Memo from CEO of Shopify.... Reflexive AI usage is now a baseline expectation at Shopify

 Team,

We are entering a time where more merchants and entrepreneurs could be created than any other in history. We often talk about bringing down the complexity curve to allow more people to choose this as a career. Each step along the entrepreneurial path is rife with decisions requiring skill, judgement and knowledge. Having AI alongside the journey and increasingly doing not just the consultation, but also doing the work for our merchants is a mindblowing step function change here.

Our task here at Shopify is to make our software unquestionably the best canvas on which to develop the best businesses of the future. We do this by keeping everyone cutting edge and bringing all the best tools to bear so our merchants can be more successful than they themselves used to imagine. For that we need to be absolutely ahead.

Reflexive AI usage is now a baseline expectation at Shopify

Maybe you are already there and find this memo puzzling. In that case you already use AI as a thought partner, deep researcher, critic, tutor, or pair programmer. I use it all the time, but even I feel I'm only scratching the surface. It’s the most rapid shift to how work is done that I’ve seen in my career and I’ve been pretty clear about my enthusiasm for it: you've heard me talk about AI in weekly videos, podcasts, town halls, and… Summit! Last summer I used agents to create my talk, and presented about that. I did this as a call to action and invitation for everyone to tinker with AI, to dispel any scepticism or confusion that this matters at all levels. Many of you took up the call, and all of us who did have been in absolute awe of the new capabilities and tools that AI can deliver to augment our skills, crafts, and fill in our gaps.

What we have learned so far is that using AI well is a skill that needs to be carefully learned by… using it a lot. It’s just too unlike everything else. The call to tinker with it was the right one, but it was too much of a suggestion. This is what I want to change here today. We also learned that, as opposed to most tools, AI acts as a multiplier.  We are all lucky to work with some amazing colleagues, the kind who contribute 10X of what was previously thought possible. It’s my favorite thing about this company. And what’s even more amazing is that, for the first time, we see the tools become 10X themselves. I’ve seen many of these people approach implausible tasks, ones we wouldn’t even have chosen to tackle before, with reflexive and brilliant usage of AI to get 100X the work done.

In my On Leadership memo years ago, I described Shopify as a red queen race based on the Alice in Wonderland story—you have to keep running just to stay still. In a company growing 20-40% year over year, you must improve by at least that every year just to re-qualify. This goes for me as well as everyone else.

This sounds daunting, but given the nature of the tools, this doesn’t even sound terribly ambitious to me anymore. It’s also exactly the kind of environment that our top performers tell us they want. Learning together, surrounded by people who also are on their own journey of personal growth and working on worthwhile, meaningful, and hard problems is precisely the environment Shopify was created to provide. This represents both an opportunity and a requirement, deeply connected to our core values of Be a Constant Learner and Thrive on Change. These aren't just aspirational phrases—they're fundamental expectations that come with being a part of this world-class team. This is what we founders wanted, and this is what we built.

What This Means

Using AI effectively is now a fundamental expectation of everyone at Shopify. It's a tool of all trades today, and will only grow in importance. Frankly, I don't think it's feasible to opt out of learning the skill of applying AI in your craft; you are welcome to try, but I want to be honest I cannot see this working out today, and definitely not tomorrow. Stagnation is almost certain, and stagnation is slow-motion failure. If you're not climbing, you're sliding.

AI must be part of your GSD Prototype phase. The prototype phase of any GSD project should be dominated by AI exploration. Prototypes are meant for learning and creating information. AI dramatically accelerates this process. You can learn to produce something that other team mates can look at, use, and reason about in a fraction of the time it used to take.

We will add AI usage questions to our performance and peer review questionnaire. Learning to use AI well is an unobvious skill. My sense is that a lot of people give up after writing a prompt and not getting the ideal thing back immediately. Learning to prompt and load context is important, and getting peers to provide feedback on how this is going will be valuable.

Learning is self directed, but share what you learned. You have access to as much of the cutting edge AI tools as possible.There is chat.shopify.io, which we had for years now. Developers have proxy, Copilot, Cursor, Claude code, all pre-tooled and ready to go. We’ll learn and adapt together as a team. We’ll be sharing Ws (and Ls!) with each other as we experiment with new AI capabilities, and we’ll dedicate time to AI integration in our monthly business reviews and product development cycles. Slack and Vault have lots of places where people share prompts that they developed, like #revenue-ai-use-cases and #ai-centaurs.

Before asking for more Headcount and resources, teams must demonstrate why they cannot get what they want done using AI. What would this area look like if autonomous AI agents were already part of the team? This question can lead to really fun discussions and projects.

Everyone means everyone. This applies to all of us—including me and the executive team.

The Path Forward

AI will totally change Shopify, our work, and the rest of our lives. We're all in on this! I couldn't think of a better place to be part of this truly unprecedented change than being here. You don't just get a front-row seat, but are surrounded by a whole company learning and pushing things forward together.

Our job is to figure out what entrepreneurship looks like in a world where AI is universally available. And I intend for us to do the best possible job of that, and to do that I need everyone’s help. I already laid out a lot of the AI projects in the themes this year- our roadmap is clear, and our product will better match our mission. What we need to succeed is our collective sum total skill and ambition at applying our craft, multiplied by AI, for the benefit of our merchants.

-tobi

CEO Shopify

Fiverr CEO''s stunning letter to all employees..... AI is coming for your jobs. Heck, it’s coming for my job too.

Hey team,

I’ve always believed in radical candor and despise those who sugar-coat reality to avoid stating the unpleasant truth. The very basis for radical candor is care. You care enough about your friends and colleagues to tell them the truth because you want them to be able to understand it, grow, and succeed.

So here is the unpleasant truth: AI is coming for your jobs. Heck, it’s coming for my job too. This is a wake-up call.

It does not matter if you are a programmer, designer, product manager, data scientist, lawyer, customer support rep, salesperson, or a finance person – AI is coming for you.

You must understand that what was once considered ‘easy tasks’ will no longer exist; what was considered ‘hard tasks’ will be the new easy, and what was considered ‘impossible tasks’ will be the new hard. If you do not become an exceptional talent at what you do, a master, you will face the need for a career change in a matter of months. I am not trying to scare you. I am not talking about your job at Fiverr. I am talking about your ability to stay in your profession in the industry.

Are we all doomed? Not all of us, but those who will not wake up and understand the new reality fast, are, unfortunately, doomed.

