Times are tough. We are seeing a financial environment where the global markets for initial public offerings (IPOs) seem to be on life support. This is despite some hope of a revival in the U.S. and cautious optimism about the pipeline in the Middle East, and down rounds for privately held companies are becoming more and more commonplace. The recent tech layoffs and liquidity wobbles at several high-profile banks have also fuelled uncertainty about the future across many industries.

While cashing out might not be as simple as it was in the boom years, leaving a company you lead in the best possible shape is still as important as ever. More than anything, your exit can potentially define who you are professionally for years to come.

A rocky exit can potentially tarnish your name with partners, investors, peers, and teams you manage, causing exponential damage for a significant chunk of your career. Your professional reputation is a key determinant of your future employability as a sought-after executive or advisor. It also often defines your ability to fundraise for future ventures.

The stark truth is that you don't have to be Elizabeth Holmes, the former CEO of health-tech company Theranos, a media darling eventually turned media's favourite villain, to have past controversies haunt you. (Luckily, Holmes's case is unusual. Her controversy turned into allegations of conspiracy to commit fraud, aimed at misleading investors and regulators, which then turned into criminal charges). Things, of course, don't have to go so wrong for you to influence your future in a big way still.

To avoid hurting your reputation – your greatest asset – careful stakeholder management is key when an exit is on the horizon. It might be time to consider your exit strategy more closely after an important financial milestone like a successful M&A deal. It's also possible that you are contemplating leaving because greener pastures await. Whatever the trigger, you want to ensure that you're leaving things on a high without burning bridges.

The first thing to do is to define the goals you want to achieve with your carefully laid out exit plan. These might include facilitating a smooth transition, preserving the company's stability and reputation, and setting the stage for its continued success under new leadership.

Being fully transparent — at least internally — about the reasons you are leaving, your plans, your contribution to the company and the transition process is crucial to avoiding rumours or even conspiracy theories in some cases.

Carefully consider your communications with staff, investors, consumers and other stakeholders. The tone of these communications would depend on your specific circumstances, your company's culture and the stakeholder group that is your target audience. Usually, though, a friendly and open approach works better than dry corporate language or — more recently — content drafted by ChatGPT.

Choosing and grooming your successor might be your responsibility, or it might not. It is also possible that you and the incoming CEO won't see eye to eye. Whatever your relationship is behind closed doors, showing a united front publicly will be important for the company and your reputation management.

Ensuring minimal to no damage to the company you lead is a core component of a successful exit. In addition to not showing any indications of strife among top executives, you would also need to undertake some due diligence, document all of the key decision-making processes carefully, and create a framework for your successor to help him or her hit the ground running.

It is also important to always be mindful of how you refer to your former colleagues and the company - both in public and in private conversations. Avoid any criticism, even years after you leave, as the negative narrative could be damaging to your legacy and may even be seen as a sign of weakness.

While this might seem intuitive, not fighting the process is also important and should make things easier for everyone involved. Work with the board, get referrals and listen to recommendations to help keep your legacy and that of the company you helped build or grow fully intact.

Keep in mind that there is always a chance — however small of a badly needed triumphal comeback. For example, if the new CEO does not live up to the expectations of the Board, investors, or consumers, you are the best person to step in to save the day. After all, Disney CEO Bob Iger was reappointed to the top job more than two years after leaving the company, even though he said that he would not return to the role.

Most importantly, however, enjoy that well-deserved gardening leave once the dust settles. It's a much-needed calm before the storm and a time to kick back, reset and enjoy some quiet time once your exit is complete before embarking on the next chapter of your professional career.

The potential of virtual and augmented reality (VR and AR) is certainly not a new topic of conversation, but it might become much more relevant again this year. Despite a prolonged period of VR optimism back in 2016, adoption rates have so far failed to meet expectations, with less than one in five Americans having ever used VR as of 2023.

To be sure, 2016 was a watershed moment for VR, with the long-awaited consumer version of Oculus Rift and Sony's PSVR, prompting tech journalists to declare it "the year when VR goes from virtual to reality." Businesses clamored around the tech's potential, with industry leaders describing different ways VR could revolutionize their marketing.

