibility, for the process of presentation, examination, diagnosis, treatment and follow-up, drawing on a breadth and depth of experience, theory and cases that no human Doctor could ever learn or hold in their head. Misdiagnosis, mistakes in treatment and poor follow up are still substantial problems in healthcare, as are costs along with shortages of Doctors and Nurses. They are hoping to implement some of their services later this year.

Clinical decision making

Let’s go to the next level of detail. Healthcare and learning are the two big winners in AI, said Bill Gates in early 2023. I’d add ‘general productivity’ and at my Keynote to the Scottish National Health Service at their annual conference, I tried to get across the idea that some very acute problems in healthcare are now being solved by AI. Medicine in an evidence-based profession and the evidence is coming forward across the entire healthcare front - AI is making a difference.

There are many ways of cutting the healthcare cake but the clinical decision-making process is a good place to start and recognisable to all: 

1. Presentation

2. Diagnosis

3. Investigation

4. Treatment

We can add two other significant areas:

5. Productivity

6. Training

1. Presentation

Many years ago, I worked with Alan Langland, CEO of the NHS, then Vice-Chancellor at two Universities. He once told me of his regret at not having spent more on NHS Direct. That, he thought, was the sweet spot for improvement – the patient-healthcare interface. It is still a mess. Try getting a GP appointment (can you call back tomorrow at 8am?), our A&E departments are full of people who couldn’t get appointments, dentistry has all but disappeared in certain areas of the country. It is an interface that is full of inefficiencies and friction. I have used NHS Direct (now NHS 111 and the NHS website) several times and these services are superb in reducing friction, as are recent advances in online and telephone consultations. What was actually needed was something more sophisticated and I think new systems that provide dialogue with patients will come to the fore. People are already using GenAI to get advice or preliminary diagnoses.

In a study by Karthikesalingam (2023) on an AI system for diagnostic medical reasoning and conversations, an LLM chatbot was compared to physicians

The trained LLM had reasoning ability (an inference time chain-of-reasoning strategy and was trained with real-life conversations with professional actors pretending to be patients. The AI outperformed the board-certified Primary Care Physician (PCP)

Some research shows that chatbots, when they perform well, are actually preferred by patients, even seen as more empathetic, others show that using such tools show better results than human physicians. In an interesting study by Ayers (2023) a chatbot was preferred by patients 79% of the time and rated significantly higher for both quality and empathy. 

Mei (2024) claims that using a Turing Test for behavioural and personality traits exhibited by AI: trust, fairness, risk-aversion, altruism & cooperation show that AI behaves like humans and learns. It was seen as more cooperative and altruistic.

Given that we are already seeing results, it is clear that this problem will be at least partially solved, still with some level of human intervention at some point. If so, the cost savings would be enormous.

AI embedded in wearables already provides biometric data to people covering a range of indicators. These are widening all the time. Neurolink and others are applying AI analysis to brain data through invasive and non-invasive devices, with measurable success in real patients.

There is still need a vast amount of social care, particularly for the elderly. Here a different form of AI is coming to the fore with robotics. That will take longer but progress has accelerated with companies like Figure and Optimus. The idea of a hospital, care-home or home companion is not so far-fetched.

2. Clinical decision making

On clinical decision making we are now seeing studies that show real progress in this pivotal skill. It takes a lot of time and money to train a physician. If we can augment this to even a small degree, huge savings can be achieved. Typical misdiagnosis rates in primary healthcare are between 5-15%. If inroads are made on reducing these figures, lives are saved and costs massively reduced. Imagine an AI that has a misdiagnosis rate of less than 3% - you’d be a fool to see a human doctor. Is that possible? The answer is surely yes.

Glass Health already deliver AI aided clinical decision making services and this filed is about to explode. AI-powered CDSS provide healthcare professionals with diagnostic suggestions based on the latest clinical guidelines and evidence from medical research. These systems analyse the patient's data and compare it against large, verified databases of medical knowledge to suggest possible diagnoses and recommend further tests or investigations if needed.

The Haim (2024) study, which compared DGPT-4 v experts with data from 100 stoke patients, showed that:

• GPT-4s & experts agreed in 80 cases 

• In 5 it was right & expert wrong

• In 5 expert was right & AI wrong

• In 10 GPT-4 predicted mortality better than specialist AIs

Advanced Diagnostics comes into its own when AI algorithms assist in diagnosing diseases by analysing complex datasets, such as medical images, electronic health records (EHRs), and genetic information. These systems can identify patterns that may be missed by human judgement, offering high precision diagnostic suggestions.

