Whereas artificial intelligence is reinventing Web writing, the written word has never been more valuable. Selim Niederhoffer, a copywriting trainer and bestselling author, has recently been exploring how marketing professionals can still succeed amidst “enshitification“, online influence, and automation. Meet an expert who remains confident in the power of words.

Selim, a seasoned copywriter and author is keen on using what he calls “magic words.” Ask him why and his answer might surprise you in this age of sheer automation. “I’ve already been thinking about magic words for years. I want to dig deeper, show real examples and, most importantly, explain why they actually work.”

His approach remains resolutely traditional.

“I genuinely work with a pen and paper.”

This approach reveals something fundamental about Web writing: it works when you truly understand the psychology behind it, not when you just mechanically apply copywriting techniques. Selim bases his work on well-established principles of persuasion.

“When there’s a principle of persuasion, there’s usually a word that goes with it. Take urgency or scarcity, for example. If something’s rare, that means limited places, running out of stock… that sort of thing. That’s how I build my word cluster.

The research then extends to field observation. “I look at what my clients are using, what’s going on at Burger King, McDonald’s, Nike. I check out the major brands too – what they’re doing on YouTube, on LinkedIn.”

Eventually, Selim identified 55 magic words but trimmed them down to 50. It’s an approach that perfectly shows what still sets humans apart from machines: the ability to critically analyse and curate with discrimination.

However, it’s worth adding nuance. Selim can’t conceal that he genuinely “loves” ChatGPT. As we’ll see later, this raises some legitimate questions.

Among the 50 words he’s analysed, the first is also the simplest: thank you! For Selim, this should be essential for every business. “How many times have you walked out of a shop where the sales assistant just didn’t seem to have noticed you? Whether you bought anything, or didn’t doesn’t make any difference, once the transaction’s done, you’re out the door.”

Yet some brands know how to thank their customers. “Nespresso or Apple, for example: the Nespresso employee comes out from behind her counter, she hands you your product. Thank you for your visit. Have a nice day! That’s how it should be.”

For Selim, saying “thank you!” is more than just being polite, it’s a way of life. “You can go further: thank you for your visit, thank you for subscribing to the newsletter, thank you for your comment. You need to constantly think in terms of gratitude.”

This approach fits into what Gary Vaynerchuk called The Thank You Economy. “We’re in an attention economy,” Selim explains. The stakes are high in Web writing: how do you maintain this human dimension at corporate scale?

“For me, the essence of business is that there’s a person in front of you who’s exchanging something with you. That’s really the foundation. But today, how do you keep that at corporate scale?”

Data confirms an intuition: gratitude improves customer experience and encourages loyalty. A valuable lesson for all those who practice Web writing and seek to create a lasting connection with their audiences.

The conversation inevitably turns to artificial intelligence. For Selim, AI is first and foremost an incredible productivity tool. “If I’ve got a newsletter to write, I’ll use AI. Sales pages? AI. The thing is, AI works brilliantly for me. I’ve even become clearer in my writing,” he admits without hesitation.

But he’s not entirely starry-eyed about it. He’s identified three major pitfalls that professionals absolutely must avoid.

For him, the key questions are: how do you refine AI, how do you avoid its main pitfalls, how do you stay in control and how do you harness all its power?

This evolution shows a profound shift in the profession: the copywriter is becoming an orchestrator of AI platforms.

Selim warns against a hidden danger: declining skills. He reminds us that the brain is a muscle, and using it creates connections. However, if we stop using it, those connections are lost. He admits that he has fallen victim to this himself.

“I have noticed that my writing isn’t as fluid as it used to be when I’m starting from scratch. Between 2010 and 2022, I was churning out three to five blog posts a week. Now, if I can just knock up a prompt, get a result and tweak it a bit, job done. But it’s less satisfying.”

This awareness led him to experiment.

“I run A/B tests. Send out version A written by AI, version B written by me, then see who clicks more. I check which headlines work best, which text performs better,” he explains.

It’s a data-driven approach that could bruise his ego, he admits with a laugh, but it’s essential for understanding what genuine human value looks like in Web writing.

The final question comes naturally: will humans still add value compared to machines in a year or two? Selim remains cautious about making predictions. “I’m rubbish at forecasting because I don’t spend enough time on it,” he admits candidly. But his reading of the market reassures him.

While I can see companies increasingly launching AI training for their staff, there’s always the question of depth. Are they adequately trained and properly supported?

“I meet marketing directors who want me on their pay-roll. We’ve got so much work to get through that we’d need ten people using ChatGPT just to keep up,” he reports.

This suggests that AI isn’t so much replacing writers as it is multiplying opportunities for content production.

Selim’s thoughts tap into bigger questions about how work is evolving.

“The idea of heading towards a society where people become useless doesn’t surprise me. I’ve seen it enough times in sci-fi,” he confides.

This vision actually mirrors history: villages like ours in the Pyrenees, with thousands of inhabitants at the turn of the 20th century now down to a few dozens. Economic and technological shifts have always redrawn the employment map.

This conversation with Selim Niederhoffer sketches out the shape of Web writing as it transforms. Words remain magical, but how we summon that magic is changing.

AI’s becoming a powerful content production tool, but the real value still lies in properly understanding how persuasion works, in being able to orchestrate these tools cleverly, and in keeping a human tone of voice.

Here’s the paradox: just when machines can churn out infinite text, what becomes scarce is quality strategic thinking, subtle psychological understanding, and the ability to inject real personality into content. Selim’s magic words are just a a beginning – they are mere ways of understanding what makes human readers tick.

According to Selim, tomorrow’s Web writing will be hybrid – a collaboration between human and machine. It will demand technical mastery of AI tools, but it’ll be rooted in a deep understanding of how persuasion actually works.

In short, words will always be magical, but that magic needs cultivating, training, constant work – otherwise it will erode in the face of automation’s easy appeal.

One needs to keep reading, discussing and engaging with real people. That’s how we will train our brains and hang onto that human edge that makes all the difference in Web writing.

Here are 10 words taken from Selim Niederhoffer’s book “Les Mots Magiques” (Magic Words), which are effective words to boost your marketing content and encourage action:

The post Web writing : words retain all their magic appeared first on Marketing and Innovation.

Commentary on the environmental impact of AI often swings wildly between doom-and-gloom catastrophism and blind techno-optimism. But where’s the truth in all this? On July 24, 2025—the symbolic date of Earth Overshoot Day—we sat down with Yves Grandmontagne, founder and editor-in-chief of DCMAG (Data Centre Magazine*), to get his take on AI and its real environmental impact. It is worthy of note that Yves and I explored Silicon Valley’s infrastructure innovators together through extensive press tours some time ago. This provided us with firsthand insight into the tech industry’s approach to these challenges. The current annotated transcript of our interview is a summary of our thorough, nuanced, and let’s admit it, quite lengthy discussion. You are therefore encouraged to treat this article as your starting point for diving deeper into this extremely complex topic.

* DCMag is only available in French

This post summarises what turned out to be an incredibly rich hour-long conversation. The sheer complexity of this topic forced us to dig into multiple technical, economic, and environmental angles—making any kind of comprehensive analysis near inconceivable.

Drawing on his deep expertise in the data centre and AI sectors, Yves Grandmontagne gives us some much-needed factual perspective on a debate that’s often polarised between doomsday scenarios and over-the-top techno-optimism. To tackle this properly, we decided to take recent quotes—both positive and negative—and fact-check them with our expert.

Yves’s analysis helps us cut through the noise and understand what’s really at stake in this technological breakthrough.

The electricity consumption issue is more nuanced than you think* – AI will represent 20-30% of data centre consumption (not twice that number), and only 2-4% of overall electricity consumption

*All numbers by Yves Grandmontagne at Data Centre Magazine

Bottom line: The impact is “real but massively overstated”—we need to put things in context and remain cool and collected.

So here are the quotes about the environmental impact of AI that we wanted to fact-check with Yves.

The first claim I put to Yves Grandmontagne was Synth Media’s prediction [Fr] that “AI’s growth could double data centre electricity consumption by 2026.” His response immediately throws cold water on this alarmist take:

“It’s absolutely true that AI is rolling out infrastructure at breakneck speed and eating up more space in data centres. Sure, it’s going to significantly bump up their energy consumption—no question about that. But will consumption actually double? I seriously doubt it. AI should account for somewhere between 20 and 30% of global data centre consumption worldwide.”

AI should account for somewhere between 20 and 30% of global data centre consumption worldwide

This reality check reveals something consistent throughout Yves Grandmontagne’s analysis: the absolute need to put numbers in context. The expert points out that this increase is just part of the natural progression tied to our ever-growing digital habits. “What’s driving this consumption increase is our daily usage, whether that’s for work or for personal reasons,” he reminds us, highlighting our collective responsibility in this evolution. It’s a topic we’ve tackled before with a broader focus on digital consumption.

The most striking part of his analysis is about the energy efficiency angle. Contrary to popular belief, data centres aren’t following a consumption curve that mirrors data growth. This improving efficiency is something that gets completely overlooked in public debates about the environmental impact of AI.