Google trends show VR and Virtual Reality searches peaked in 2016 — only to start declining from 2017 onwards, marked by the occasional spike prompted by new industry announcements. However, with WWDC around the corner once again, 2023 may be the year VR re-enters mainstream consciousness as Apple brings its pioneering marketing and pricing strategies, acclaimed hardware design and sleek UX (user experience) into the virtual world.

1. The power of pricing

Apple's impact on the smartphone market is universally known. I fondly remember 2007 when the first iPhone was introduced — it cost an eye-watering $499, but this was balanced by network deals that offset the cost, subsidizing payment in exchange for a 2-year contract. Apple made this genius move, creating a de-facto standard of selling expensive consumer devices in exchange for forced loyalty. Some $499 for a phone, even inflation-adjusted to $720 today, no longer seems unreasonable.

In 2023, Apple may try to replicate this model's success by subsidizing the $3k price of a VR device, offering a 12-to-24-month repayment period. However, even with a pricing strategy that historically locks in consumers and iPhone user figures consistently increasing year on year, a $3k consumer expense might not be well timed, given the cost of living crisis in key markets, including the US and UK.

2. Comfort and content

It is very likely, however, that one of the key success factors for Apple's device will be how long people can actually use it without discomfort. For many, this is currently only around 15 minutes, and it seems Apple can't afford to release a device with such high fatigue potential. The weight of the device on the head, how it stays in place when a person moves and how it rubs against the skin and reacts to sweat are all concerns, and that's before we even consider the motion sickness some experience in VR — which is some two-thirds of people, according to a 2021 study of 4,500 German VR users. Given all that, tackling this long-standing industry hurdle of making VR headsets more comfortable to use should be a priority.

Another important factor is that content is still king with a medium like VR. Focusing on hardware alone — however elegant and refined — will fail, so even if the headset is a technical wonder, the question remains: what are we expected to do with it?

VR's cornerstone is games, but not all gaming genres translate well to VR. The fast-paced, frenetic movement will cause nausea pretty quickly, so a different pace and approach is required there. Also, Apple's gaming reputation has faced some criticism as of late, largely due to what the app store offers.

Sure, Apple Arcade — a paid service — has been praised for curating lists of quality mobile gaming experiences, but for the rest of the app store, shallow free-to-play games designed to squeeze money from players, and games with more ads than actual play, are all to be quickly experienced and then forgotten.

While Apple's games strategy seems to require a significant overhaul, streaming and other content could provide a secondary audience. VR in education, manufacturing, shopping and healthcare are useful, but still niche. Currently, it's not clear what use cases Apple will focus on given its core competencies, other than perhaps streaming Apple TV into a VR headset.

3. Leisure time and closet space

Another battle Apple faces globally is the lack of free time that many people experience today. The 4-hour life concept suggests a typical worker has only four free hours a day, and Apple's VR headset will be fighting for a chunk of that time against a heavily saturated entertainment tech market (in addition to other pastimes). User experience will be essential for the device to be used repeatedly, even in short sessions, without it suffering the same fate as a tennis racket, which is often used three times before always resting in the back of the closet.

Speaking of closets, another issue is space. When the VR headset is not in use, it needs to be stored somewhere. While the device's dimensions are unclear, we can use a human head as a reference. So, it's fair to assume that the headset will become yet another item sitting in a living room or, god forbid, a bedroom. As a fan of decluttering, I'm already concerned.

So, as Apple prepares for WWDC in June, throwing its headset into the ring, we'll almost certainly see a spike in VR-focused discussion and debate. However, unless content, comfort, technological advances, cost and strategy are all perfectly aligned, chances are this launch and the mainstream interest in VR may become just another short-lived spike.

Mobile entertainment is now a multi-billion-dollar global industry, evolving at breakneck speed as technological advances unlock new possibilities and shape consumer preferences in new and unexpected ways.