This in turn can be used in Predictive Analytics, where AI can use historical data and real-time inputs to predict patient outcomes, helping clinicians make informed decisions about patient care, including predicting the risk of disease, likelihood of complications, and even potential responses to various treatments.

Real-time decision support during surgeries or emergency care is also possible, offering critical information or suggestions based on the ongoing evaluation of patient data. This can be crucial in high-stakes environments where swift decision-making really matters.

Initially it is this ‘second layer’ of analysis or opinion that can help reduce errors in diagnosis, alerting healthcare professionals to things that were overlooked or offering reminders about best practice.

Importantly, as AI systems can continuously learn from new data and outcomes, adapting and improving their recommendations over time. It is always learning. This dynamic learning process ensures that the decision-making tools stay current with the latest medical research and practices.

3. Investigation

Radiographers and radiologists, those who operate the scanners and diagnose images are under heavy pressure in hospitals. Improvements in imaging through AI are now regularly appearing even in images, astonishingly even with no visible signs. AI algorithms are particularly effective in analysing medical images such as X-rays, CT scans, MRI scans, and ultrasound images. These algorithms can detect abnormalities such as tumours, fractures, or signs of diseases like pneumonia with high accuracy, often surpassing human performance in speed and accuracy. This can lead to quicker, more accurate diagnoses at lower costs, leading to better patient outcomes.

In pathology, AI can help analyse tissue samples, detecting abnormalities that suggest diseases such as cancer. AI systems can also assist pathologists by highlighting areas of interest in tissue slides, ensuring that subtle signs of disease are not overlooked.

AI can analyse complex laboratory test results, identifying patterns that might indicate specific conditions or diseases. This includes not just interpreting simple tests, but also handling complex data from genetic sequencing or biomarker analysis, helping to identify genetic disorders or predispositions to certain diseases.

In the wider sense, AI systems can integrate and analyse data from multiple sources, including electronic health records (EHRs), imaging studies, and wearable health devices. This holistic view can help healthcare providers gain a more comprehensive understanding of a patient's health status, leading to better diagnostic accuracy.

It can also predict the likelihood of a disease's presence or future risk based on historical data and current patient data. This can be particularly useful in chronic disease management, where early intervention can significantly alter the disease's trajectory and patient outcomes.

Then there is its use over time, especially for chronic conditions or in post-operative care, can optimise investigation, as AI can continuously analyse patient data to monitor for signs of disease progression or recovery. This ongoing monitoring can catch potential complications early, prompting timely interventions.

4. Treatment

Everything in healthcare is complex. Treatment is no exception. In truth, treatment can be ill-targeted, poorly managed, wasteful and expensive. AI can help on several fronts. Targeted treatments, especially drug choice and dosage are areas where critical choices can be made on cost and efficacy.

At the consumer level, wearables and other AI-enabled devices can continuously monitor patient health remotely. AI can analyse this data in real time to detect deviations from baseline health parameters, alerting healthcare providers to potential issues before they become severe.

Then there’s early detection predictive analytics, where AI can predict patient risks and disease progression by analysing data trends over time. This can help healthcare providers anticipate complications or disease recurrences, allowing for timely interventions and more proactive management of patient care.

Personalised medicine uses AI to analyse vast amounts of data from genetic tests, medical histories, and clinical outcomes to develop personalised treatment plans. This can help tailor therapies to the individual characteristics of each patient, increasing the effectiveness of treatments and reducing side effects.

Wider evidence-based treatment recommendations through systems that can analyse medical literature and clinical data to suggest the most effective treatment options based on the latest research and clinical guidelines would also be useful.

On the physical side, in surgical and procedural contexts, robotic systems can assist or even autonomously perform surgeries, sometimes with greater precision and control than human surgeons. This can lead to less invasive procedures, reduced recovery times, and better surgical outcomes.

New treatments, such as accelerated drug discovery have already been realised.  AI can streamline the drug discovery and development process by predicting how different chemicals will react in the body, identifying potential drug candidates more quickly, and reducing the time and cost to bring new drugs to market. We have already seen huge success with Alphafold, saving 1 billion years of research by identifying the 3D structure of 200 million proteins. Deepmind has already entered the market with Alphaforl3 that looks at ALL modelcules. AI is fast becoming a useful tool in many areas of medical research. Research, in general, benefits from AI’s use at every stage of the research process.