When it comes to the 10,113 tonnes of CO2 equivalent attributed to ChatGPT usage in January 2023 (Basta Media – Data for Good, AI has the potential to destroy the planet), Yves Grandmontagne takes a refreshingly pragmatic approach:

“I can’t verify that exact figure. Getting that precise—down to 10,113 tonnes—represents a massive methodological challenge, especially when you’re dealing with AI infrastructures that are distributed systems.”

This observation raises a crucial methodological point: just how tough it is to accurately measure the carbon footprint of distributed infrastructures. The expert does acknowledge this pollution is real, but puts it in perspective: “Those 10,113 tonnes of CO2 still represent volumes significantly smaller than what many other industries pump out.”

This contextualisation isn’t about downplaying the issue—it’s about keeping things proportional. Yves Grandmontagne reminds us of a basic truth that is often overlooked: “The moment we use our smartphones, we become CO2 producers.” This highlights the inconsistency in criticisms that single out AI from our overall digital consumption.

MIT’s claim that “a ChatGPT query uses ten times more electricity than a Google search [p 9]” perfectly illustrates the danger of oversimplified comparisons, according to Yves Grandmontagne. His take is particularly eye-opening:

“This comparison just doesn’t work. I think we’re making a fundamental methodological error here because we’re comparing two completely different technologies. Google is a search engine that gives you results that you then have to sift through to find what you’re actually looking for. ChatGPT, on the other hand, serves up information that’s already structured and ready to use.”

This analysis shows just how sophisticated you need to be when evaluating the environmental impact of AI. A ChatGPT query might actually replace multiple Google searches plus visits to various websites. That makes direct comparison pretty meaningless. This distinction will probably become moot anyway with the rollout of Google AI Overviews, which will integrate similar functionality to ChatGPT.

One of the most revealing parts of our interview dealt with data centre water consumption. Yves Grandmontagne draws a crucial distinction between American and European practices:

In the United States, when you install a data centre, you’re a private company reaching out to other private companies for water on one side, electricity on the other. And the utility companies that manage energy or water are thrilled to have a massive client that’ll absorb a chunk of their production.

This geopolitical insight explains those 5.4 million litres of fresh water attributed to ChatGPT-3 training. The issue isn’t the technology itself—it’s local practices and regulations. In Europe, our expert reminds us, “we’ve been developing infrastructure cooling systems that work in closed circuits or are air-based rather than water-based for quite some time”.

Take Orange’s data centre in Val de Rueil, for instance—it’s cooled by the crisp Normandy air. The only exceptions are during heat waves, which are naturally time-limited.

Yves Grandmontagne’s technical explanation demystifies the cooling process: “Water acts as a conductor to capture heat.” He then breaks down the dual-circuit system that protects infrastructure while managing thermal exchanges. This approach reveals that warm water discharge from data centres (~20-25°C) stays well below that of nuclear plants (27-35°C for the Gravelines nuclear plant in the North of France). That gives us a useful comparison point for our debate.

Warm water discharge from data centres (around 20-25 degrees) stays well below that of nuclear plants (27-35°C for Gravelines)

We should note that these figures vary depending on location, time of year, and technological choices. A 12°C increase in the North Sea, even if reports show nothing concerning at the macro level, probably isn’t neutral at the micro level over the long term. Similarly, warm water discharges from data centres probably aren’t completely neutral either. Another point worth nuancing, though we should be cautious here.

The expert also highlights emerging technologies like Direct Liquid Cooling (DLC) and immersion, which operate in closed circuits and drastically cut water consumption. This technological evolution shows the sector’s ability to adapt to environmental challenges.

More importantly, the above claim about water consumption was only related to the training of ChatGPT. This operation occurs only once, unlike the everyday usage by individuals worldwide, multiplied by hundreds of millions of users. This leads to staggering amounts of consumption. Once again, this highlights our individual responsibilities regarding energy and water consumption in data centres and AI.

Looking at data centre moratoriums reveals some pretty contrasting situations. Yves Grandmontagne particularly highlights the Irish case. “Remember, Ireland was Europe’s poorest country 30-40 years ago [Editor’s note: the podcast figure is off]. To build an industry and attract capital, they found this solution.”

And it worked—Ireland’s GDP per capita now exceeds $100,000, putting it amongst the world’s best in 2025. [Editor’s note: there’s an infographic with sources at the end of this article]

Today, 30% of Irish energy production goes to data centres. Yves’s reaction? “That’s clearly way too much!” This extreme situation shows the risks of an unregulated approach, but also how governments can respond intelligently. “The Irish government didn’t say ‘no more data centres’—they decided to make new construction conditional on implementing solutions.”

The pragmatic approach adopted by the Celtic Tiger contrasts sharply with outright bans. It offers a middle ground between economic development and environmental constraints.

One of the biggest revelations from our interview concerns identifying the real bottleneck. “The real problem isn’t production—it’s the grid. The energy grid, meaning getting energy to where it’s actually consumed.”

This analysis puts debates about the environmental impact of AI in a whole new light. France, Yves Grandmontagne reminds us, “is completely energy self-sufficient in production.” But it faces transport infrastructure challenges. This technical perspective shows that solutions don’t necessarily lie in cutting consumption, but in optimising distribution.

On global statistics, Yves Grandmontagne sets the record straight: “Currently, we’re more like 2%. And we’re heading pretty fast towards 4%.” These figures, relative to global electricity consumption, give us some useful perspective.

The expert insists on keeping these percentages in context: “When you pull out a graph showing energy consumption across all industries, if you put industry and data centres side by side, you realise that the latter don’t weigh much.”

The Real Energy Consumers

To provide further context, Yves Grandmontagne lists the truly energy-intensive sectors: “There’s transport, industries, steelworks, agriculture… The latter consumes enormous amounts of energy. We often forget to mention this.”

This perspective reveals according to our research that the chemical industry represents 32% of French industrial energy consumption, offering a striking comparison point with the 2-4% of data centres globally.

Let’s now turn to techno-solutionists and examine their claims clinically as well.

Sam Altman’s declarations that “1% of global electricity to train powerful AI would be a massive victory” also find nuanced resonance with Yves Grandmontagne: “It’s true that powerful AI called ChatGPT will consume perhaps 70% of global AI consumption worldwide.”

This analysis reveals the economic reality behind the declarations: Microsoft invested 10 billion in OpenAI, creating a temporary quasi-monopoly situation. The expert underlines the mechanical nature of this consumption: “You need computing. Computing consumes energy. And since we use more and more of it, it consumes more and more energy. That’s normal.”

Satya Nadella’s (Microsoft) vision that “AI can be a powerful accelerator for addressing the climate crisis” finds technical justification with Yves Grandmontagne: “The real problem isn’t production, it’s energy transport.”

The expert explains that managing a complex energy mix (nuclear, renewables, fossil fuels) requires sophisticated piloting tools: “We can only do this efficiently, we know today, by using AI tools.”

This technical perspective reveals that AI isn’t just an energy consumer, but potentially an optimiser of the global energy system. However, Yves Grandmontagne tempers ambitions: “It will be a combination. It’s not one element alone that can help.”

It also seems highly doubtful to us to claim that AI, however much a “pharmakon” it may be, can be a poison that serves as a universal remedy to this problem that far exceeds it, as we’ve written previously.

Microsoft’s announcement regarding its “zero water” cooling plan illustrates the sector’s technological evolution. Yves Grandmontagne confirms: “This isn’t greenwashing. It’s not really an initiative either—this stuff already exists.”

The expert details the new technologies needed to cope with the dramatic increase in power requirements:

When we were using conventional technologies until now, we had between 5 and 10 kilowatts coming into a rack. When you add AI, at minimum, you’re consuming 80 kilowatts or you’re consuming 150 kilowatts.
This progression forces the adoption of Direct Liquid Cooling or immersion, technologies that operate in closed circuits. “That’s the direction of technological evolution for cooling AI data centres,” he concludes.

A particularly revealing statistic emerges from the interview:

Over the last decade, the number of data centres has doubled, their floor space has quadrupled, and their energy consumption has only increased by 6%. This data illustrates considerable progress in energy efficiency, often overlooked in debates about the environmental impact of AI. It reveals the sector’s capacity to reconcile growth with energy optimisation.

An often-overlooked aspect of the debate lies in AI geography. Yves Grandmontagne reminds us: “For now, all AI production happens in the United States, not Europe.” This geographic concentration puts European criticism in perspective whilst highlighting our technological dependence.

The expert mentions the €109 billion in AI investments announced by President Macron, of which “a third, or even half will concern data centres.” This strategy aims to reduce our dependence whilst developing a more efficient European sector.

Yves Grandmontagne’s analysis reveals the weaknesses of the American model: “In the United States, they have major weaknesses” regarding energy transport. This infrastructure failure partly explains the criticised overconsumption and justifies the different approaches adopted in Europe.

Meta’s project in Texas (5 gigawatts across an area equivalent to Manhattan) illustrates this problem: “Meta doesn’t have the energy, so it’s looking for it.” This situation contrasts with Europe’s more integrated and regulated approach.

Sundar Pichai’s declaration (Google) that “AI is more important than fire or electricity” finds nuanced resonance with Yves Grandmontagne: “It’s a real revolution that will go much further than previous ones, because those were more industrial. This one is a revolution with an impact on our daily lives.”

This analysis highlights the specificity of the AI revolution: its speed and penetration into daily life. “In barely two years, it’s been a real wave that’s engulfed us,” observes the expert, contrasting with the hundreds of thousands of years it took to adopt fire.