What can we do? First of all, take a moment and let this sink in. Drink a glass of water. Scream hard in front of the mirror if it helps you. Now relax. Panic hasn’t solved problems for anyone. Let’s talk about what would help you become an exceptional talent in your field:

Study, research, and master the latest AI solutions in your field. Try multiple solutions and figure out what gives you super-powers. By superpowers, I mean the ability to generate more outcomes per unit of time with better quality per delivery. Programmers: code (Cursor…). Customer support: tickets (Intercom, Fin, SentiSum…), Lawyers: contracts (Lexis+ AI, Legora…), etc.

Find the most knowledgeable people on our team who can help you become more familiar with the latest and greatest in AI.

Time is the most valuable asset we have — if you’re working like it’s 2024, you’re doing it wrong! You are expected and needed to do more, faster, and more efficiently now.

Become a prompt engineer. Google is dead. LLM and GenAI are the new basics, and if you’re not using them as experts, your value will decrease before you know what hit you.

Get involved in making the organization more efficient using AI tools and technologies. It does not make sense to hire more people before we learn how to do more with what we have.

Understand the company strategy well and contribute to helping it achieve its goals. Don’t wait to be invited to a meeting where we ask each participant for ideas – there will be no such meeting. Instead, pitch your ideas proactively.

Stop waiting for the world or your place of work to hand you opportunities to learn and grow—create those opportunities yourself. I vow to help anyone who wants to help themselves.

If you don’t like what I wrote; If you think I’m full of shit, or just an asshole who’s trying to scare you – be my guest and disregard this message. I love all of you and wish you nothing but good things, but I honestly don’t think that a promising professional future awaits you if you disregard reality.

If, on the other hand, you understand deep inside that I’m right and want all of us to be on the winning side of history, join me in a conversation about where we go from here as a company and as individual professionals. We have a magnificent company and a bright future ahead of us. We just need to wake up and understand that it won’t be pretty or easy. It will be hard and demanding, but damn well worth it.

This message is food for thought. I have asked Shelly to free up time on my calendar in the next few weeks so that those of you who wish to sit with me and discuss our future can do so. I look forward to seeing you.

Yours,

Micha

 

UDL is is an ideologicaly-driven sham

Universal Design for Learning (UDL) claims to be revolutionary but it is an ideologically-driven sham. It does far more harm than good. Folding in some of the worst learning theory into one 'my way or the highway' approach, it literally makes no sense. It is a barely disguised expansion of discredited Learning Styles theory, with costly and ineffective prescriptions.

They claim its framework promises inclusivity, adaptability, and improved access for all learners. Yet beneath this superficial rhetoric lies a troubling lack of theoretical grounding. It is more of an ideological banner than a well-developed pedagogical method.

At its core, UDL asserts that variability in learning is not an exception but the norm, and that curricula should be designed to offer multiple means of engagement, representation and expression. This sounds progressive, but it begs the question: where is the evidence that this approach is universally effective? Unlike more established pedagogical frameworks that are underpinned by decades of empirical research, UDL lacks the scaffolding of a cohesive theoretical tradition. It cherry-picks ideas from neuroscience, educational psychology and accessibility studies without unifying them into a coherent explanatory model. Consequently, it has become a collection of loosely connected guidelines.

UDL is undeniably ideological in nature, that is its appeal. Rooted in a utopian vision of educational equality, it is executed so simplistically that it does great harm. It devastates actual individual needs by stupidly replicating things across many modalities. By focusing so heavily on inclusion, UDL ignores the realities of constrained resources, teacher capacity, and the complex priorities of educational institutions. It is a simplistic bromide that puts replication over meeting real needs in real organisations. It literally abandons pedagogy on the altar of ideology.

The ideological bent of UDL sidelines alternative approaches that need specialisation or targeted interventions. For example, tailored support for students with specific disabilities often requires highly individualised methods that go beyond UDL's dilute and generic calls for flexibility. In an obsessive attempt to be ‘universal’, it dilutes its applicability to those who really do need personal, specialised attention the most. Ironically, this universalism can neglects the very individuals it aims to serve.

UDL’s slavish emphasis on learner choice and agency becomes an ideological trap in practice. The belief that all students should have multiple ways to engage with content and demonstrate understanding assumes they have a level of maturity, self-awareness, and autonomy that many learners, particularly younger ones. This is quite simply mad. 

An overemphasis on learner freedom leaves less confident or less experienced students adrift, struggling to navigate an educational landscape designed to cater to everyone and, paradoxically, to no one in particular.

Universal Design for Learning aspires to reshape education into a more inclusive, flexible, and equitable system. But its lack of theoretical rigor and reliance on ideological principles leave it vulnerable to critique, means it should be dismissed as an impractical, overhyped framework. It has little to do with the real-world complexities of education, which is why it only thrives on the fringes of Higher Education.

One could well rename it a Universal Disaster for Education.

Raising in Arizona with AI: groundbreaking journey in AI ducation

Arizona has taken a bold leap in education by approving a pioneering model: a charter school where artificial intelligence takes the lead in teaching. Set to launch next year, Unbound Academy will be a fully online institution catering to students in grades four through eight. 

Traditional teaching will be replaced with two hours of daily academic instruction tailored entirely by AI. Leveraging platforms like IXL and Khan Academy, the AI will adapt lessons to each student's pace and abilities, analysing emotional cues, task completion times, and responses to create a personalised, frustration-free learning experience designed to maximise engagement and progress. That's the claim at least. But there have been many attempts at online schooling.

For the rest of the school day, students will take life-skills workshops on topics like financial literacy, entrepreneurship, and critical thinking, guided not by teachers but by facilitators known as 'guides'. This innovative approach stems from Unbound’s success with its private school in Texas, where it claims to have doubled academic outcomes with fewer instructional hours. Inspired by Elon Musk, Unbound aims to refine this model for potential expansion into states such as Arkansas and Utah.

They see this as a much-needed shift to address inefficiencies in traditional education while preparing students for an evolving world. Backed by influential supporters like the Bill and Melinda Gates Foundation, Unbound’s AI-driven school could mark the dawn of a transformative era in education—or spark ongoing debate over its impact on accessibility and quality. Let's see...

Why Latin should NOT be in the school curriculum

The venerable Mary Beard invited me to debate the ‘Future of Latin’ at the British Museum some time ago and every few years, normally in line with the electoral cycle, the issue arises again. So how did it go and what were my arguments?

Sell-out

A sell out, with 350 paying Latinists, who turned up to hear Mary chair a debate which pitched David Aaronovitch and I against Peter Jones and Natalie Haynes. As we walked on stage and introduced ourselves (I found that I was the only person who didn’t go to Oxford), everybody seemed to know each other (except me). This is exactly the sort of challenge I like, as although it was a sell-out, I’m not in the habit of selling-out on my beliefs and principles.