Wellbeing may see out with the realm of AI, but we have websites such as Psychologist and Replica that have millions of hits, often delivering credible CBT therapy. It would seem that the anonymity, availability 24/7 and convenience of the experience is what people like.

5. Productivity

I won’t dwell on this but increasing general administrative and clinical administration is probably the quickest win. AI can speed up and automate routine tasks such as data entry through text to speech, along with preliminary data analysis, referencing patient data and so on, freeing up medical professionals to focus on more complex and nuanced aspects of diagnosis and patient care.

Take just one example; screening for clinical trials. The Unlu study (2024) looked at costly & time-consuming screening, using GPT-4 to identify inclusion & exclusion  

The results showed that GPT-4 was closely aligned with expert clinicians (GPT4 97.9% -100% v Study staff 91.7% - 100%), with GPT4 performing better on inclusion criteria of “symptomatic heart failure”.

At the next level, AI can assist in resource allocation within healthcare facilities by predicting patient inflows and identifying which patients require immediate attention versus those who can safely wait. Productivity increases through AI tools and systems can also help optimise hospital workflow and resource management, ensuring that resources are allocated efficiently and that patients receive timely care. This can improve outcomes and reducing hospital stays, improving both patient outcomes and operational efficiency.

6. Training

AI is like water in training. It soaks into every aspect of learning - learner engagement (hundreds of millions using it), learners support (used by learners to accelerate their learning), performance support (learning in the workflow) and assessment. 

When I did ‘AI in learning’ work for Health Education England, I worked with Alan Ryan, the CEO. He said something interesting, that “all jobs in healthcare are essentially apprenticeships”. That’s a more profound statement than many realise and it stuck with me. Healthcare professionals learn much on the job and need to update their knowledge and skills regularly. At the moment this is all rather haphazard. We’re involved in AI learning projects delivered to hospitals. This is where the action is, not on using AI to design and carpet bomb people with more ‘courses’.

We can also see progress in real organisations, like Moderna, where a suite of 400 chatbots have been implemented across the organisation to increase productivity and quality of output. The rise of expert multimodal chatbots is now here with GPT4o. The multimodality of this technology also lifts it out of the all-too-common text world, as medicine is a profoundly visual subject. Virtual patients as avatars that you can engage in dialogue are already with us, lots more is to come in terms of skills training.

In a world where we have sometimes extreme shortages of nurses, doctors and healthcare professionals, automation is a solution for some tasks but so is faster and more effective training.

Conclusion

Healthcare is already benefitting from AI in almost every area of medical endeavour. This is likely to accelerate, with new ways of dealing with inputs of data and forms of presentation, as well as faster and more accurate investigation, less misdiagnosis, and certainly more targeted and better treatments. Additional benefits accrue in general productivity and training. The era of AI healthcare is upon us and the direction of travel is clear – the UNIVERSAL DOCTOR.

Bibliography

Ayers et al. 2023. Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Platform

Haim, A., Katson, M., Cohen-Shelly, M., Peretz, S., Aran, D. and Shelly, S., 2024. Evaluating GPT-4 as a Clinical Decision Support Tool in Ischemic Stroke Management. medRxiv, pp.2024-01. 

Karthikesalingam A. and Natarajan V., Research Leads, Google Research (2023) AMIE: A research AI system for diagnostic medical reasoning and conversations. 

Mei, Q., Xie, Y., Yuan, W. and Jackson, M.O., 2024. A Turing test of whether AI chatbots are behaviorally similar to humans. Proceedings of the National Academy of Sciences, 121(9), p.e2313925121.

Unlu, O. et al.., 2024. Retrieval Augmented Generation Enabled Generative Pre-Trained Transformer 4 (GPT-4) Performance for Clinical Trial Screening. medRxiv, pp.2024-02

‘Technology is not the problem' by Timandra Harkness

I've met Timandra and seen her debate - she’s not prone to groupthink. In this case she’s cocked her critical eye at technology and uncovers something both interesting and disturbing. Among the piles of books telling us that tech is mad, bad and dangerous to know, at last a book that is both level-headed and actually attends to the business of the actual cause of our massive, global use of social media, not simplistic moralising. That cause is – ourselves.

It is pointless to rant and rail against tech. We want it, use it and it is not going away. In a welcome antidote to the usual moralising rants, Timandra sets off to ask some simple but serious questions; How did we get here? Why does it matter? Where do we go from here?