Beyond environmental questions, Yves Grandmontagne expresses his social concerns:

I’m amongst those who still worry that it will massively impact jobs, and I think we need to be realistic about this.

This social dimension of the environment reveals the complexity of trade-offs to come. The expert nevertheless encourages adoption: “We mustn’t cut ourselves off from this potential either, because extraordinary things will come to fruition soon.” It’s not easy to form an opinion on this employment issue given how contradictory the viewpoints are.

Yves Grandmontagne’s final message to students (since this post was created in preparation for our course on AI and content creation) particularly resonates:

Be careful about the discourse you listen to, who’s behind it, etc. It’s easy to twist information
This warning against disinformation highlights the political instrumentalisation of the environmental debate.

An often-overlooked perspective emerges: “When a data centre sets up in a city, in its wake, an entire digital economy develops. But we forget to mention that.” The expert quantifies: “One job created in a data centre means 100 jobs behind it that will be developed in the region.”

This economic dimension reveals the complexity of territorial trade-offs and the need for global rather than sectoral approaches.

Yves Grandmontagne’s expertise reveals a landscape far more nuanced than polarised environmental debates suggest. His main conclusions deserve to be retained.

The environmental impact of AI is “real, but largely overestimated,” in his words. This reality can be explained by several factors: the remarkable progress in energy efficiency, the relativity of consumption compared to other industrial sectors, and the constant technological evolution towards greater efficiency.

Geography matters enormously in this equation. American practices, often criticised, don’t reflect the more regulated and efficient European approaches. This geographical distinction invites us to contextualise criticism and encourage good practices.

The technological future is heading towards more efficient solutions: closed-circuit cooling, proximity AI, energy optimisation by AI itself. These developments suggest that current problems are transitional and technical rather than structural.

Finally, the systemic dimension revealed by this analysis invites us to move beyond sectoral approaches. AI isn’t just an energy consumer, but potentially an optimiser of the global energy system. This systemic perspective may be the key to truly sustainable artificial intelligence development.

Yves Grandmontagne’s message resonates as a call for nuance and critical vigilance: “Let’s remain cautious and serene.” In an often emotional debate, this technical wisdom offers precious guidance for navigating between the pitfalls of denial and catastrophism. This more nuanced discourse is undoubtedly less marketable than extreme opinions, which fuel coffee shop chatter and social media.

This interview ultimately shows that the question of AI’s environmental impact doesn’t have a simple answer, but requires a systemic approach that’s geographically situated and technically informed. It makes up an essential starting point for all those who wish to move beyond preconceived ideas and contribute to a genuinely constructive debate about the future of our digital society.

Infographic on AI’s Environmental Impact

The post The Truth About the Environmental Impact of AI appeared first on Marketing and Innovation.

Will the AI bubble burst or is GenAI here to stay? The artificial intelligence industry is experiencing unprecedented financial euphoria. Yet, the current situation is very confusing. AI investments are reaching dizzying heights. Let’s mention OpenAI’s $40 billion funding round at $300 billion valuation and Mistral AI’s €1.7 billion funding round. Yet, some commentators are very critical of the situation. For instance, Ed Zitron predicts that the AI bubble will burst in Q4 2025. All this is fueling intense, rather than rational, debate. I wanted to confront these concerns with the expertise of Bernhard Schaffrik, Principal Analyst at Forrester Research. His analysis is insightful and nuanced. In his mind, there will be some sort of correction, but at the same time, GenAI is too popular to disappear.

Forrester’s Bernhard Schaffrik is recognized as one of the most insightful experts in artificial intelligence. He provides a nuanced analysis that transcends simple financial considerations. His perspective on the AI bubble burst scenario offers first-hand insights for understanding where this transformative technology is truly heading.

The question of a potential AI bubble burst cannot receive a univocal answer. As Bernhard Schaffrik rightfully points out, it all depends on one’s perspective. This duality of vision probably constitutes one of the keys to understanding the current situation and the likelihood of an AI bubble burst.

“It’s almost impossible to get a one-sentence response from an analyst. Allow me two sentences. Number one is, of course, it always depends on the role or the profile you’re asking. If we are talking about financial investors, then yes, there are strong signals of this being a bubble because there is so much money being pumped into it—more than $120 billion US dollars in capital expenditure on AI infrastructure alone, just by the Magnificent Seven tech providers. So that bubble could burst,” explains Forrester’s expert.

This assessment gains particular relevance when considering Google’s $9 billion AI data center investment in Oklahoma for advanced AI training infrastructure.

This financial perspective, however, tells only part of the story. Technological adoption follows a different logic from financial markets, as Schaffrik confirmed during our exchange about the AI bubble burst potential.

“But now, if you put yourself in the shoes of enterprise decision makers, tech decision makers, also AI users, there are many who would say, ‘I don’t care if that bubble bursts, the technology is there, and it won’t go away.’

“Regardless of the amounts all the financial transactions surrounding the AI industry, people are actually using this technology. And they like what they are seeing. It might not be the disruptive, transformative value some are surmising. It’s probably more incremental than that, but the adoption of that technology is undeniable.”

Fortune’s analysis reveals a concerning gap between current investments and generated revenues. To justify current investments, AI companies would need to generate $40 billion in annual revenue, while they currently produce only $15 to $20 billion.

I was wondering whether this $20-25 billion gap could represent a systemic risk that could trigger an AI bubble burst.

Schaffrik remains relatively optimistic on this point: “There is still enough money in that market to back these revenue gaps at least for a while. And what I’m also seeing is that especially when it comes to the largest enterpriseson the planet, they are convinced to continue using that software. And if it comes at a premium which is decent, arguably, maybe a couple percentage points higher than what they are paying today for the software, then this seems to be acceptable.”

This acceptance of additional costs by large enterprises stems from the incremental value they perceive, even if it hasn’t yet reached the promised transformation level that might prevent an AI bubble burst scenario.

A particularly troubling element in the current ecosystem is the recent NewsGuard study revealing that major LLM systems are no longer progressing but regressing, generating more hallucinations and errors. This observation raises fundamental questions about current technology maturity and its impact on AI bubble burst predictions.

“I’m not saying that LLMs and generative AI are progressing in a linear fashion nor that this technology will be disruptive in any way, despite the promise. As we have seen with emerging technologies for decades and even centuries, it takes breakthrough technological revolutions rather than evolutions to fulfill such promises,” analyzes Schaffrik.

This vision of the current limitations of AI doesn’t diminish Bernhard’s long-term optimism: “But I’m also convinced that these breakthroughs will happen, not within the next seven, eight, nine, 12 months, but maybe in the long term. Something else will be coming up.”

One of Schaffrik’s most compelling criticisms concerns the energy efficiency of current systems. His comparison between the human brain and data centers is striking and relevant to understanding whether we’re facing an AI bubble burst.

“If we look at the amount of energy our brains are requiring to create a certain inference, and how much an LLM would require to achieve the same result with electricity, this cannot be the way forward.”

This energy inefficiency constitutes a major barrier to scalability and will require significant technological breakthroughs to overcome, potentially influencing AI bubble burst timing.

The 95% failure rate of corporate AI pilots revealed by MIT research might seem alarming and suggestive of an impending AI bubble burst. Yet Schaffrik places this figure in its historical context: “It’s quite normal. As an analyst covering innovation management, what I have been observing over time is that about 10% of all innovation-related minimum viable products, proof of concepts, pilots, will turn into a product.”

The problem would rather lie in unrealistic expectations: “Everybody rushed at it because one believed that since it’s accessible through natural language, it should  be easier to deploy, to implement, and there are no drawbacks and negatives. That might explain that the failure rate is slightly higher than with technologies we saw in the past.”

This assessment aligns with Gartner’s prediction that 30% of GenAI projects will be abandoned after proof of concept by 2025 due to poor ROI and unclear business value.

Despite current limitations, Schaffrik maintains his bold prediction from his July 2025 analysis “Demystifying Artificial General Intelligence” that Artificial General Intelligence (AGI) represents “the biggest change in tech we have ever seen and is starting right now.” This vision, which could influence AI bubble burst scenarios, is structured around three maturity stages.

A crucial point raised in our discussion concerns the difference between training and experience. As I pointed out to Schaffrik, experience develops critical thinking that current LLMs don’t yet possess, which could impact AI bubble burst predictions.

“We might get to a point where most of us humans wouldn’t be able to tell if on the other side, a human or a machine is interacting with us. There will be areas where we will still be able to tell. But experience is something we could at least partially solve with more data and better data,” states Schaffrik.

The solution, according to him, lies in massive data collection: “So much of the billions of investment money flowing now into all these big companies is also to collect and curate data also from the physical environment. Bringing all this data together, will create something that mimics experience.”

The philosophical question of what humans will do when machines surpass us in thinking capabilities represents Schaffrik’s personal concern regarding potential societal implications of advanced AI, regardless of any AI bubble burst.

“That’s my personal doomsday scenario, I must say. It’s not good for us humans to just idle around. So it’s not so much a technical conversation, but more a political, societal, psychological and philosophical one. So I’m sure we are far away from this, but we are getting there.”