Neither contrarian nor philistine

To be clear, I was not there as a contrarian or philistine, as I’ve been in love with the classics since I was a boy. My first secondary school lay astride the Antonine Wall and for over 40 year I’ve been to every corner of the Roman, Greek and Egyptian Empires, from Scotland to Syria. I cycled Hadrian’s wall, still go to Greece every year and never miss an opportunity to visit sites, especially on the Peloponnese. and I go to Egypt, as I do almost every year, for another dose of Egyptology in December, and have done since 1989. 

I am, however, also a rationalist and realist, and my 30 plus years of experience in the learning game have made me deeply suspicious of the position of Latin, among many other things, in our culture and school curriculum. 

As Bertrand Russell said:

“I was made to learn Latin and Greek, but I resented it, being of opinion that it was silly to learn a language that was no longer spoken. I believe that all the little good I got from years of classical studies I could have got in adult life in a month.”

I was there to argue that it should NOT be taught in schools at all. These were, and are, my arguments. Note that I am not against Latin the language as an object of study, I am against it being taught in schools. The arguments for each of these propositions were presented by my opponents and audience members. These were my responses.

1. LATIN does NOT help you learn other languages

Why scratch your ear by going over the top of your head? Isn’t it obviously easier to just get on and learn Spanish, Italian and French, rather than the convoluted route through Latin. Researchers Thorndike, Sherwin, Haag & Stern all think so. In the Sherwin meta-study 'Research and the teaching of English, “the study of Latin does not necessarily increase the ability to learn another language… No consistent experimental evidence in support of this contention was found.” Learners have limited time, that time is clearly better spent on the target language itself. 

In fact, Latin can make learning a new language MORE difficult. In Search of the Benefits of Latin by Haag and Stern (2003), who followed up on Thorndike’s work nearly a century earlier, in the Journal of Educational Psychology is the key paper. They took two groups of German students, one who studied French, the other Latin as their second language. Both groups were given a course in Spanish and the results measured. When the results were analysed by a Spanish assessor (who didn’t know who had taken French or Latin) the French students made significantly fewer grammatical errors than the Latin students. As predicted the Latin students wrongly transferred the rules of Latin to Spanish. For example “misconstructions in verbs emerged to be either highly reminiscent of or identical to Latin verbs”. The French group turned out to be much better prepared to cope with Spanish grammar.

Psychologically the Latin students had suffered from negative transfer using false friends in their new language. The problem with understanding Latin is that you need to pay close attention to word endings; case markers on nouns and time markers on verbs. But in English and Romance languages word order and prepositions are more important. Endings play a minor role. The fact that the grammatical similarities between modern Romance languages are much greater than that between Latin and modern Romance languages, means that the defenders of Latin are flogging a dead horse. Thorndike was right – transfer of the wrong kind occurs.

2. LATIN does NOT have an edge in improving cognitive skills

This argument is greatly loved by the parents of ‘gifted children’ although I rarely come across a middle-class parent whose child is not gifted. For gifted, read ‘pushed’ (not a bad thing but very different). Again Haag and Stern (2000), in a review of the literature found that Thorndike, “did not find any differences in science and maths in students who learned Latin at school and those who did not”. Two groups of comparable students, where one studied Latin, the other English, were assessed after two years, “No differences were found in either verbal or non-verbal IQ or grades in German or Maths”. This again had been predicted by Thorndike decades before, namely that transfer needs common ground in the source and target.

3. LATIN does NOT give significant advantages in using English

English is a Germanic language – we are largely speaking in Old English rooted language. The TOP 100 words are Old English (sorry three are Old Norse – THEY, THEIR, THEM). As for one audience member’s argument that it is necessary as all children need to be able to understand etymology, I disagree. Is there anything more annoying than the dinner-party bore who stops you and explains the root of a word, as if it made any difference to your argument or its contemporary meaning. If anything a good course in Old English would be better. Do you really have to go through years of Latin so that you understand the roots of Homo sapiens?

4. LATIN does NOT guides us correct use of English grammar

Stephen Pinker, Harvard’s world renowned expert in psycholinguistics backs this up in The Language Instinct, “Latin declensional paradigms are not the best way to convey the inherent beauty of grammar”. He recommends computer programming and universal grammar on the grounds that they are “about living minds and not dead tongues”.

Pinker has a go at the Latin language mavens who want to pointlessly foist Latinate rules of grammar into English. As Pinker explains, this snobbery took root in 18th century London, when Latin was used as a mark of social class (still true today) and Latin grammar rules were crudely pasted into books on English grammar, for example, ‘don’t split infinitives’ and ‘don’t end a sentence with a preposition’. Latin simply doesn’t allow you to split an infinitive and to stupidly insist that it’s wrong in English, is as stupid as making us all wear togas.

English does NOT have Latin grammar. English grammar fundamentally Germanic. Latin highly inflected, role of word in a sentence indicated by its endings. English relies primarily on word order to determine grammatical relationships. Latin nouns gendered English are not.

5. LATIN is necessary for SCIENCE, LAW and MEDICINE

One girl in the audience, from Merseyside was adamant that Lawyers needed to have studied Latin. Incidentally, if you’ve heard the argument that Latin helps medical students learn and understand the considerable amount of medical vocabulary that has to be learned in medical schools. This also turns out to be false as shown in Pampush and Petto (2010). The Latin vocabulary in law, medicine and biology is there but one can simply learn the words, without knowing their etymology - most lawyers, doctors and scientists do exactly that. Why spend years learning Latin to help you in a small selection of vocabulary? The pay-off is, as Russell pointed out, is tiny.

6. LATIN brings the joy of ideas and literature

One contribution from the audience I did like was the idea that Latin bridges us to rich tradition of thought and literature. He mentioned Roman literature but also the Magna Carta, Bacon and Newton. First, anyone studying history will not lose out by working with translations of the Magna Carta or Bacon. And does anyone really need to read Newton’s tortuous Latin, other than scholars in the history of maths? I think not. 

And why not Greek? Wouldn’t you prefer the riches of Plato and Aristotle, Homer’s Iliad and Odyssey, along with the works of any one of Aeschylus, Sophocles, Euripides and Aristophanes to the largely derivative Roman philosophers (few can name any) and dramatists. Even in History, the Greeks Thucydides and Herodotus are a match for any Roman writer, including Tacitus. In politics, our democratic traditions are largely Greek. Even English law is not Roman (although in Scotland it is). Then there’s the politics and democratic traditions that are fundamentally Greek. Even in maths and science the mighty Pythagoras, Euclid and Archimedes trump the Romans.