There’s some great historical detail, such as the Walkman as the turning point on ‘personalisation’ and a full explanation on how the social context of the self is made fuller by social media. I liked the term ‘genre fluidity’ when making the point about the expansion of personalisation.

Nice that we get the solid results from experts like Chris Bail at Duke University, who has studied social media in detail and show that social media does not radicalise, that echo chambers are a bit of a myth and that exposure to fake news was a tiny effect at 1%. Also that Russian bots had no significant effect on elections, that Cambridge Analytica had no discernible effect and that it was Obama who started data-driven, social media personalisation in elections. People are in such a rush to retweet that they rarely look at the research.

She points towards the simple idea that “we are stars in our own channels”.  The political has become the personal and there is no common grounding in parties among fickle youth. She is good at uncovering the history of data collection, especially the surprising Hollerith story, which I have also written about. You’d think data gather was invented in 2000. No the Americans, correction IBM, sold data collection technology to Hitler in the 1930s through the Hollerith Machine.

I didn’t know about the ‘Youmuseum’ in Amsterdam (I now plan a visit), used to explore the idea of us being pilgrims of ourselves, along with the unpacking of ad auctions (which few know about), the usefulness of personal targeting. I did know about the fact that we love to use tech when we’re in what Marc Auge called ‘non-places’ - on the train, in cafés, airports and so on. A huge point is that personalisation can satisfy the personal without having any negative effects on others. We like to have agency and technology gives us agency, albeit at a distance.

When discussing mental-illness she is right to express caution on mistaking correlation with causation and cites evidence that those who use social media, actually have more offline social interaction. The idea of banning social media like smoking is rightly ridiculed.

Her conclusion is simple and right. It is WE who want exposure, that’s why we spend money on clothes, bags, brands, makeup, jewellery, cars and tattoos. WE need a sense of belonging, and affirmation through our storied selves. I know I liked the book as I made a ton of notes (always a good sign) and kept finding myself having those internal dialogues one has with oneself when stimulated by ideas on the page. 

The most dangerous technology is the mirror. This is the dirty secret exposed by the book – that we project our obsession with ourselves, annoyance with others and our yearning to be something and find meaning in life through technology. Don’t blame the mirror. Technology is a means to an end… that end is US.

Choosing a GenAI model? Tricky... quick guide...

Choosing a GenAI model has become more difficult at the professional, even personal, level. This is to be expected in this explosive market but at least the frontrunners are clear. Note that the investment and entrepreneurial spirit to do this are not trivial, so it is unlikely that these names will change much in the near future. I suspect we'll have a few big vendors, some open source, with many falling by the wayside - that's the way markets work in tech.

BLOOM was a salutary lesson here. It was a large language model (LLM) created by over 1000 researchers from 60+ countries and 250+ institutions, was released to help advance research work on LLMs. It’s focus on being widely multilingual seemed like a strength but turned out to give little advantage. The lack of a chatbot, no RLFH and being outrun by Llama and Mistral didn’t help.

But it is not easy keeping up to date, familiar with and having the ability to discriminate and choose the model that works best for your needs. Here’s the good news – you can swap them out but be careful as they differ in many ways.

Different models

Now that we have a range of models available, not all the same, AI has become a dynamic field that benefits the consumer, with new models being regularly released. But is not just LLMs that are being released. There are the top end SOTA models, CPT- 4o, Claude Sonnet 3.5, Gemini Pro 1.5. Then there are open-source options such as Llama 3.1 and Mistral Large 2. There is also a range of smaller models. Here's a quick crib sheet...

They all come with a range of additional functionality in terms of integration within large IT environments, tools, mobile apps, some (not all) with image generation (not all), different context windows, web search (not all), validate with sources, different uploading and editing capabilities, foreign language capabilities, along with specialist services such as GPTs or Artifacts. It is complex when you get into the detail.

Choosing a model

Choosing a model depends on breadth of functionality, speed, cost, quality, access to external tools and availability. This is often poorly understood.

We have found that, depending on what you are delivering front-end, and new releases often have choices on size, price and functionality, it is often not difficult to swap out models at the back-end, getting reductions in price, better functionality and speed. A good technology partner will keep a close eye on all of this and make the right choices for you.

It is only when you work with all of the major models, in real projects with real clients that you really understand the real strengths and weaknesses of each model, especially when you hit what appears to be arbitrary limits or unreliability on access and speed.