Here is a look at the top five trends changing this industry today:

1. Bite-sized, mobile-first entertainment

Mobile phones and tablets have become ubiquitous, and user expectations are shifting towards mobile-first experiences optimized for smaller screens as a result. At the same time, leisure time is increasingly becoming a luxury as the pace of life for the active part of the population continues to speed up. One consequence is that users are increasingly drawn to content that can be enjoyed quickly and easily on the go. We have witnessed the rise of platforms like TikTok, Instagram Reels, YouTube Shorts and Yepp, serving up user-generated short-form content to a broad range of audiences.

While there is a lot of discussion about the addictive properties of short-form entertainment, screen time regulation and age restrictions for platforms that offer bite-sized mobile fun, one thing is clear — this type of content has true mass appeal and is likely to remain a major fixture in the mobile entertainment space for the foreseeable future.

2. Better connectivity

More reliable connectivity, faster speed and greater proliferation of 5G are also transforming mobile entertainment in their own ways. Better connectivity enables developers to serve up more interactive experiences and data "heavy" formats, such as video streaming and conferencing, audio streaming, podcasting and networked gaming. This democratizes the creation of high-quality live content, which is no longer the exclusive turf of big broadcasting corporations, nor is it reliant upon wifi connectivity and a desktop device.

In addition, the speed and coverage of 5G networks enable more precise location-based services. These enhance mobile entertainment experiences, such as augmented reality games or virtual tours, enabling a more immersive user experience.

With the ability to provide higher-quality and more engaging content, mobile entertainment businesses can unlock new revenue streams, such as subscription-based services or pay-per-view options. By opening the door to more prosperous, more interactive, and more immersive content that can be consumed on the go, improved connectivity directly impacts the possibilities for entertainment on mobile devices and fuelling industry growth.

3. AI and machine learning

Artificial intelligence (AI) has a profound effect on mobile entertainment. Using AI-based tools such as machine learning helps developers improve and optimize backend processes like streamlining repetitive tasks, improving content moderation, and enabling leaner teams to achieve results. It also helps provide the more targeted, personalized entertainment experience that consumers have come to expect – serving up content based on a user's interests and past viewing behavior.

While AI is also making it easier to generate content, including text, images and video, users are increasingly looking for content that feels authentic and relatable – something that is still hard, if not impossible, for AI to produce.

Therefore, when it comes to funny videos, fun memes and similar entertainment, user-generated content is still king for now, while AI works backstage to enhance how it is delivered and consumed.

4. Social media integration

An argument has been made that mobile technologies are making us less sociable as a society, with some even ringing alarm bells that the art of casual in-person communication is in danger of being lost. After all, look around when riding the subway, and you'll see most of your fellow passengers with their heads bent over their mobile devices, completely oblivious to their surroundings and more often than not entirely uninterested in striking up any conversations with their fellow passengers (which is not such a bad thing, to be honest). However, within the confines of the digital world, the opposite trend is underway, and consumers increasingly expect entertaining content that is much more social and interactive.

Users are no longer passive consumers who just want to play a game or watch a video. Increasingly, they prefer to interact with other players, share their memes, comment on the videos they watch and otherwise engage with their digital communities and audiences. This trend is prompting the integration of social media functionality into mobile entertainment apps, providing more opportunities for users to interact with others online and within their digital communities.

5. AR and VR

Advances in augmented reality (AR) and virtual reality (VR) tech have opened new possibilities for mobile entertainment. AR technology allows users to overlay digital content on top of the real world, creating a more engaging and interactive experience for users. Sharing features within social apps enable users to capture and share their AR experiences, such as swapping faces in photos or putting funny filters on images. AR also enables location-based experiences in social apps, which can be used for real-world events or virtual events. Users can interact with digital content tied to their physical location, participate in AR-based scavenger hunts and other location-based games, or engage in pretend play, such as trying on countless pairs of e-sneakers.

As a result of the many AR- and VR-enabled features coming to the market, consumers are starting to expect more immersive, personalized, interactive, real-time, multimodal, and accessible experiences, prompting a higher level of competition among gaming and mobile entertainment companies to meet these expectations.