7. LATIN’s history of exclusion

Of course, Latin was introduced to this country as the language of the clergy, it was not, as is sometimes supposed introduced by the Romans, as few Latinate words come from that era. As the language of the church it largely excluded the laity, as most remained illiterate and spoke various forms of English. It was then used as the gatekeeper for learning. This had some benefits, when Latin was sort of European Esperanto, but continued for centuries after that died and was long used as the gatekeeper at Oxbridge and other institutions. David Aaronovitch made the telling point that it is still a key ‘marketing’ differentiator for independent schools.

8. LATIN would NOT die if not taught in schools

There are plenty of scholars in subjects that are not taught at schools. The bottom line with any dead language, especially Latin, is that there’s little that is new and to be uncovered. Compare this to the vast amounts of Sumerian cuneiform tablets that still need to be both deciphered and excavated. In the end I agree with one of our greatest living Latin scholars Mary Beard, “the overall strength of the classics is not to be measured by exactly how many young people know Latin or Greek from school or University. It is better measured by asking how many believe that there should be people in the world who do know Latin and Greek.” This about sums up my position. 

I am not against the study of Latin or any other historic languages. This is largely a matter of proportionality for our Universities. By all means let a few study Latin. What I am against is too prominent a role for Latin in contemporary school curricula. Our young people have enough on their plate at 5-18, as the range of subjects expands to include a wider range of science subjects, IT and other vocational skills. A dead language, in the sense of no longer being spoken or used for scholarship, at this stage is merely the dead hand of educational history being played out by interested parties.

9. LATIN is NOT about choice

Several people argued that Latin is a matter of choice in schools. The curriculum is crowded enough with increasing demand every year as new subjects, such as computer studies, emerge. As we have seen from recent educational lunacy in policy, has resulted in the destruction of choice in the curriculum. Professor Alison Wolf, Gove’s lapdog, ignored advice from industry and education experts to crush 3100 vocational qualifications. That strange beast, the EBacc, which had Latin as a core choice, deliberately excluded all vocational subjects, even ICT. Gove's successors , such as Williamson did the same, with special temporary funding, creating a one-sided system that simply reinforces the old apartheid system we have in this country between academic and vocational learning. 

This is not to say that all education should be aimed at utility and employment, just that a contemporary curriculum in schools is always a trade-off, with new subjects and content always arriving, so that everything we taught in the past can’t just remain. We need to weed and feed a curriculum, which means some hard choices. My preference would be to focus more on modern languages themselves, where we have declining numbers.

10. LATIN is a political issue

It has now become a political pawn. A largely (not exclusively) right wing movement to impose private school norms (7%) on state schools (93%) became an electoral issue, hence Williamson's special funding.  Labour won and implemented their promised VAT policy on private schools and in reassessing budgets they also scrapped Williamson's artificial subsidy for Latin in state schools. We can argue for or against Latin in schools but this was democracy in action.

Conclusion

I rather liked this audience and I especially liked Peter Jones.  He was my opponent but put to bed those old tropes about Latin improving your ability to learn languages, improve intelligence or think logically. He was remarkably free from cant and any sense of snobbery. What he loved was Latin and his plea was for the beauty and intrinsic value of the language. With that I have no argument.

AGI is here...

As easy as one-two-three? No. Easy as one-three. OpenAI has skipped o2 to call their new ‘frontier’ model o3 (Altman hinted that this may have been due to Telco o2 issue). I like the idea that AI is undermining everything we were told makes good marketing - no read branding, messaging, just get it out there with benchmarks.

Social media was full of people saying Google had destroyed OpenAI. But their pre-Christmas release single was smashed off the Charts by OpenAI’s o3.

Transcending human intelligence

Not sure we've realised that we now have AGI. Arc-AGI solved with 87.7% (human threshold 85%) as well as other benchmarks... we will, of course, move the bar higher, find ways to avoid recognising the achievement... then pass it again... Arc Prize is a not-for-profit with an AGI benchmark. There are new skills so that the AI cannot memorise them.  

To give you some idea of the speed and scale of progress:

Software engineering

On real-world software tasks, evaluations on o3 are more than 20% better than o1 models at 71%. AI has already established itself as solid, useful and widely used coding tool. People have been letting AI code then iterating  iterate. This takes it to another level. One wonders how long the job of coder will survive at this rate.

Maths and science

Also superb at maths and PhD science questions at 88.7 %. A typical PhD gets around 70%. Frontier is toughest mathematical dataset – extremely hard problems. o3 has 25% accuracy which is a strong result.

GPQA Diamond

An interesting measure of how far we have come is GPQA Diamond. It's a clever test that compares a model against npvice Google search, human domain experts and the model. Experts get 81% right in their fields, highly skilled non-experts with 30 minutes per question and Google access get 22%. GPT-4 got 37% at the start of 2024. o1 got 78%. o3 is 87.7%. That is astonishing.

AGI

Artificial General Intelligence suffers from a problem of definition, as do most abstractions at this level. Does it mean:

Specific human reasoning skills (maths, science etc)

Average human competences

Greater than all of humanity

There is no singularity, there is a spectrum or constellation of possible targets here. What is clear is that these targets are being hit, not in one go, but one by one, sometimes in clusters. Maths and science are easy to measure but also fiendishly difficult to achieve, so this is a real milestone. Yet they focus on clear rationality. To be fair critics were telling us that AI would never get here, never mind get here so fast. We should celebrate this as many of the problems we face with climate, energy, healthcare and education may well be solved, not in the sense of final solutions but better solutions.

Problem solving in real life is messier and more of a challenge. That's why the agentic move is so interesting, as it tackles this set of human capabilities. We have brains that have evolved into a specific environment where we had to solve specific problems. This is where the dynmaic interrogative, dialogic nature of AI helps enormously. It has already made great strides in this direction.

Embodied AI, in the physical world of elevators, cars, cabs, trucks, drones, ships and submersibles has also taken great leaps. This is another set of targets that are being hit one by one. One could argue that neurological targets are also on our hotlist - Neuralink is a good example, where we enhance our neurological deficits.

AI may help us understand neuroscience and the brain, solve engineering problems to accelerate fusion, help with drug discovery and many other intractable problems. What we can be sure of is the increased impact of AI on productivity. The leaps in efficacy prove this.

Conclusion

This is a warning to people who claim that scaling is over. Sutskever was right – we have a way to go and other techniques are clearly delivering the goods. It is clear that AI is delivering faster than expected. The consequences of AGI are closer than expected with huge productivity gains on the horizon. That is the subject of the book I am currently writing.