Regarding leadership in this transformation, Schaffrik acknowledges the complexity: “Rulers are supposed to rule. The question is more like, are they intentionally gathering a diverse set of experts who would be able to consult them? Technically, this is possible. Are they willing to? It’s another question.”

His confidence in human adaptability remains intact: “I’m still confident that once we are realizing the true dangers of certain technologies, we will start to rethink. And we have always found a way to move forward, and we will find a way this time as well.”

Our conversation with Bernhard Schaffrik reveals that the AI bubble burst question transcends simple financial considerations. While financial markets may indeed experience corrections, the underlying technology continues its unstoppable advancement.

The key insight is that we’re witnessing a fundamental shift that will persist regardless of market volatility. Schaffrik’s analysis suggests that rather than fearing an AI bubble burst, we should focus on preparing for the transformative changes ahead. The technology won’t disappear, but it will evolve in ways we can barely imagine today. As we stand at this inflection point, the question isn’t whether the AI bubble will burst, but how we’ll navigate the profound societal and technological transformations that lie ahead.

The AI bubble burst debate, ultimately, is just the beginning of a much larger conversation about our future with increasingly capable technology.

The post Is the AI Bubble About to Burst? appeared first on Marketing and Innovation.

The world of artificial intelligence is evolving at breakneck speed, and nowhere is this more conspicuous than in the emergence of AI agents. As organizations grapple with separating genuine innovation from marketing hype, we sat down with Ed Keisling, Chief AI Officer at Progress Software, to cut through the noise and understand what AI agents really mean for businesses today. Ed brings a unique perspective, having taken on his new role in February 2025 at a time when the industry is proclaiming this as “the year of agents.” His insights reveal both the tremendous potential and the current limitations of this transformative technology. As always, time is of the essence.

The creation of Chief AI Officer roles across the technology industry signals more than just a trend—it represents a fundamental shift in how businesses view artificial intelligence. As Ed explains, “AI needs to be a strategic pillar of a business to drive innovation and growth. It really reflects the pace at which technology is evolving, and having somebody that is accountable to follow all these latest updates and really look at it through the lens of new risks and opportunities.”

This observation resonates with the broader digital transformation patterns we’ve witnessed over the past decade. Just as Chief Digital Officers emerged to guide organizations through the digital transformation revolution, Chief AI Officers are now stepping up to navigate the AI transformation. The role isn’t merely about implementing technology—it’s about strategic thinking, risk assessment, and identifying genuine business opportunities in a rapidly changing landscape.

One of the most persistent challenges in the AI space is the confusion surrounding terminology. AI agents, in particular, have become an overloaded term that means different things to different people. Ed provides valuable clarity by positioning agents on a spectrum of AI capabilities.

“When generative AI came out, it was generally reactive,” Ed notes. “We would go to ChatGPT, provide a prompt, and it would generate a response based on its training patterns. Agents are moving along that spectrum in terms of capabilities—they have the ability to perceive their environment, access to audio, video, documents, and crucially, the ability to reason, plan, and learn from their actions.”

Traditional automation relies on strict rule-based systems—the digital equivalent of if-then-else logic. Chatbots, while more sophisticated, remain predominantly reactive. AI agents represent a step toward proactive, reasoning systems that can adapt to changing circumstances.

AI agents represent a step toward proactive, reasoning systems that can adapt to changing circumstances

The evolution doesn’t stop there. Ed introduces the concept of “agentic AI“—a broader paradigm where multiple agents collaborate, passing context between each other to accomplish complex tasks. This represents the holy grail of AI automation: systems that can dynamically adapt to real-time situations without constant human intervention.

Despite the exciting potential, Ed provides a sobering reality check about current capabilities. His observation about the Pareto principle in AI is particularly insightful: “AI is the ultimate manifestation of the 80/20 rule. You can very rapidly get to value with 20% of the work achieving 80% of the results, but actually getting it to work 100% of the time is still very, very difficult.”

This phenomenon explains why AI demonstrations look so compelling while real-world implementations often fall short of expectations. The gap between proof-of-concept and production-ready systems remains significant, requiring careful planning, clean data, and well-defined business processes. As always, I should add, “the more it changes, the more it stays the same,”as the French poet would have it.

While pure AI agents may still be evolving, Progress Software’s acquisition of Nuclia, an agentic RAG (Retrieval Augmented Generation) provider, demonstrates a more immediate and practical application of AI technology. Ed explains the fundamental problem RAG solves: “Large language models have been trained on the entirety of the Internet, giving them broad general knowledge, but they don’t have access to data stored behind firewalls or on personal computers.”

This limitation is critical for businesses. While public AI models are impressive, their real value emerges when they can access and reason about proprietary business data. RAG technology bridges this gap, allowing organizations to leverage AI’s reasoning capabilities while grounding responses in their specific knowledge base.

The practical implications are significant. As Ed points out, “Small to medium-sized businesses have lots of unstructured data—audio, video, log files, recordings, PDFs, charts—that represent proprietary business value, but they have no way of indexing or finding or correlating the data within it.” RAG technology makes this data accessible and actionable.

Ed’s experience at the AI4 conference provides valuable insights into the current state of the AI industry. His observation about AI washing is particularly relevant: “There was a lot of AI washing—companies that weren’t sure they understood the problem to be solved, with very thin wrappers around language models to solve point problems. It felt like a hammer looking for a nail.”

The key differentiator, according to Ed, lies in problem-solving focus rather than technology-first thinking. “AI allows you to solve old problems in a new way and to make seemingly impossible problems possible. You’re thinking about how to drive outcomes—making developers more productive, automating tedious workflows, providing better insights that weren’t possible before.”

This perspective aligns with successful technology adoption patterns throughout history. The most successful implementations focus on specific business outcomes rather than showcasing technological capabilities.

Progress Software’s ShareFile platform provides concrete examples of AI delivering measurable business value. The platform serves client-facing teams in regulated industries—doctor’s offices, law firms, and tax accountants—where document management is critical but time-consuming.

The AI implementations are practical and measurable: “We can create curated lists of documents appropriate based on your situation, and as you upload documents, we can figure out which document relates to which checklist item. We’ve measured this at being three and a half times faster.”

More importantly, the system addresses security concerns that many organizations face: “We have capabilities that scan for social security numbers, personal information, and credit card information that you didn’t want to upload. This single capability flags around 35,000 documents a week.”

These examples demonstrate AI’s sweet spot: automating routine tasks while enhancing security and accuracy. The value isn’t just in speed—it’s in freeing professionals to focus on high-value work instead of admin tasks.

One of the most contentious aspects of AI adoption concerns workforce impact. Ed’s perspective is refreshingly pragmatic: “This is a fundamental reshaping of how business is done—a new skill and opportunity for people to grow, learn, and reinvent themselves. There aren’t experts who have been doing this for five or ten years. If you have the headspace and desire, you can become that expert.”

This view positions AI as an enabler rather than a replacement. The technology’s real power lies in eliminating organizational silos and enabling individuals to accomplish more with the right tools and training. “One person is now going to be capable of doing multiple things with the right prompts, giving them opportunities to do more and drive more value for the organization.”

The message is clear: organisations with infinite backlogs of valuable work don’t need to fear AI displacement. Instead, they should focus on upskilling their workforce to leverage these new capabilities effectively.

Ed’s recommendations for AI adoption focus on practical, incremental approaches rather than transformative leaps. “There’s enormous green space for individuals to become fully enabled with AI. The majority of people using AI today are using it in a Google-like fashion, but they haven’t taken time to understand how to correctly prompt agents or use advanced capabilities.”

The most successful implementations start with individual productivity tools—document summarization, email assistance, and internal search capabilities—before advancing to more complex agentic systems. This approach allows organizations to build AI literacy while demonstrating concrete value.

Our conversation with Ed Keisling reveals that AI agents represent both enormous potential and significant current limitations. While the vision of fully autonomous AI systems remains largely aspirational, practical applications of AI technology are already delivering measurable business value.

The key insight for business leaders is the importance of realistic expectations coupled with strategic preparation. AI agents are not yet ready to replace human workers, but they are already transforming how work gets done. Organisations that focus on practical applications, invest in workforce development, and maintain healthy skepticism about vendor promises will be best positioned to benefit from this technological evolution.

As Ed concludes, “You have to put your personal opinions and biases aside and accept and lean into it. At least you can be part of the process and conversation and understand where the edges are.” This balanced approach—embracing the technology while maintaining critical oversight—represents the most reasonable path forward in the age of AI agents.

The future of AI agents is being written today, not in grand demonstrations of artificial general intelligence, but in the practical applications that solve real business problems, one automated workflow at a time.

Ed Keisling is the newly appointed Chief AI Officer at Progress Software Corporation, bringing over three decades of technology leadership experience. He previously served as Senior Vice President of Engineering for Infrastructure Management at Progress, was an executive team member at Vecna Technologies overseeing Engineering, IT, DevOps, Support, Program Management and Analytics, and spent over 17 years in senior engineering roles at Pegasystems. Specializing in complex system architectures, cloud computing, and large-scale infrastructure management, Keisling also mentors through the UNH Pathways Program and MIT’s Undergraduate Practice Opportunities Program (UPOP). In his new CAIO role, he leads Progress’s AI strategy and product portfolio transformation, reporting directly to CEO Yogesh Gupta.