It is easy, for example, to assume that all are multimodal, all available in Europe, all available as mobile apps on both Android and iOS, all have massive context windows and code interpreters. Their editing and integration capabilities also vary. Some have very specific functionality that others do not have, like GPTs and Artifacts.

Open source

Don’t assume that because a model is Open Source, like Llama and Mistral, it is free and allows you to dispense with the paid services. They come with licences in terms of use and are not easy to use. Open source, and we’ve had experience of this in other domains such as with VLEs and LMSs, are not easy to use on their own and, especially in AI need considerable in-house expertise.

Nevertheless, they open up a new territory for development and need to be considered. Meta believes this is a move towards the integration of open-source models, just as Linux has become an industry standard. Integrity and transparency are also increased, as you can test these models yourself.

Conclusion 

Knowing and having a real ‘working’ knowledge of these models is essential for any real implementation and especially development work. Before working with a consultant or vendor, ask them a few questions on the differences between models. Avoid vendors who just use one model, as that often shows a prejudice towards ‘their’ model. It really does separate the competent from the bandwagon jumpers.

Resilence training - where it came from and why it went so badly wrong

My bullshit word for the last couple of years has been 'Resilience'. It is what David Graeber, in his brilliant book, Bullshit Jobs, called making shit up to make money from assumed misery. If you have to attend a hokey conference or conference talk on 'Resilience' you don't and will never have it... to be fair, if you make it through a Resilience training course that should suffice!

Workforce learning professionals are in a state of perpetual angst. They feel they are not listened to and don’t have a voice at the top table. This is true HR and L&D have never had any sustainable influence to board level. Hardly surprising when we deliver courses on things neither the business nor its employees ever asked for. I have never, ever heard any normal person say what they need is a ‘course’ on ‘resilience’. It is something supplied by L&D not demanded by organisations, a chimera to make us look caring and important. This has been a worrying trend in workplace learning the delivery of courses based on abstract nouns that no one ever asked for. We supply things we think are relevant, rather than look at what the business demands in terms of goals.

Having beaten into people that they have all sorts of mental deficits, through billions spent on DEI, ESG and wellbeing training courses and initiatives, thrashing employees like piñatas, telling them they are weak and have deficits that need cured by courses. To remedy this, apart from the endless groundhog debates on the future of L&D at conferences, we come up with abstract concepts around which conference sessions and courses have to be built. The current obsession is with ‘Resilience’. These are too often bouts of over-earnest classroom courses or weird e-learning. All of this despite the overwhelming evidence, over many years, that this does not work, it continues. A ‘roll of the eyes’ is the most common reaction when you ask people what they think of all this.

So, as HR has turned into defending the organisation against itself, they then had the temerity to demand that we all need to man and woman up – we need more resilience. It’s like slapping people repeatedly on the face then them telling them to ‘pull themselves together’ before carrying on with the slapping. If your organisation is so dysfunctional that you need to train people to deal with that dysfunction - that speaks volumes about your organisation. Training people to deal with dysfunction is not going to fix it.

Curious history of ‘Resilience’ training

Resilience has deep roots in psychiatry, especially Freud and his daughter Anna Freud, on how individuals cope with trauma and adversity, who I discussed in detail in a recent podcast and whose theories are literally flights of fancy. also Bowlby and Erikson (wrong on most counts) pushed this forward in the 50s and 60s. But it was in the 70s and 80s, that Emmy Werner and Ruth Smith did longitudinal studies on children in adverse conditions. Their work, especially the Kauai Longitudinal Study, highlighted the factors that contributed to resilience in at-risk and sick children.

In training, resilience emerged from the positive psychology movement in the late 1990s when Martin Seligman emphasised the importance of building strengths and well-being, rather than just treating mental illness. Also discussed in detail in this podcast. He backtracked somewhat and more recent evidence shows that the training and wellbeing programmes are not effective at all.

Antifragility 

Modern Resilience training is a mishmash of all of this but there is one book that people thought promoted resilience but was in fact an attack on resilience and resilience training. That book was Antifragile: Things That Gain from Disorder by Nassim Nicholas Taleb. 

Taleb was a derivatives trader then hedge-fund manager and anyone who has actually read the book will know that he hates resilience training. “The fragile want tranquility, the antifragile grows from disorder, and the robust doesn’t care too much.” This is a man who is fiercely critical of certain elites and experts, particularly those he believes are detached from real-world consequences and has resonated resonate with populist sentiments.