Future issues

One issue needs discussion - compute costs. I think this will be solved. We saw a 250x decline in token costs in 20 months. So $20 for hard problems are likely to come down to cents.

One can now start to ask how the cost of compute compares to human costs in organisations for similar tasks and roles. The productivity game looks as though it will start with coding, where much of it can be automated.

On problems to solve:

• fusion

• medical research

• personalised tutors

• optimising political policies

• next-generation batteries

• cheap renewables

A final though on AI being self-generative. At what point does this technology start working on itself to solve the frontier problems and advance even quicker?

 

Fast technology and the slowcoach brain – fascinating paper

The findings in this paper by Zheng and Meister 'The Unbearable Slowness of Being: Why do we live at 10 bits/s?' are quite startling when you consider the vast disparity between our sensory input capacity and our cognitive processing speed. It's almost as if we're living in slow motion within a world of high-speed data. This realisation challenges our intuitive understanding of human intelligence and efficiency. 

Shaped by evolution

It's astonishing that despite our sensory systems being capable of absorbing vast amounts of data, our cognitive output remains so limited. The surprise at this slow processing suggests that perhaps our brain isn't designed for speed but for careful, deliberate processing which might be more suited to survival in our evolutionary context. Humans and other species might operate at rates sufficient for their ecological niches, suggesting evolutionary constraints. Human cognition tends towards serial processes, possibly due to evolutionary origins where multitasking was not advantageous.

Humans process information at an oddly slow rate of about 10 bits per second (bps). This contrasts starkly with our sensory input capacity of approximately 10^9 bps. Even expert typists operate at around 10 bps, English narration speed suggests an information rate of about 13 bps, blindfolded speed-cubing has a perception rate during the puzzle inspection phase of around 11.8 bps. Top-end memory tasks like binary digit memorisation or speed card inspection reveal rates from 5 to 18 bps, showing that even under high memory demands, the rate remains within the 10 bps ballpark.

Bottleneck

The bottleneck is not neural capacity, it is throughput. Sensory neurons, particularly photoreceptors, can handle gigabits of data, while the brain's behavioural output remains at 10 bps. The paradox is that, despite having neurons capable of transmitting information quickly, when it comes to human behaviour and cognition, we are slowcoaches. This limitation seems to be fundamental.

Photographic memory claims turn out to be false. Even with exceptional memory, the data acquisition rate doesn't significantly exceed 10 bps. Our perceived richness of visual scenes is an illusion, as actual perception is much more limited, especially outside the focal point.

Let’s think about this, albeit in a limited and slow fashion! Even with advanced technology like Neuralink, we'd still be bound by this 10 bps limit for cognitive throughput. This is a sobering thought. If our brain operates at such a slow rate, it might imply that human ‘intelligence’ is less about raw computational power and more about the quality and nature of information processing that focuses on depth rather than breadth.

Conclusion

Neurons are not inherently inefficient; rather, the brain's architecture seems optimised for serial processing over parallel. With AI we have several dimensions of scale, in terms of relevant data, retrieval and parallel processing. AI may be offering a way out of this human envelope, that of being fixed, linear and slow, to augmenting our abilities with technology that is flexible, parallel and fast.

Does ChatGPT enhance student learning? Meta-study with some surprises!

Great to see a meta-study asking this bold question:

Does ChatGPT enhance student learning? A systematic review and meta-analysis of experimental studies.

See study here.

STUDY

Nice spread of subjects and focus on classroom teaching. Predominantly university-level interventions (84%), with a smaller focus on K-12 (14.49%), language education is most studied (32%), followed by computing, health, physics, and education. Most of the interventions occur in classroom environments (87%) with a duration of 5 to 10 weeks. The AI was primarily used as a direct learning tool (80%), with some integration into broader educational platforms.

RESULTS

The results were, I’s say, quite startlingly positive. Many are decrying this technology as something that depresses academic performance, demotivates students, and blocks higher-order critical thinking. The study suggests the opposite.

Academic Performance

Significant improvement with ChatGPT use, though concerns about whether this is due to the intervention or the quality of AI-generated content.

Affective-Motivational States

Positive effects on motivation and emotional engagement

Higher-Order Thinking Propensities

Positive influence on self-reported propensities.

Mental Effort

Significant reduction, suggesting less cognitive strain with ChatGPT use.

Self-Efficacy

No significant impact observed.

The study covers a broad spectrum of educational contexts and subject areas, providing a pretty wide view of ChatGPT's application in education. I’ll leave the methodological rigour questions for those more qualified but I liked the ambitious approach to examining a range of learning outcomes, offering insights into some key dimensions of student learning. I was surprised that self-efficacy was not positive – interesting finding as it runs against the affective-motivational finding.

Sure there are weaknesses; some small sample sizes, sometimes little pre-testing and one odd one - ChatGPT sometimes used in assessments. But this is a good start.

Reclaiming Productivity: Aligning Work, Learning, and Societal Needs

‘Productivity is being able to do things that you were never able to do before,’ said Franz Kafka. That’s pretty much the same as learning. I’d like to see education and workplace learning be more aware of productivity as a worthy idea in this wider sense of the word.

Nobel Prize-winning economist Paul Krugman also said, “Productivity isn't everything, but in the long run, it is almost everything”. He has a point. Producing autonomous and productive adults is surely what we aspire to as a collective goal of a nation. A country's ability to improve its standard of living depends on its ability to raise its output per worker. Isn't that what we want from L&D?

There is a sense that we’ve lost something; people not caring, huge numbers not in employment, yet massive skills shortages. In the UK we have an economically-inactive group of 9 million working-age people. Around 1.5 million are unemployed, about 2.7 million are students (here, I’d argue, we have too many staying on for too long in HE). We have 2.3 million ‘sick’, which is shocking and 1.5 million as carers. We should be tackling this head on.

We have an education system way out of kilter with societal needs. A realignment of education to life and a living would help. The looking down upon, defunding and eradication of any vestige of vocational learning from curricula in schools and Universities has not helped. We want more houses but don’t have the skilled people to build them; green energy but have a generation who can hold placards, sit in front of traffic and throw paint at art but can’t actually do anything practical to build the green technology we need; more nurses but have turned it into a graduate profession disenfranchising working class entrants.

In workplace learning we need to stop distracting ourselves with abstractions. The 'Skills-Based Organisation' has long been an empty trope, because we have been seduced into thinking that abstract nouns like leadership (lots of spend but so little of it), culture, diversity, equality, values, inclusion, resilience etc are 'skills' or some mysterious miasma that will encourage and produce skills. 