We are living in turbulent times. Natural disasters are becoming more frequent, a tragic war is again raging in Europe, and a central U.S. bank loved by VCs and startups on both sides of the Atlantic has unexpectedly collapsed in a matter of days. All of this, coupled with technology advancing at breakneck speed, makes it crystal clear that the future is getting increasingly challenging to map out.

Given this toxic mix of volatility and unpredictability with an occasional black swan event mixed in, what can senior executives do to ensure they are leading companies into a sustainable future? I believe that chaos engineering could help.

Chaos engineering to the rescue

Typically, there are three stages to chaos engineering: forming a hypothesis about how a system will behave when something goes wrong, designing the smallest possible experiment to test that hypothesis in the system and then measuring the impact of any failures at each step to gain a deeper understanding of the system's real-world behavior.

In a sense, chaos engineering is not that different from the idea of antifragility and can pave a path toward better risk management, ensuring corporate sustainability in the long run. Even so, I believe it should be applied cautiously because some risks are unacceptable. For example, taking risks that could lead to corporate bankruptcy or death must be identified and avoided at all costs.

To put it another way, having a fire escape plan and conducting regular fire drills is an absolute must (some 20+ years ago, the building I was working in burned down, causing more than 100 deaths and making me take fire safety very seriously). Still, setting fire to a building simply to observe people's behavior to draw up a better safety protocol for future use is not acceptable by any means.

Unacceptable risks

Practicing simulations, scenarios and drills is an essential part of testing risk management processes for any company, provided it does not cause irreparable harm. For example, it is possible to turn a few servers off and see what happens, but kicking a few customers out of a system or from a physical store just to observe the fallout is hardly a practical solution.

To be sure, regarding career longevity, running around creating fires is not an advantageous strategy for a top manager, who should want to build and maintain relationships with their subordinates. After all, the job of a senior executive, first and foremost, is to remove hurdles and enable their team to work and deliver results – not to throw sand in the gears every now and then because a chaos monkey told them to.

So, to make chaos engineering work for the company and your career, it pays for a CEO to work with the Board of Directors to establish a Risk Committee with some senior leaders or one Board member in its ranks to lend it greater authority and credibility.

Many companies, including ours, have a two-step process to identify and manage risk. Low- and medium-impact risks are reviewed and managed by line managers, and high-impact risks are communicated to the Board quarterly. The mitigation of high-impact risks is directly related to the risk appetite of the Board and their potential impact on the company's sustainability.

Getting everyone involved

To carry out this work on a day-to-day basis at FunCorp, we task our department heads with keeping track of risks within their areas of focus and determining things that could go wrong if certain moving parts stop working. This applies to intercompany relationships and to those with suppliers, partners, consultants and other counterparts.

Every company has a history of past incidents. It should therefore make an effort to document and analyze what went wrong and why, including in technology and its other areas of operations. These sorts of 'postmortems' are invaluable resources for any company.

Thus, risk management should be a company-wide effort involving everyone from senior executives and management to line employees. Enabling staff to contribute to risk analysis and mitigation, and thus further highlighting to them their impact on the company's sustainability, goes a long way in building mutual trust and a sense of common ownership. I strongly believe in this approach.

Planning risk response

Why is it important to consider risk and introduce processes to avoid or mitigate their effects?

Poorly planned or poorly executed risk responses, as well as a lack of follow-through in response to organizational learning, can end up causing real damage to a company's reputation and bottom line. This means that risk plans should be regularly reviewed and updated.

It is also important to keep in mind that even companies within the same industry have different risk profiles, so repurposing a competitor's risk playbook would still involve additional work to finetune.

It is never easy to pinpoint all the potential risks and get the complete list right on the first try. So, a process for modifying this list, including expanding or editing it as times change, should be part and parcel of routine corporate risk management. After all, any failure to incorporate new risks or modify the potential impacts of existing ones would put any company in a vulnerable position.

Known unknowns and radical transparency

Risk management is about known unknowns. The business environment is mostly about unknown unknowns, and I can confidently say that being reasonably paranoid (i.e., having an up-to-date risk map) is an excellent way to build a toolbox large enough to apply even to currently unknown problems.