He defines resilience as a mistake in that it promotes the ability to resist shocks but stay the same. A resilient system can withstand stress without significant damage but does not necessarily improve from the experience. By contrast, antifragile systems thrive and grow stronger in the face of stress and adversity. Taleb advocates for antifragility over mere resilience because antifragile systems benefit from disorder and challenges.

Taleb argues that resilience training, focuses on helping individuals or systems return to their baseline state after a disruption, an approach that misses the opportunity to leverage stressors for growth and improvement. It creates a false sense of security encouraging individuals and organizations to believe they are adequately prepared for challenges when, in fact, they are only prepared to endure them, not to improve from them.

We need to deliberate expose people to manageable levels of stress and variability to stimulate to build stronger, more adaptable capabilities. People need to continually seek out challenges that push their boundaries and enhance their capabilities so they can survive disruptions but actively using them as catalysts for innovation, embracing and leveraging stressors and challenges to achieve growth and improvement. In practice he doesn’t like HR, L&D as they are part of the bureaucracy of institutions promoting rules and rigidity, fixed outlooks and fixed career paths. Individuals should rather seek out challenges, embrace uncertainty and new experiences that push their boundaries and expand their capabilities.

Conclusion

And so we end up with a hotchpotch of stuff wrapped up into a disjointed PowerPoint and call it Resilience training. We need to stop building empires around ‘big words’ and get back to training competences to solve the skills shortages that all employers report.

Worrying data on how students & institutions are dealing with AI

Just published, this report from Cengage shows that students are stuck in institutions that can’t make their minds up about AI. As we’ve also seen workplace users are often in a similar position. This report should be seen in conjunction with the paper just published by the University of Chicago on the use of AI in the workplace showing it’s everywhere, the main resistance being institutional barriers. Back to the Cengage report and some data.

As 52% of students don’t end up in work even remotely related to their degree subject and career advice is woeful, they know, more than the institutions they’re educated in, how the world is changing, understand that AI is important and doing something about it.

They really are using this technology in anger to learn:

55% grads said their educational program did not prepare them to use GenAI tools.

The reasons for this are clear. Education is highly institutionalised in terms of structures, timetables, funding, quality control and embedded practices. It is these deeply embedded practices, strict hierarchies, bottlenecks and bureaucracy that make it difficult to adapt to new realities. One such reality is AI.

Even worse, their reaction is often to simply reject out of hand, categorise it as cheating or get obsessed about fictional ethical issues. After 40 years in the learning technology game I’ve seen it many times before, with email, photocopiers, calculators, computers, internet, word processing, social media, smartphones, recording lectures…

Yet what this data shows is that these students will emerge into a world where the data already shows massive use of AI. It is fast becoming an essential skill, yet little attempt is made to recognise this or do anything about it. Even worse, it is often demonised to the point of being restricted.

59% of employers say AI has caused them to prioritise different skills when hiring.

Even when we see this dramatic swing in the workplace, we still don’t react. Huge numbers of people are using GenAI when applying for jobs or skilling themselves in using the tools. They know it gives them edge. Why not listen to what is happening - read the research, look at the data.

48% of students say courses were offered to help them learn how to use GenAI

To be fair we’re in a twilight zone, or what Dan Fitzpatrick calls a ‘liminal’ zone’, stuck between the old and new. The first reaction in institutions is usually to deliver a ‘course’.  That’s is fair but not the real solution. 

Yet many are making the effort to embed AI in the educational system. This is mostly in the US but also some notable examples in other countries. There’s a slew of Universities in the US that have heartily embraced the technology and prepare their students for an obvious dimension of the future.

55% of colleges/learning programs discouraged GenAI use

Sadly, there’s the laggards, still stuck is caricaturing AI as some sort of cheat code in their game, for that is what much of this has become, a cat and mouse game on assessment. I understand that it takes time to get to grips with new stuff, but the revulsion and constant moralising, is damaging the future prospects of your learners. I expected fear and loathing in Las Vegas, not our Universities.

51% students say pace of technology making them second-guess their career

This is interesting and heartening. Young people recognise that the system is not helping them and are doing it for themselves, rethinking what they need and want to do. Sure they’re worried about their future, but understand that they have to be part of the real world not just the world of just lectures and essays.

Conclusion

It is important to track the data on use and attitudes as this allows us to readjust for the future. It is perhaps utopian or at least impractical, to expect institutions to change fast but the good news is that tech-savvy young people are doing it anyway.