The very idea that skills have anything to do with text-based learning and assessment or sitting around tables scribbling on pieces of flipchart paper is laughable. A renewed focus on productivity would help but we're not much interested in actual outputs, only abstract inputs. Training so often kills productivity by distracting people from reality. People love a cosy chat with others who agree with them. But this is so often a zero-sum game - the net gain in terms of change, skills, productivity is zero. In fact it may be a negative-sum game as it takes up tons of time, costs and tends to fix people into old, fossilised behaviours, meaning absolutely nothing changes or improves.

Productivity is the ratio of output to inputs, which can be applied to people, not cruelly or mechanically but sensitively. Yet we flee from evaluation and measurement as we’re scared of what we may find. Productivity can be difficult to measure and improve but it translates into more satisfied employees a better standard of living for all and the crazy idea that your kids will be better off than you. 

Astounding talk by Sutskever

A 30 minute talk given yesterday by Ilya Sutzkever already being seen as seminal.

We have gone through a decade of astonishing advancements in neural network learning. We were right to equate an artificial neuron with a human neuron. Biologically inspired AI has been hugely successful. It led to huge success through scaling. We forget that we were using crude string matching, tables and statistical techniques. We worked in this world and from 2014 were successfully producing real product. But we knew its limitations.

PEAK DATA
Then came GTP 2.0 and LLMs. These have been astonishingly successful. On speed alone they do what takes humans ages to complete, they are also often better on quality. But Sutskever’s big message was that pre-training will end due to data limitation, peak data, the fossil fuel of AI.

What do we do after pre-training? Agents, synthetic data and OpenAI o1 are examples of attempt to move forward. He draws from biology showing the relationship between body size of mammals and their brains. Hominids have an accelerated brain to body ratio. We are different. But so is AI, which can also be different. This is evolution on steroids. What took billions of years to achieve through evolution to hominids, is being done in just a few years.

SUPERINTELLIGENCE
Progress in AI has been remarkable but he thinks we are heading towards ‘superintelligence’. At the moment we have amazing systems which in many areas (evaluated) exceed human capabilities. They still get a little confused at times, who doesn’t. Hallucinations happen but models will eventually autocorrect. This may already be happening.

We have lots to gain from current AI, as it is so incredibly useful. Even if we stopped right now there is so much that can be done with the current technology to keep us busy for a decade. It is a productivity boost in may areas of human endeavour as well as being a discovery tool.

Systems are only very slightly agentic but agency, reasoning and self-awareness will happen – why? They are useful. The human brain, stopped growing in size but we got stronger cognitively, so humanity kept advancing. Similarly, the agents and tools on top of LLMs will fuel the progress through agentic behaviour. This future AI will possess unpredictable capabilities and self-awareness, transforming their capabilities.

FUTURE
He left us hanging and is very careful not to become too flippant and certain of himself, like some silly, keynote futurist. He has worked on the leading edge of this technology for decades and has contributed hugely to the field. While careful about predicting the exact nature of future development, which he thinks we cannot predict with certainty, he nonetheless, paints a picture of rapid progress and achievement.

Tyranny of Text: Education, Work, and the AI Revolution

I have just visited the Ramesseum, and the statue that inspired Shelley’s ‘Ozymandias’. This part of the world seems cursed by being the place where ‘writing’ was first invented, religious books written, each granting different groups a sense of stupid, eternal entitlement. They’ve been at each other’s throats ever since. When people dream of text-based heavenly Paradises, they can't see worldly Paradises in front of their eyes.

We are drowning in a sea of 'text' in learning. From 5 to now 25, young people spend almost all of their time reading, writing and critiquing ‘text’ in an educational system because it is easy, creating the illusion that you can assess most skills through text -  you can’t. 

We have decimated vocational learning by sucking up funding into often purely text-based Degree subjects. Lecturing is easy, teaching is hard. Setting essays is easy, assessment is hard. Producing text is now easy, doing things is still hard. Once you see LLMs as producing text as calculators produce good numerical solutions, you relax a bit on AI.

Less is more

AI is not about generating more text. Its true purpose has been in generating LESS text; summarising beautifully, producing grammatically perfect text with no spelling or punctuation errors. Most text is not in books and essays but in mundane communications, as emails, messaging and social media – and AI is largely an aid to communication.  In work and life, communications is often over-long, badly written and error prone. That problem is being solved.

Young people are quite adept at short, concise messaging, they do it all day every day. The problems come from people fed on a diet of long-form text, who tend to see everything as a potential essay, so in organisations and government bureaucracy, which existed long before AI, is text-heavy, the production of unnecessary forms, documentation and reports. AI will optimise and automate this.

Learn by doing

Most of work and life is about doing, most education is about writing. Yet much text production in white collar work may well be automated out of existence. Let’s recognise that AI is now fundamentally multimodal, using speech, images and video, also creating 3D worlds, robots and automated vehicles. This is not to eliminate text, just see it as not primary and over-egged in education, work and life. With multimodal AI and robotics, AI is moving fast into the real of doing, both teaching us how to do things and doing things for us and in place of us. The lines are now blurring, with increased scepticism over text-only education. Education is for both life and living.

AI may have seemed like a text-based phenomenon, but it is proving to be more about communication, speech, robots, data and automation. We will be made more productive by having less text, automating as much as we can out of the system, dissolving text-laden bureaucracy. Most of the critics of AI come from those who deal with text as a living, in education and work.

PODCAST on Invention of writing
PODCAST on Literacy & Orality

How do emotions impact learning and mental effort? Maybe not as we think

I’m deeply suspicious, not of the claim that emotions are important in learning, but of the assumption made on the back of this, that emotion is quite simply a good thing in learning. It leads to all sorts of wrong assumptions about fun, gaming and no end of odd theorising about happiness.

Like the shallow side of social constructivists, who simply conclude that all learning should be ‘social’, whatever that means, there is a tendency to see ‘emotion’ as a good thing, no matter what. This problem has been exacerbated in recent times by the therapeutic assumption that emotions are somehow intrinsically virtuous.

New study

So this study caught my eye (thanks Carl Hwenrdrick) as it digs into something I had always thought may be true, that positive emotions don’t work the way you think. 

https://www.sciencedirect.com/science/article/pii/S1041608024001900

While feeling good (like being happy or motivated) definitely helps with learning, it doesn’t seem to do so by lowering the mental effort (via cognitive load) needed to process new information. This challenges earlier ideas that positive emotions expand your mental resources.