To share one example, in my previous business Aviasales, the third largest global flight metasearch, we had contingency plans for instances when passenger traffic fell 25%, 50% and 70%. What looked like a purely intellectual exercise ended up being incredibly useful when Covid-19 struck, and instead of endlessly searching for a 'new normal,' we were able to implement our risk response plan with minor modifications. The team was confident in a positive outcome because they themselves had participated in developing the plan.

In my current industry, entertainment tech, other risks are commonplace. For example, all companies that deal with User-Generated Content (UGC) face a risk of exposing their audience to the content that is inappropriate in some way. This risk poses an almost existential threat to the business — the content that does not meet company standards affects user retention and makes advertisers concerned. At FunCorp, we identified the need for a proactive solution, aiming to shoot down such content before it appears on our platform for everyone to see.

I am a strong believer in corporate sustainability, and risk management is one of its essential pillars. It requires the proactive identification of risks, timely involvement of the Board and C-suite, and radical transparency between management and employees so that problems are never swept under the rug. Given the chaotic nature of risk itself, chaos engineering can also help along the way.

The average millennial views a staggering 20-30 memes every day, so it's unsurprising that companies quickly got into the mix. As more brands use memes to connect with customers, a new business case is on the rise: memes are a valuable and underutilized way for companies to communicate with their employees.

From weaving them into internal meetings to sprinkling them into company-wide newsletters, memes can do more than make people laugh. When deployed strategically, memes are tools companies can use to build a stronger and healthier culture, democratize feedback channels and even support employees' mental health.

1. Strengthen company culture

The beauty of memes, including those shared on platforms like Yepp, is that they allow people to connect over shared experiences and see the light and humor in difficult situations. These aspects also make them a fantastic tool for community building in this era of remote work, where 8 in 10 US employees work in either a hybrid or fully remote work environment.

With after-work drinks, impromptu water cooler chat and the nuances of non-verbal and in-person communication no longer helping shape colleague interactions and culture, it has become all the more important to find new ways to foster a sense of organizational belonging among employees that don't see each other on the daily.

Some of the world's largest companies, including Google, are already reaping the benefits of integrating memes into their internal communications, having gone so far as to create dedicated internal platforms through which employees can create and share memes. Allowing employees to poke gentle fun at everything, from the quirks of internal processes to a shared complacency over corporate work perks, helps people feel as though they belong to something larger than the hub they work in and share and discover commonalities that aren't restricted by geography. As Gen Z continues to enter the workforce, memes are an increasingly important tool for connecting with younger employees.

Memes humanize colleagues. Of course, professionalism is still important, but there is real cultural value in channels where teams can form connections with each other through humor. When used judiciously, memes help us acknowledge and lighten a load of stressful situations, and when teams better understand each other, it improves both output and morale.

As companies grow and mature, they also develop their own internal language, often cryptic to those unfamiliar with it. We see companies increasingly replacing Three Letter Acronyms (TLAs) with memes, understood by or having a specific context to employees. This makes memes a powerful tool for onboarding, helping to explain how things are done in that particular company.

2. Detect employee burnout

Memes can also be used as a powerful tool for detecting employee burnout. In a corporate environment, where workers often feel uncomfortable speaking up on sensitive issues such as the difficult personal circumstances they are going through or the overwhelming workload they face, memes can serve as a bridge to help employers more easily broach these topics with employees.

At FunCorp, where I serve as Group CEO, we have already integrated memes into our HR processes to help identify the broader team mood, catch any possible internal conflicts, and spot burnout at early stages among employees. Ahead of meetings, our Human Resources department will share a selection of memes portraying different emotional states and ask employees to select the memes they feel best to encapsulate their mood for that week. If they don't think that any apply, they can also choose or make their own meme to submit.

Team members then discuss the reasons for their chosen memes, opening the floor for discussion on how to remedy any underlying issues that could be causing problems. Exercises like these help to not only unite the team but also legitimize employees' feelings and build trust within the workplace – vital ingredients for business success.

3. Provide feedback to top management

Memes have a unique ability to so accurately capture and articulate moods, reactions, or unspoken thoughts that would be uncomfortable addressing in person, making them a valuable tool for delivering feedback to top management.