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In an era of overtourism, where mass travel increasingly strains destinations worldwide, Christopher Hill offers a compelling alternative with his voluntourism/volunteer travel business, Hands-Up Holidays. As a founder and managing director of this company, Hill has built a business model that demonstrates how travel companies can be forces for good rather than exploitation. His approach to volunteer travel challenges the conventional wisdom that luxury and social responsibility cannot coexist.

What makes Hands Up Holidays’ philosophy particularly noteworthy is its commitment to controlled growth, prioritising quality experiences over scale. Rather than pursuing rapid expansion that could compromise his mission, Christopher Hill maintains personal oversight of every client interaction, proving that sustainable business practices can create more meaningful outcomes for travellers and communities alike.

Operating across over 30 countries, Hands Up Holidays represents a fascinating case study in how the apparent contradiction between luxury accommodations and volunteer work can enhance both experiences.

Here is the account of our interview.

We offer a great variety of projects. Our most popular initiatives are building projects, which can range from small-scale but highly tangible endeavours like constructing or installing eco-friendly stoves in village homes to larger undertakings such as helping build houses or renovating school classrooms.

Beyond construction, we focus heavily on wildlife conservation projects, where families might care for elephants or participate in sea turtle protection programs. The third major area involves educational support, particularly serving as reading partners in local schools. Each project is carefully selected to ensure meaningful impact while being suitable for family participation.

It was quite a dramatic shift, and in true dramatic fashion, I experienced my own road to Damascus moment in South Africa. This happened about six years into my career in London’s financial sector. During a trip there, beyond the traditional safari experiences and stays at beautiful lodges throughout Cape Town and the Garden Route, I participated in building a house for a family in one of the townships. This experience was genuinely life-changing in two fundamental ways.

First, it enabled me to interact authentically with local people, gaining real insights into their lives and sharing stories with them – something that had been missing from my previous travels despite being quite fortunate to travel extensively. Second, the satisfaction of helping and making a tangible difference in their lives by providing this family with a proper home was profound. This experience made me realise that I had developed solid business skills but wanted to apply them to something more meaningful and fulfilling. That became the catalyst for establishing what would become Hands Up Holidays three years later.

I should emphasise that there are legitimate reasons for caution. I was fortunate to be in the capable hands of my former London flatmate, who had moved to South Africa and become a professional tour guide, developing his own network of trusted relationships there.

He was the one who took me into the townships, and I certainly wouldn’t recommend just showing up there independently. While chances are you’d be fine, you need to remain cautious. I should also mention that there’s a concerning trend of township tourism that can devolve into mere voyeurism, which we absolutely oppose. However, there are ethical township visits that focus on the positive developments happening in these communities and provide genuine opportunities for meaningful interaction.

Luxury and volunteering don’t immediately seem like natural partners. However, when you examine it more deeply, the luxury component serves as the means to facilitate participation from people who want to make a difference but aren’t willing to sacrifice comfort. This certainly isn’t for everyone, but our model is what I call ‘philanthropy volunteering’.

The primary benefit comes from the funds our clients bring to projects. If providing luxury accommodations and creature comforts enables those funds to be invested in meaningful projects, then we’re the right organisation for them. Conversely, if you don’t mind basic conditions, there are many other fantastic organisations that will help you make a difference in that way.

There are two main approaches we use. First, I personally visit every single project we offer, so I can genuinely attest that they’re beneficial and provide real value to recipients, whether communities or wildlife. Second, this connects to my earlier point about different ways to make a difference. People can contribute through time – spending weeks or months at a project – or through specialised skills, like doctors or physiotherapists applying their expertise. The third way is through funding, which is where we excel. We enable our guests to experience projects and gain that meaningful interaction, but their primary benefit comes from providing the funding to build houses, construct stoves, or create accessible facilities, whatever the specific need may be.

I’d argue it’s primarily the parents’ responsibility rather than mine. However, I think it’s important to understand that no one arrives on our trips surprised to discover they’ll be renovating a school. This volunteer component is our fundamental point of difference – it’s what we specialise in and what sets us apart. People only choose us because this is exactly what they want to do. I hope families have these discussions with their children well in advance of booking.

Absolutely, they’re very different. When I first established Hands Up Holidays, I had people like me in mind – young professionals who were cash-rich but time-poor, wanting good vacations while making a difference. However, from the very beginning, we attracted family bookings, which hadn’t been on my radar at all when I was developing the concept. When I asked these families about their motivations, they’d say things like, ‘Our children come from privileged backgrounds, and we want them to appreciate how fortunate they are,’ or ‘We’re seeking a meaningful family bonding experience.’ Many also express that they want to inspire their children to become the next generation of changemakers. So yes, there’s definitely a strong mission-driven aspect in our family clients’ thinking when they make enquiries.

They’re older than I anticipated. When I wrote the first business plan and brochure, I was targeting young professionals aged roughly 25 to 35. While we do attract some clients in that demographic, I was genuinely surprised by the number of families booking with us. These family clients are typically in their 30s and 40s.

We take a holistic approach to all our trips, with sustainability integrated throughout. While our trips are luxury experiences, we prioritise properties that demonstrate sustainable luxury principles in their design and operations. We recommend restaurants offering organic dishes sourced locally whenever possible, maintain a policy of using only local guides, and choose eco-friendly transport options where available. This approach helps combat overtourism. We also encourage travel to safer but less mainstream destinations – places like Georgia in the Caucasus, Belize, or Roatan in Honduras, which we’re launching in the coming weeks. These destinations aren’t overcrowded with tourists. Additionally, incorporating volunteer components naturally slows down the pace of travel. Instead of rushing from site to site, you’re investing several days in one particular destination and community.

For me, the key is maintaining this as a passion project. I live and breathe this work, and I personally handle all customer enquiries. This isn’t just about passion – I genuinely delight in crafting unique itineraries for our clients – it’s also about quality control. I’m happy that it’s just me managing this aspect, and by virtue of that personal involvement, it naturally limits how much the business can scale. This constraint actually serves our mission perfectly.

The volunteer travel market in which Christopher Hill’s Hands Up Holidays operates represents less than 0.01% of the global tourism industry’s USD 11.7 trillion annual revenue (World Travel & Tourism Council, Future Market Insights). The numbers may look small, but the company is nonetheless showing the way for the reinvention of the travel industry, which sorely needs it.

Walk through any popular tourist destination nowadays, and you’ll see tourists seeking familiar food. This isn’t criticism – it’s human nature. But it highlights how we travel without truly connecting.

Tourism is bringing people to destinations people think were made for them
but would be better without them.

And this is sad. This isn’t what ‘travelling’ is about. It’s about connecting, rubbing shoulders with the locals, understanding or trying to grasp foreign mores, tasting local food, etc. Hill’s approach differs greatly in that.

The voluntourism/volunteer travel model won’t transform the entire industry overnight. But it proves alternatives exist. Travel can serve communities rather than exploit them. And you don’t need to rough it for that matter. Lastly, growing a business doesn’t require sacrificing one’s values.

Hill shows that meaningful travel is possible. Not revolutionary, just different and respectful.

The following facts and figures were gathered with the help of Perplexity and checked against their sources. Errors may have occurred, readers are advised to double check the numbers before quoting. All sources are available at the end of this blog post

Overall, volunteer tourism continues to grow as travellers seek more meaningful, responsible, and impactful travel experiences [3] [6].

[1] Volunteer Tourism Market Size, Share & Growth Report, 2030 https://www.grandviewresearch.com/industry-analysis/volunteer-tourism-market-report

[2] Volunteer Tourism Market Size & Forecast [2033] https://www.globalgrowthinsights.com/market-reports/volunteer-tourism-market-118068

[3] Volunteer Tourism Global Business Report 2025 | Cultural https://www.globenewswire.com/news-release/2025/05/07/3076249/28124/en/Volunteer-Tourism-Global-Business-Report-2025-Cultural-Immersion-Experiences-Drive-Adoption-of-Long-Term-Volunteer-Tourism-Itineraries.html

[4] Volunteer Tourism Global Business Report 2025 https://uk.finance.yahoo.com/news/volunteer-tourism-global-business-report-133500797.html

[5] Volunteer Tourism Market Decade Long Trends, Analysis … https://www.archivemarketresearch.com/reports/volunteer-tourism-market-7751

[6] The European market potential for volunteer and … https://www.cbi.eu/market-information/tourism/volunteer-and-educational-tourism/market-potential

[7] Volunteer Tourism Market Size & Share Forecasts https://www.fundamentalbusinessinsights.com/industry-report/volunteer-tourism-market-13216

[8] Purposeful travel in 2025: The changing face of voluntourism https://fooddrinklife.com/volunteer-tourism/

[9] Volunteer Tourism https://www.marketresearch.com/Global-Industry-Analysts-v1039/Volunteer-Tourism-41409685/

[10] Voluntourism – Tourism and Travel: A Research Guide https://guides.loc.gov/tourism-and-travel/voluntourism

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AI is radically transforming the B2B sales landscape and accelerating the shift towards intelligent sales enablement. At a major B2B event which took place in Paris in July 2025, I met with Stephane Renger, co-founder and managing director of Salesapps. The leading European sales enablement vendor has placed AI at the heart of its innovation strategy. In this interview conducted at the event, Stephane explains how AI agents are revolutionising commercial efficiency whilst maximising security and privacy. A fascinating dive into the future of AI-powered sales enablement that’s redefining commercial performance standards.