Emotions aren’t set in stone and shift while you learn. For example, frustration at the start of a tough task can turn into satisfaction as students figure things out. So, frustration isn’t always a bad thing—it can be part of the process. Emotions also shape cognitive load. We usually think of cognitive load as tied to how complex the information is, but this study points out that emotions directly influence how much mental effort a task feels like it takes. Finally, not all positive emotions are equal. While being in a good mood generally helps, too much excitement or overconfidence can backfire. Students might rush through material, miss details, or oversimplify because they are ‘feeling too good’. This last point is important. 

Emotions and learning

Affective learning deals with the emotional side of learners, their emotions and feelings. These feelings cover a wide range of positive and negative attitudes, interests, beliefs and motivations before, during and after learning. 

Teachers, lecturers and trainers are professional learners and often understate the role that emotions play in learning. Yet speak to any learner and many learners will report not what they achieved in learning but how they felt. Few get through school without feeling bored or indifferent to lessons and subjects that seem dull, remote and irrelevant. Fewer still get through a degree without feeling numbed in boring lectures. On the other hand, successful learners report excitement, engagement and feelings of pride and achievement. The point is that this can go both ways.

This complex world of feelings and emotions is often sidelined by the dominance of the purely rational, academic cognitive side of learning theory. This is partly down to the dominance of Bloom’s silly taxonomy, the cognitive domain being only one of three, the other two the psychomotor and affective are often completely ignored. 

Kahneman

Kahneman posits the idea that we have two brains, in Thinking Fast and Slow; System 1 - fast, emotional and instinctive, also System 2 - slower and rational. I am no longer convinced that the distinction is as clear as we think it is (sometimes expressed as elephant and rider). Our brain has substantial weaknesses, due to its long and messy evolutionary history. We know that it stubbornly procrastinates, fails to remain attentive (attention being a necessary condition for most learning) and easily distracted. It is also subject to emotional pulls and mood swings, even depression. This is both a blessing and a curse. The emotive dimension of learning is often underestimated but it can also distract and over-stimulate. 

Krathwohl

Everyone knows Bloom, but we hear little about the man who completed Bloom’s work in the affective domain, the less known David Krathwohl. Although Bloom's original taxonomy consisted of six categories, when Krathwohl revised it in 2001, he put emphasis on the interaction between the cognitive and affective. With Lori Anderson he also helped reduce Bloom’s cognitive taxonomy down to four categories or knowledge dimensions:

   Factual knowledge

   Conceptual knowledge

   Procedural knowledge

   Metacognitive knowledge

For each of these four, smaller dimensions were identified. He also changed the cognitive processes to verbs and renamed Evaluation and Synthesis as Creation.

Krathwahl then proposed six levels of affective learning:

   Characterization

   Organization

   Valuing

   Responding

   Receiving

My own view is that this Affective Taxonomy suffers from the same hierarchical rigidity as Bloom’s taxonomy in the Cognitive domain. It is far too rigid and hierarchical. Some even argue that there is no real taxonomy of affective learning as it emerges from or is part of the cognitive domain. Affective factors are also difficult to identify and assess as they involve feelings, attitudes, and beliefs, so ignored as something difficult to measure, vague and unimportant. While there is recognition that feelings and emotions play a strong role in motivation and learning, it is rarely be seen as being on a par with its cognitive counterpart.

There is certainly the tendency for schools and academia to focus on text-based, pure reason, as their primary skillset, at the expense of other aspects of learning. This has led to a paucity of research in the area. 

Speak to workplace trainers or sports coaches and you will hear far more about affective learning, as it really does matter. These are ever present in learning and can also be internalised, either to hinder learning or harnessed and used positively by the learner to move forward. So feelings play a strong role in both demotivation and motivation. Understanding their role is essential if you are a learning professional, yet few could name a single theorist in this area.

Panksepp

Jaak Panksepp introduces the evolutionary origins of emotions and warns us that although emotions are vital in learning, they can also hinder learning. Panksepp saw life as being empty without emotions, emotions being survival features, as part of our evolutionary heritage. We do not teach or learn these seven PRIMARY affective systems, as they are innate:

  SEEKING (expectancy)

  FEAR (anxiety)

  RAGE (anger)

  LUST (sexual excitement)

  CARE (nurturance)

  PANIC/GRIEF (sadness)

  PLAY (social joy)

We can learn from these emotions, but we do not learn them, only learn to modulate them,  He did think that they formed the basis of our personality, different emphases producing different personality types. SECONDARY emotional processes are learnt through classical and operant conditioning and TERTIARY emotions are sensory (taste, pleasure, pain) and homeostatic affects (hunger and thirst). 

We, unlike animals, are cognitive creatures, but he regrets the common disregard of emotions and our evolutionary heritage in understanding the foundations of learning and higher cognitive processes. Much of what is presented in traditional learning theory, whether rewards, punishments or reinforcements actually rely on the emotional responses of the brain. Yet emotions are a double-edged sword.

Some emotions, such as RAGE, FEAR and PANIC are not conducive to learning and may inhibit or hinder learning. On the other hand, learning may benefit from the SEEKING emotion, with its feeling of enthusiasm, as it is instinctive for survival, it promotes learning through purpose, anticipation and curiosity. Its absence diminished a disposition towards learning.

Damasio & Immordino‐Yang

Damasio & Immordino‐Yang see emotions and reason as entwined or enmeshed. They not only not only regulate our lives, they regulate learning. Emotion is therefore critical to learning and memories well as playing a powerful role in learning as motivators.

Conclusion

A complex area but we must be careful of being too shallow on our consideration of emotions. It may be that acts of learning or thinking induce emotions, not that emotions are always the well spring for learning. It is also clear that they may limit, cap or damage learning. We must keep a close eye of the detailed research in this area, rather than trite statements about the important and efficacy of learning. 

 

Do DEI programmes need a reality check?

You may feel uncomfortable reading this, as it is a contentious area. But contention must not be left to fester, sides becoming increasingly polarised. 

Companies and educational institutions are pouring billions into Diversity, Equity, and Inclusion (DEI) initiatives. But do they work? DEI has to face up to the evidence, and there is a case for rethinking diversity training. Are we getting it all wrong with billions spent, but does DEI deliver? Mandatory training seems to have a backlash problem and unintended consequences. Turns out, the evidence does not support this spend. Could this training be hurting more than helping and do they need a reality check?

PAPER

This paper by the NCRI points out some serious flaws in some DEI approaches, and adds to the growing body of larger and wider studies that came to the same conclusion.

They mostly have no significant effect as they focus on the wrong thing – training not process. Indeed, some DEI programs stir up resentment and suspicion, making things worse rather than better. 