Google parent company Alphabet's recent callout asking employees to begin sharing desks amid office downsizing, for example, led to swathes of employee activity within their internal meme platform Memegen. Taking aim at the "corpspeak" used by senior executives, one popular meme shared by staff read, "Not every cost cutting measure needs to be good for employees. A simple 'We are cutting office space to reduce costs' would make leadership sound more believable".

The company's rushed release of ChatGPT competitor Bard received an equally frosty reception, with one employee receiving many upvotes for a post that read, "Dear Sundar, the Bard launch and the layoffs were rushed, botched, and myopic. Please return to taking a long-term outlook."

Were it not for memes being so integrated into the company's internal communications, these messages would unlikely reach top management in such a direct fashion. In cases like these, memes help democratize feedback channels and act as a sounding board for management decisions. Initiatives like this can be particularly valuable in cultures where directly questioning management decisions in person may be negatively perceived.

Using tools like memes can help remove the personal conflict and confidence barrier that could otherwise prevent an innovative solution or critical, business-saving feedback from being raised.

4. Bring joy to internal communications

Who likes sitting through 30-page presentations and listening to a speaker drone on in monotone about the company's latest business developments? The answer: no one. Memes can be a fun and effective way to spice up internal company communications by injecting humor, creativity and relatability into otherwise mundane messages.

Beyond simply providing laughs, however, memes can also help with knowledge retention. Research reveals that humor systematically activates the brain's dopamine reward system, with cognitive studies demonstrating that dopamine is important for both goal-oriented motivations and long-term memory. Incorporating memes into company presentations can add laughs while enabling employees to better retain any information shared.

5. Improve employees' mental health

While research on this front remains in the early stages, several studies have already been released in support of the theory that memes can help people to boost their mood and better withstand the daily stressors of everyday life.

One of the largest of these studies, undertaken during the Covid-19 pandemic by researchers at Penn State, revealed that individuals who viewed memes with Covid-19-related captions reported lower levels of COVID-related stress than those who saw a non-COVID caption, proving the value of memes in boosting positive emotions.

Incorporating memes into your corporate processes could help boost employees' mood and overall team morale, or as I like to refer to it – "a meme a day could keep the exit interview away."

For the last few months, the debate about artificial intelligence and artificially-generated content (AGC) in particular — the likes of Lensa's images loosely derived from existing photos and popular aesthetics or texts generated by ChatGPT — has been heating up.

ChatGPT is a free tool from OpenAI that has been trained to chat with people and "answer follow-up questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests." On the other hand, the Lensa AI app by Prisma Labs uses artificial intelligence to generate images based on selfies into fantastical portraits, ranging from the beautiful to the downright bizarre.

Does the emergence of these two tools, alongside many others like them, mean that entire social media profiles could potentially be managed and "staffed" by AI? And if AI is as good as we are at answering questions that once required extensive research or making works of art, what does this mean for employment trends going forward?

Are entire creative industries and tech companies that cater to creatives and thrive on user-generated (UGC) content destined for extinction? The landscape continues to evolve quickly, but despite all the fun that can be had with AGC, when it comes to high value-added and non-repetitive tasks, I don't see AI replacing humans just yet.

1. Reliable research

What research (i.e., too much of my time spent playing with ChatGPT) shows is that when it comes to getting answers to clearly worded, simple questions, the robots are very good at sifting through vast amounts of published content and regurgitating the main ideas quickly. This can certainly save time.

However, the ideas that AI spits out are based on the vast swathes of content available online and are, therefore, not properly fact-checked and sourced. The algorithm cannot distinguish between a well-researched scholarly paper and an article penned by a little-known media organization with questionable editorial standards.

It takes posts by conspiracy theorists and charlatans at face value alongside reputable research to come to conclusions. Therefore, it is hard to know whether the AI's answers are correct. Can they be trusted? Can the sources for the information be made clear to enable the user to decide the veracity of the derived answer for him or herself?