Stephane Renger: At Salesapps, we’re working on AI agents with the goal of bringing greater efficiency to sales teams, while maximising data privacy and security.

S.R. We developed three types of intelligent agents for our AI-powered sales enablement solution.

S.R. They are quite blatant, mainly in terms of time savings. Thanks to AI, salespeople quickly access information that used to take hours to research.

For a complete sales team, there is at least 20% time saving, which equates to one working day every week. Marketing teams and content managers can save up to 50% of their time.

S.R. No, it’s not. Salespeople only spend one third of their time actually selling. Two thirds are devoted to paperwork, CRM, preparation. Automation frees up their time from what isn’t their core business.

S.R. It depends mainly on the products being sold. The buyer only spends 5% of their time with the salesperson, 80% of the purchasing process happens on the web. So the remaining 5% must involve a complex buying process to require human intervention. In other cases, self-service is way sufficient, even in B2B.

S. R. For the past two years, we’ve been talking more about ‘AI-powered sales enablement’ than traditional Sales Enablement. This concept is more immediately understandable and evokes innovation in the commercial approach. Worthy of note, for English-speaking markets, we keep that term ‘sales enablement’, but for French-speaking markets, ‘Modern Selling’ is the term of choice. It is more meaningful, especially when discussing AI agent integration, for French-speaking audiences who do not always understand the word ‘enablement’.

S.R. Classic AI models, be it Gemini, ChatGPT, or others are used to research public information. If I want to know who you are or what your company does, there’s nothing better than such a tool that will browse the web, LinkedIn, and other sources to obtain this information. But if I want to process proprietary information, an internal document database, some meeting minutes, an internal conversation, etc., we must ensure data privacy and security at all cost. In this case, we’d rather work with on-premise language models that we can monitor end-to-end. We’ll feed them with this internal information and secure that information.

S.R. It’s very difficult to predict the future even three to six months from now. Our focus revolves around artificial intelligence. Its performance is increasingly impressive and prices more affordable. Right now, I’m particularly focused on sales coaching and training, and commercial skills development. Simulation and capturing a real-life interview in audio is already possible, but video is still relatively expensive, almost €50 per hour. With the lightning-fast progress in synthetic voices and images, I’m convinced that within three to six months, we’ll be able to offer much more advanced technologies at prices that will allow widespread distribution of this type of agent to sales populations and businesses.

These past ten years, Visionary Marketing has written quite a few pieces and white papers on the subject of Sales Enablement. Since then, technological progress and sales team transformation have been lightning fast. This interview with Stephane Renger perfectly illustrates this profound transformation of the sales domain.

AI-powered sales enablement is now fully operational, and this technology is redefining commercial efficiency. The productivity gains quoted by Salesapps — 20% for sales teams, 50% for marketing teams — are a sign that something serious is happening now.

What struck me at this major B2B event in Paris, was the profound transformation of the market offering in this domain. A few years ago, this side of the water, many Sales Enablement vendors were present, either international or local. In just a couple of years, Salesapps* established itself as the undisputed leader in this sales enablement market in French-speaking countries and is now conquering other markets.

The future looks exciting: between conversational AI agents, personalised coaching, and predictive analysis, tomorrow’s salesperson will have tools at her disposal of unmatched power. We haven’t yet seen the end of the evolution of the sales function, and digital technologies are playing a major role in this upheaval.

[*Disclosure: Visionary Marketing worked for Salesapps in 2022]

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Are AI and developers the world’s best friends or is artificial intelligence a threat to the future of programmers? As artificial intelligence models are becoming increasingly sophisticated, many questions are raised about the future of developers across the industry. Will AI replace programmers entirely, as Eric Schmidt and Dario Amodei are predicting? Will junior developers be facing extinction, as Steve Yegge surmised in a now-famous blog post? Or are we witnessing the dawn of a new era in which technology amplifies human creativity rather than replacing it? I interviewed Nathaniel Okenwa, Developer Evangelist at Twilio, to pick his brains about this question, and his conclusion is that, in the future, software development will undoubtedly remain human-driven even though many changes will occur. The video recording of that interview is available at the end of this blog post.

With nearly a decade of hands-on programming experience and a unique perspective on developer community engagement, Nathaniel Okenwa brought both technical depth and strategic insight to this conversation about the evolving landscape of software development.

“My parents celebrated when I got that job title of developer evangelist,” Okenwa said. “I speak and meet with developers, online or in person, and I talk about the tools and the technologies they’re using. A part of this job is being with the community and then spreading the good news of Twilio as well.”

For those unfamiliar with the company, “Twilio is a customer engagement platform and one of the providers helping businesses with their customer support, communication tools, and APIs.”

The elephant in the room for the Tech industry is the fate of junior developers. Steve Yegge’s provocative piece ‘The Death of the Junior Developer’ has sparked intense debate, suggesting that AI won’t make inexperienced developers smarter but will enable experienced programmers to eliminate the need for juniors altogether. A daunting perspective for young programmers.

Nathaniel offers a more nuanced perspective that challenges this binary thinking .

Often, a company doesn’t hire junior developers for their current capabilities. They recruit them because they’re investing in what they will become in the future. Junior developers need to exist if we are going to have mid-level senior developers, developer leaders, and architects at a later time.

Nathaniel is right. Programming isn’t just about syntax and algorithms; it’s about developing problem-solving instincts, understanding business contexts, and learning to translate human needs into technological solutions.

‘If you want to create the next generation of builders, then I don’t think junior developers are going to disappear in the long term. We may forget how important they are for a little bit, but they will definitely make a comeback later on.’

The urgency of these questions intensified when Eric Schmidt, former CEO of Alphabet, made bold predictions about the timeline for developer displacement. His assertion that developers would be reduced to merely correcting AI output within six months and potentially eliminated entirely within a year sent shivers down the spines of the programming community.

Nathaniel acknowledges the partial truth in Schmidt’s predictions while advocating for a more sophisticated understanding of what developers do. “I think there are elements of truth in it, but I think the situation is a bit more nuanced. AI is, in my mind, another Industrial Revolution. In this context, it means we’ll be looking for repeatable tasks that are extremely simple and how to replace them with technology.”

The industrial revolution analogy is particularly apt here. Just as mechanisation didn’t eliminate human work but transformed it, AI appears poised to reshape rather than replace programming roles. “I think AI is going to take some aspects of programming and make them so cheap from an effort perspective that it’s not necessarily going to be the best use of people’s time. However, I think developers aren’t just folks that are repeatedly solving minor syntax sentences. They are creative builders coming up with different ways of taking a real-world problem and abstracting it into technology pieces.”

One of the most compelling aspects of Nathaniel’s perspective is his emphasis on abstraction as the key to understanding how AI will transform development work. Rather than replacing developers, AI represents another rung on the abstraction ladder that programmers have been climbing for decades.

Right now, I can use my programming skills to build a website and serve it to millions of people on the Internet. Thirty or forty years ago, I would have needed a whole set of different skills to make that happen. I would have needed so many more hardware skills and so many more specific high-level networking skills. And all of those things have been abstracted away for me to really focus on making this website really fast and performant.

This historical perspective illuminates a pattern that AI-anxious people are missing. Each generation of developers has built upon increasingly sophisticated foundations, allowing them to tackle more complex problems without getting bogged down in lower-level implementation details.

The printing press analogy further clarifies this progression: “If we think about the printing press, at first you needed to have lots of people who would sit down and handwrite a book in order for you to make 100 copies. The printing press came around, and the amount of effort and skills to achieve that shrunk considerably. But you still needed someone who could run that printing press.”

However, this progression toward higher abstraction levels raises uncomfortable questions about inclusivity and capability. Not every developer possesses the intellectual agility to continuously climb the abstraction ladder, and there’s value in acknowledging this reality.

Nathaniel addresses this concern with characteristic optimism while maintaining realism. “I suppose there will always be people who remain comfortable doing what they are doing in the ways they are doing it. But with technology making so many more different things available, what’s going to happen is users, customers, and the general public are all going to expect more from our technologies and from us.”

The market forces driving this evolution are relentless. As AI enables higher-quality experiences at scale, customer expectations rise accordingly, creating pressure on all technology providers to evolve or risk irrelevance.

“The folks who aren’t meeting these higher standards of experiences will not be able to deliver the value that their customers and employers are expecting from them. If they don’t continue to meet that bar of expectation that is growing higher and higher, especially as AI helps people to develop new ways of doing this, they will be left behind.”

This raises an interesting paradox about digital transformation that I’ve observed throughout my career in technology consulting. Thirty years ago, we predicted that traditional industries like banking would be disrupted by digital-native competitors. Yet established banks have largely survived, adapting gradually while maintaining their market positions.

Nathaniel offers an insightful perspective on this seeming contradiction: “It didn’t kill banks, but I would argue that even if maybe they took a while to get there, the way we interact with our banks is completely different from the way we interacted with banks when we were younger. There are 18-year-olds who have no idea what a chequebook is.”

The transformation happened, but more gradually and less dramatically than predicted. This pattern suggests that AI’s impact on development may follow a similar trajectory—profound but evolutionary rather than revolutionary.