Mandatory training has led to a backlash, as Forcing people into diversity training can create discomfort and even hostility. Nobody likes feeling blamed and you donpt win people over by accusatory tactics.

‘Anti-oppression’ rhetoric tends to polarise people as it seems explicitly political. Materials (identified in the paper) that emphasise systemic oppression and victimhood often make participants view others through a hostile lens.

Another danger is that perception becomes reality, where DEI narratives lead people to see prejudice and unfairness in neutral situations. This exposure to anti-racism or anti-Islamophobia narratives can increase participants’ willingness to punish perceived ‘oppressors’ without clear proof of wrong-doing.

There is also an interesting  discussion in the paper surfacing DEI narratives to controlling tendencies. Some DEI content appears to foster controlling and punitive mindsets, authoritarian traits of authoritarianism This is interesting, and I true to a degree.

CONCLUSION

I’ve been writing about this for nearly 20 years and this is yet another paper that calls for an honest evaluation of this gargantuan spend and shines a much-needed light on the unintended consequences of many DEI approaches. It’s shocking that so much money is spent on DEI without robust evidence of effectiveness or harm mitigation. Evidence like this should be a wake-up call for organisations to rethink how they handle diversity training—but it won’t. Why not? We are in an evidence-free zone, where even discussion of the evidence cannot happen. That’s why The New York Times and Bloomberg shelved coverage 

EVIDENCE

There is a consistent theme here - mandatory, accusatory, or one-off DEI efforts can do more harm than good. The key is building trust, creating buy-in, and focusing on systemic change rather than short-term fixes. On that front I highly recommend Simon Fanshawe’s excellent book ‘The Power of Difference’. He was a founder of Stonewall and has fought for Gay rights all of his life.

An early study, Kidder et al. (2004), tackled the backlash that DEI programs can trigger when framed as affirmative action. Turns out, calling it affirmative action often sets off alarms about reverse discrimination, making people feel unfairly targeted. Instead of promoting diversity, it can lead to resentment, undermining the very goal of the programs. This research was one of the first to flag the danger of how DEI is framed.

Then along came a big one Kalev, Dobbin, & Kelly (2006), who found that mandatory diversity training doesn’t just fail—it can actually backfire. Employees often resist being told they have unconscious biases, which can foster defensiveness. The researchers suggested that voluntary training or pairing it with programs like mentorship schemes works far better, making employees feel part of the solution rather than the problem. Legault, Gutsell, & Inzlicht (2011) looked at anti-prejudice messaging and found something surprising: when people feel pressured or coerced into thinking a certain way, they dig their heels in. Instead of reducing bias, these messages can actually make it worse. Giving people more autonomy in how they engage with DEI efforts might be a smarter move. Fast forward a few years, to Moss-Racusin et al. (2016) who found that programs dangling carrots or sticks to enforce diversity goals often fail. People respond better when their motivation comes from within. The takeaway? If you want people to embrace DEI, make it about shared values, not external pressure.

Yes it’s Dobbin & Kalev (2016) again, showing why so many DEI programs flop. Surprise, surprise, forcing people to participate doesn’t help. In fact, mandatory sessions can spark resistance. Instead, they found that voluntary programs, mentorships, and leadership support yield better results by creating buy-in and trust. Leslie (2019) shines a light on unintended consequences. Leslie found that bias-focused training can backfire by reinforcing stereotypes and creating perceptions of unfairness. The lesson? A clumsy approach to diversity can end up dividing people rather than bringing them together.

More recently we have a meta-analysis is a big deal because it pulled together loads of research on DEI training by Moss-Racusin et al. (2016).  The verdict? There’s not enough evidence showing these programs work, especially the quick-fix ones like single seminars. Long-term, systemic change is what’s needed, not just ticking the “we did training” box. Iyer (2022) then dug into why people oppose DEI programs, especially those from so-called “advantaged” groups. The big issue? They often feel threatened or unfairly targeted. Ignoring their concerns only fuels resistance, so a more inclusive approach that engages everyone might be the way forward.

Burnett & Aguinis (2024) pointed out that DEI training aimed at specific groups can lead to discomfort and defensiveness, which defeats the purpose. They suggest reframing these programs to be less accusatory and more about shared goals to avoid making people feel unfairly singled out.

Bibiography

Kidder, D. L., Lankau, M. J., Chrobot‐Mason, D., Mollica, K. A., & Friedman, R. A. (2004). Backlash toward diversity initiatives: Examining the impact of diversity program justification, personal and group outcomes. International Journal of Conflict Management, 15(1), 77–102. https://doi.org/10.1108/eb022908

Kalev, A., Dobbin, F., & Kelly, E. (2006). Best practices or best guesses? Assessing the efficacy of corporate affirmative action and diversity policies. American Sociological Review, 71(4), 589–617. https://doi.org/10.1177/000312240607100404

Legault, L., Gutsell, J. N., & Inzlicht, M. (2011). Ironic effects of antiprejudice messages: How motivational interventions can reduce (but also increase) prejudice. Psychological Science, 22(12), 1472–1477. https://doi.org/10.1177/0956797611427918

Moss-Racusin, C. A., van der Toorn, J., Dovidio, J. F., Brescoll, V. L., Graham, M. J., & Handelsman, J. (2016). A “scientific diversity” intervention to reduce gender bias in a sample of life scientists. Cultural Diversity and Ethnic Minority Psychology, 22(3), 295–306. https://doi.org/10.1037/cdp0000070

Dobbin, F., & Kalev, A. (2016). Why diversity programs fail. Harvard Business Review, 94(7-8), 52–60. https://hbr.org/2016/07/why-diversity-programs-fail

Leslie, L. M. (2019). Diversity initiative effectiveness: A typological theory of unintended consequences. Academy of Management Review, 44(3), 538–563. https://doi.org/10.5465/amr.2017.0087

Paluck, E. L., Porat, R., Clark, C. S., & Green, D. P. (2021). Prejudice reduction: Progress and challenges. Annual Review of Psychology, 72, 533–560. https://doi.org/10.1146/annurev-psych-071620-030619

Iyer, A. (2022). Understanding advantaged groups’ opposition to diversity, equity, and inclusion (DEI) policies: The role of perceived threat. Social and Personality Psychology Compass, 16(4), e12666. https://doi.org/10.1111/spc3.12666

Burnett, L., & Aguinis, H. (2024). How to prevent and minimize DEI backfire. Organizational Dynamics, 53(2), 100981. https://doi.org/10.1016/j.orgdyn.2023.100981

Fanshawe, S. (2021). The power of difference: Where the complexities of diversity and inclusion meet practical solutions. Kogan Page Publishers.