Perhaps the most practical insight from our conversation concerns how AI tools are being adopted by developers. The success of GitHub Copilot and similar tools demonstrates that integration beats replacement as an adoption strategy.

“Sometimes there are engineers who are reluctant to use AI or don’t want to go out of their way to bring AI into their workflows. But when Copilot came out, when other tools that have that technology built into the applications, the interfaces they are already using, the adoption increases significantly.”

This observation reveals a crucial truth about technology adoption: the most successful innovations enhance existing workflows rather than demanding entirely new ones. The future of development tools lies not in replacing programmers but in making them more effective within familiar environments.

One of the most honest moments in our conversation addressed the reality of code reuse – something every developer practises but few discuss openly. The fear that AI will turn programmers into mindless copy-paste operators misses the historical context of how developers have always worked.

“Copying and pasting has been an integral part of the engineer’s journey for decades, and it’s not going anywhere soon. Even if we are copying and pasting from a different place, and even if we are no longer using Ctrl-C and Ctrl-V, we have always been learning from the code that others write.”

The key distinction lies not in the source of solutions but in the developer’s understanding of what they’re implementing. “The real issue is that of developers who go and find a solution online and copy and paste it without understanding what is going on. Now, if people do that with AI, they may create a black box, and that’s going to be great when it works. But when it doesn’t, when you receive a phone call in the middle of the night, and that production’s gone down, that’s a different kettle of fish. Having a black box is not going to be something that a huge company, a huge enterprise, is going to want to rely on.”

Our conversation touched on one of the most persistent challenges in enterprise software: the prevalence of legacy systems built in languages like COBOL that have resisted modernisation for decades. This serves as a fascinating case study in the relationship between technological possibility and human decision-making.

I think we underestimate the power of people. People are sometimes afraid of change. They sometimes want to rely on what has worked for years. And so, even if AI presents a great solution, it’s going to be people who decide whether to take it on board or not.

This observation cuts to the heart of why technological predictions often prove overly optimistic. Technical feasibility doesn’t guarantee adoption, and institutional inertia remains a powerful force in shaping the pace of change.

For students and emerging developers wondering how to navigate this uncertain landscape, Nathaniel offers pragmatic guidance that emphasises exploration.

“The first thing is to explore and experiment. Experiment with the new technologies. Find out what those new job titles are going to be and chart the path to become an expert. Either a specialist in using AI as a tool, or an expert in creating AI, or an expert in maximising its performance, or an expert in building the tools that AI will use.”

Nathaniel’s emphasis on APIs is particularly noteworthy: “APIs are nothing new. The better your API, the better the AI agents that will resort to it.” This suggests that developers who understand how to build AI-friendly interfaces will find themselves increasingly valuable.

Most importantly, he advocates for focusing on uniquely human contributions: “Don’t just focus on the typical tasks a junior developer is asked to do because AI can do them really well. Instead, focus on the ways that you can bring a unique twist to your solution.”

As our conversation with Nathaniel Okenwa demonstrates, the future of developers isn’t about choosing between human programmers and AI systems—it’s about understanding how these technologies can work together to solve increasingly complex problems. The most successful developers will be those who embrace AI as a powerful tool while focusing on the uniquely human aspects of software development: creativity, problem-solving, and the ability to translate human needs into technological solutions.

In a sense Nathaniel’s advice isn’t that different from Steve Yegge’s. You’ll have to become an expert to make good use of AI; it’s not artificial intelligence that will turn you into an expert.

Rather than fearing obsolescence, developers should view this moment as an opportunity to evolve, much as previous generations adapted to new programming languages, frameworks, and paradigms. The future belongs to those who can harness AI’s capabilities while providing the human insight, creativity, and understanding that no algorithm can replicate.

The future of developers isn’t about replacement—it’s about enhancement, evolution, and the continued pursuit of building technology that serves human needs. In that mission, developers remain not just relevant but essential.

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How do luxury brands maximize experiences in sports events? I attended the 2025 Monte-Carlo Masters, which showed a strong presence of elite brands fighting for high-end customer engagement. Brands such as Rolex, Sergio Tacchini, and Replay can be found advertised almost everywhere at the famous tennis tournament. These brands use the values of this tennis tournament’s identity, which are class, prestige, excellence, and exclusivity, to reinforce their brand image. In this article we will be looking into the strategy behind premium companies and their connection to the Monte-Carlo Masters Tennis Tournament. 

Luxury brands have had a history of gravitating towards sports such as tennis, golf and equestrian sports because these sports emphasized precision, elegance, and tradition. Brands saw that it seemed like a good fit for their deluxe identity due to the traditional affluent audiences that these sports offered.

Only the best of the best athletes competing at these events align with the values of the most luxurious brands that they are the best of what they do. These brands are able to prolong their exclusivity while opening up visibility to sports viewership.

As brands become bigger and sports viewership grows, stylish brands are opening up to collaborations with bigger sports that may not have as much class or prestige, such as football and basketball. 

What once was known as the Monte-Carlo Masters is now known as the Rolex Monte-Carlo Masters. Rolex has positioned themselves front and center at a prestige tournament. Not only are they in the title of the tournament, they are on the logo and can be found everywhere at the tournament itself.

Another brand that has strategically positioned itself is Sergio Tacchini. Being at the tournament itself, it is impossible to miss; every ball kid and many employees working for the tournament wear a piece of clothing from Sergio Tacchini. Just being at the tournament, you are constantly being advertised to, whether you realize it or not; everywhere you look, you are reading another brand name.

Other brands, such as Maserati and Emirates, help back the elitist and prestigious image of the tournament.

Monaco, home of the tournament, is known for its wealth as well as its opulent residents, one more reason to advertise an elegant brand, as the target market is mainly wealthy individuals. “According to the World Population Review, Monaco is the richest country in the world in terms of GDP per capita and is regarded as the “billionaires’ playground.”

Celebrities and top-level athletes being at the tournament make being at the event feel like it’s only for those of wealth, class, and elegance. 

Some of the most exclusive experiences at the Monte-Carlo Masters are sponsored by posh brands. VIP lounges and luxury suites are curated for high-end customers and guests.

Additionally, behind-the-scenes access, meet-and-greets with athletes, and fancy gifting moments allow brands to showcase their exclusivity even more to only those that can afford them. Hospitality packages include gifts, discounts, special access, and events made to feel extraordinarily classy. 

The Société des Bains de Mer (SBM), which is responsible for venues at the event, creates gourmet dining opportunities as well as private lounges mimicking luxury brands.

Customers at the Monte-Carlo Masters are rewarded just by being at the event itself. These rewards include access to limited edition merchandise, entries to giveaways or raffles, and the opportunity to use the VR Tennis simulator.

To further show status of class and order, boutiques at the event are limited to a certain amount of people at a time to prevent cluttering.

When a customer buys a ticket for the tennis match, they are not just coming to watch one game, they are spending practically the whole day there. Even in between matches there are activities to be done around the venue.

These activities include, finding something to eat (there are a lot of choices), VR tennis simulator, walking around and exploring the area, shopping in the countless boutiques and tennis stores.

The value of going to a sports event comes from the electric anticipation of not knowing what will happen. Fans come to witness firsthand the action and to purchase limited edition products showing they were there. Rolex and other brands offer merchandise exclusive to the event itself, signifying someone was at the event.

A piece of limited edition merchandise to show you were at the Rolex Monte-Carlo Masters is also an advertisement every time you wear it in public.

Short-term promotions and exclusive collaborations build a sense of urgency used to encourage customers to buy their product. Brands use this to drive up their exclusivity, giving only people that were at the event a chance to purchase something that will remind them of how special that moment was. 

Social media has completely changed this world as we know it, and that is not only limited to the social aspect, but it has changed the business world drastically. Content creation is a great way to gain publicity, and what better place than a sports event to promote your upscale brand? Brands such as Replay and Malongo took advantage of this event sponsorship and made social media promotions showing their collaboration with the renowned event.

Rolex has been partnering with tennis for 46 years, since 1978.

These statistics show that the tournament continues to improve their technology and assets. Furthermore increasing their brand identity relating to top class.

Classy, lavish brands strive to take advantage of events such as the Monte-Carlo Masters tournament to build brand identity and to increase publicity. The Monte-Carlo Masters serves as a sort of “playground” for luxurious brands to strategize, promote, and attach themselves to the values that the event portrays.

Brands that strive to align with excellence, class, prestige, and elitism promote themselves using the Monte-Carlo Masters to represent these traits. These companies take advantage of the tournament knowing that the audience is generally wealthier.

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If you are dying to understand the various GenAI prompting methods, how AI interacts with your prompt, and why this is key to optimising your results, this free prompting guide was made for you. This manual describes GenAI chatbots and the different methods of prompting. It was put forward by Frederic Cavazza, a digital transformation expert, consultant and speaker with over 25 years of experience.

I’ve known Frederic Cavazza for years and I’ve even had the pleasure of working on a few engagements with him. As we were on our way to a GenAI client workshop a few months ago, he showed me this guide and I thought to myself: “This is exactly what I’d like to share with my readers and students”.

Hence this translation and adaption of Frederic’s prompting guide, with his kind permission. I tried his tricks myself and I can guarantee that the mega prompt he describes at the end of the guide is really something you should test, copy, paste, adapt and keep in your own prompt library.

Artificial intelligence is booming, and chatbots like ChatGPT are radically transforming the way we interact with digital tools, changing the way we work. This guide aims to introduce you to the art of ‘prompting’, a key skill for engaging effectively with these artificial intelligence platforms and making the most of their potential.

If you’re researching a topic on the Web and you type in a simple key phrase, the result may not be very compelling. On the other hand, if you structure your search well, you’ll get more relevant answers. And so it goes with artificial intelligence tools like chatbots or digital assistants.

The more structured the prompt, the more relevant the outcome

The way you ask AI chatbots questions or give them instructions is conducive to more or less convincing results. This is what is called ‘prompting’, i.e., the art of formulating clear and precise instructions to guide the work of artificial intelligence models.

In essence, a well-structured prompt is like a well-formulated search query. When done properly, it shall provide relevant results.

There is no one-size-fits-all prompt methodology, as use cases differ from one user to another. However, we recommend you use one of these three methods based on your needs.

These three methods are entitled RTF (Role, task, format), CRAFT (Context, role, action, format, tone of voice) and COAT-SITES (context objective, acumen, task, specimen, impediments, tone of voice, encoding, scrutiny). Each technique works best depending on expected results. RTF was made for quick results, CRAFT, for simple questions with more accurate results and COAT-SITES, for clear cut questions and extensive results.

So, what are these methods about? Here they are in more detail.

With RTF, the prompts specify the role, task and format that AI should adhere to. It consists in a simple, “you are…, you must…, your answer must…” Role indicates who the AI bot should impersonate, providing a contextual framework. Task, gives AI the precise action or problem to be solved, guiding AI towards the expected objective. And Format specifies the type and structure of the outcome.

Should you be looking for more accurate results, it might then be a good idea to expand your prompt to incorporate more context. The CRAFT method is therefore what you would have to resort to. CRAFT implies providing specifics to the AI chatbot in your prompt such as, “I’m in charge of…, you are… you have to…, your answer should include…, choose the following tone of voice.”

With the CRAFT method, Context describes the overall situation or requirement. Role, tells AI the character it should enact. Action, specifies what AI must do, directing the LLM. Format, provides examples or details clarifying final expectations. Tone of voice, defines the expected style or category AI must follow, aligning your response with your objective and audience. Expanding your prompt to formulate a better structured and accurate result.

In the case of COAT-SITES technique, your prompt is not only expanding the context of the situation but giving AI strict guidelines to narrow the margin of misunderstanding. Allowing for your results to be more accurate and extensive. This includes Context, Task, Tone of voice, as in the previous methods but also includes Objective, Acumen, Specimen, Impediments, Encoding and Scrutiny.

Objective and Acumen, give AI the tools to reach your expected result with the correct level of expertise. Specimen and Impediments, provide clear examples and guidelines of what is wanted and what should be avoided. By providing models or illustrations, you clarify the expectations, just like defining what cannot be done narrows down your result. Lastly, COAT-SITES encompasses Encoding and Scrutiny. Encoding defines the output format syncing the results to your objective and scrutiny offers a final sanity check to ensure the results comply with your stated guidelines.

Once you have familiarized yourself with these three prompting methodologies, give them a go. Here are some tips and tricks to keep in mind when navigating GenAI chatbots. Frederic details them all in the guide for you.

After you have signed in to a chatbot, enter a few questions into the prompt window to get a feel for how it replies. After you’ve done that, try testing out the suggested prompting methods, saving a specific topic or tricky task for COAT-SITES.

When interacting with your favourite chatbot, remember to select relevant keywords, stick to one question at a time, test and tweak your prompts and follow up. Don’t settle for half-baked answers and consider asking a different chatbot to critique your results.

Lastly, remember at all times that the better formulated your prompt, the better AI can provide accurate and relevant results. So give these methods a test and see how your interaction with GenAI chatbots evolves.

The post GenAI Prompting Guide for Aspiring Experts appeared first on Marketing and Innovation.

How is AI Search changing the Internet and what role are we playing in this transformation? In this article, I discuss the current state of adoption of AI-powered search engines. By reflecting on the perspectives of Kevin Roose, Matteo Wong and Joanna Stern, this piece explores what we gain—faster, more organized access to information—and what we risk losing—the diversity of sources, depth of content and our curiosity to go beyond a single answer.

Is it time to ditch Google and your other favorite search engines? In the past few years, AI has disrupted numerous industries in the digital sector, but arguably one of the most noteworthy shifts has been in the online search market. Think of the last time you searched the internet for information—did you sift through pages of websites, or did AI place the answer at your feet? 

The decades-long reign of Google might just be challenged by this new age of conversational and contextually aware search power. 

To examine this idea more deeply, this piece will consider three different articles written by Kevin Roose, Matteo Wong and Joanna Stern. As the conversation surrounding AI evolves, their observations offer unique points, from skepticism to adoption, reflecting the considerable development of AI capabilities and increasing adoption by users. 

By breaking down their findings and looking at the numbers of adoption, I will explore the current and future landscape of AI-powered search. So whether you’ve already hitched your ride to the AI bandwagon or are still clinging to your Google tabs, here’s a glance at where our process of search, the internet, and information is headed. 

In February 2024, Kevin Roose wrote an article for The New York Times titled “Can This A.I.-Powered Search Engine Replace Google? It Has for Me.” As you might have guessed from the title, Roose’s experience was a positive one, but not without some hesitation.

To test his theory, Roose gave up Google, instead opting for Perplexity, an AI search engine founded by former OpenAI and Meta researchers. Roose’s several-week adoption of Perplexity left him sufficiently convinced AI search engines were a valid competitor against traditional web browsers, but adjustments were needed if they are going to win the race. 

The information retrieval and contextual understanding offered by AI proved more useful for the majority of his work. However, due to AI’s limitations, Google was not obsolete. Acknowledging the absence of credible sources, real-time updates, and the occasional lack of truth AI provided, Roose found his usage of AI had certainly become more prominent, but most successful when used alongside Google.

The article suggests the adoption of AI will not be a bold movement, but a gradual and natural shift in user behavior. Still indecisive on the effects AI will have on journalists, publishers and others who create the internet landscape, Roose stated, “I’ll have to weigh the convenience of using Perplexity against the worry that, by using it, I’m contributing to my own doom.” 

Roose is far from the only one to have concerns about the growing popularity of AI. The Atlantic’s Matteo Wong placed a heavy critique on AI in his article “The Death of Search.” Wong’s piece focused less on the way in which a person uses AI and more on the way that AI changes our relationship with information.

In his view, the concern is not AI’s credibility or factuality—those issues could be fixed as systems evolve—but the loss of an exploratory model of search. When people stop engaging critically with information, they lose the ability to evaluate and explore their own curiosity. 

He states, “It could completely reorient our relationship to knowledge, prioritizing rapid, detailed, abridged answers over a deep understanding and the consideration of varied sources and viewpoints.” The indication of Wong’s argument is that by extinguishing that exploratory model of search and making information “too” accessible, it dismantles the fundamental idea of the web. 

In opposition to Wong’s take on the adoption of AI-powered search engines is Joanna Stern, who shared her full support in her New York Times article, “I Quit Google for ChatGPT—and I’m Not Going Back.” Stern’s piece rings similarly to Roose’s in that once she made the switch, the probability of going back was unlikely.

Trying a variety of AI platforms, Stern found the ease and refinement of AI a refreshing break from sponsored links and promoted products offered by Google. Only in searching for a known product, website or article did Google still prove useful. 

While she noted AI is consistent with the limitations of poor sourcing and inaccurate information, Stern’s main concern was the loss of visibility and traffic for the information’s original source. 

What’s to keep AI from making these websites and publications obsolete, and who do you credit for the information you got? Stern wraps her argument into a neat bow by saying, “So, yes, I’ll encourage you to try AI for search, as long as you promise to click a link when you can.”

While I have familiarized myself with the platforms ChatGPT and Perplexity, my experience with AI-powered search engines is still limited. However, from what I have seen, I am impressed. The concise, summarized answers leave me satisfied and often without lingering questions. Still, despite the efficiency, I have found I do not stray far from Google. I am not sure if it is a habit or precaution.

So where do you stand? Are you ready to commit to AI, or is apprehension holding you back? As each article shows us, the level of adoption is varying, and chances are you’re somewhere in the middle.

But there’s no doubt the way we access and receive information is changing. The worldwide market size of AI jumped from approximately $50 billion to $184 billion in just one year.

Additionally, a survey by Activate tells us the number of adults in the United States using AI first for their online search was around 13 million just two years ago and is projected to reach 90 million by 2027.

The growth is telling, and it’s not limited to one platform. ChatGPT, Perplexity, Copilot, Claude—even Google is trying to reinvent itself with Gemini.

So whether you’re a curious newcomer, cautious observer or full-on convert, it’s clear we are stepping into a new age of internet.

Moving forward we will have to decide if the gains outweigh the losses and with continual adoption, new questions arise. Will AI search enhance or limit access to diverse perspectives? How much of a role will it play in our daily lives? I guess we will have to wait and see.

The post AI Search : Breaking Up With Your Traditional Search Engine appeared first on Marketing and Innovation.