For now, it takes a person with strong research and analytical skills, sound judgment and a good grasp of the media landscape to provide trustworthy research. Will it take more time for a human than an AI to sift through sources to come up with an answer? Yes, but speed is not everything when reliability and trust are at stake.

2. Appropriate content

Being able to distinguish between sources and cite them in research is just one part of the problem. Even though OpenAI has tried to include some safeguards into its tech to make sure ChatGPT would decline inappropriate or offensive requests, like all AI products, it has the potential to learn the biases of those who train it. This means that it can, and already has produced some sexist, racist and otherwise offensive material, as several journalists have noted.

When using AI for copyrighting, be it for marketing collateral, blog posts or website content, it is important to ensure that the text AI produces is appropriate in tone for the corporate or personal brand. Cultural context is key, and this is not something AI can decipher well at this point. So, even when using AI as a shortcut to copyrighting, a human touch will still be needed to check for cultural sensitivity and tone to avoid any potentially disastrous blunders.

3. Quality entertainment

Ensuring something is appropriate is one thing, but what makes truly great content? When it comes to content that is meant to entertain, such as comedy sketches or even memes, I would argue that timing, nuance and creativity are key.

By definition, the comedy that resonates the most with people is relatable — it's authentic, it's storytelling and the laughs erupt from a personal connection alongside an understanding of the audience. Often, that lived experience, whether real or imagined, separates the best comedians or the most popular creators on meme platforms like Yepp from the hacks.

Artificially-generated humour is based on logic, structure and formula rather than opportunistic observations or experience of "in the moment" quips. AI is not good at reading the room, regardless of whether that room is virtual. It can mimic existing jokes, but coming up with new, creative ideas that will spark a connection and produce a genuine laugh is not yet within the robot's arsenal.

4. Thought leadership

Can AI predict future trends? If we're talking about ChatGPT and similar tech, it can only conclude or make future predictions based on the information that has already been published. This means that it is unlikely to come up with anything truly 'new' when predicting future trends in any industry or sector.

Is a well-structured analysis, based on trends and predictions already in the public domain, useful? Sure, it's interesting to see what has already been said. However, this template-based approach that summarises existing information does not lend itself well to communicating new ideas that make for engaging and useful content, which defines thought leadership.

At this point, AI is unlikely to develop creative and disruptive ideas drawn from lived experience, analytical and creative skills. This thought leadership remains reserved for qualified humans – at least for now.

This December, news and social media feeds were littered with AI-generated musings, portraits, and attempted essays. The sudden virality of computer-created content is further evidence that artificial intelligence has staying power on social media, following trends such as face swap filters and those voice modifiers that made everyone’s TikToks sound like they’re narrated by a wacky cartoon character. 

So what’s the end game? Will artificially-generated content (AGC) overtake user-generated content (UGC) on our feeds? 

I think the answer is a definite “no”. 

My reasoning is simple but powerful: as Elon Musk’s cringe-inducing attempts at humorous tweets remind us over and over, it is very, very difficult to fake being funny.

Comedy is based on experience

AI is good at a variety of things. The technology is excellent at accelerating content creation and can amplify the impact of existing content, making posts catchier or more aesthetically pleasing. Instagram influencers use these tools on a daily basis to enhance their social media pages with a view to boosting followers, and now the recent hype surrounding ChatGPT is generating some pushback on the idea that AI can’t be funny – after all, if technology can convincingly converse, why wouldn’t cracking jokes be the logical next step? 

However, comedy is not conversation. By definition, the comedy that resonates the most with people is relatable – it’s authentic, it’s storytelling, and the laughs erupt from a personal connection alongside an understanding of the audience at hand. Even if the audience hasn’t personally experienced the funny story, a good comedian will enable them to imagine themselves there. It is that lived experience, whether real or imagined, that separates the best comedians from the hacks, and will prevent AI from edging out humans when it comes to making people laugh.

Comedy has nuance

The humour in jokes comes from connection, yes, but skilled comedians (and amateur funny people on the internet) understand how to make that connection. A joke’s set-up, nuances, or the way it subverts expectations is what gets the laughs, and sometimes a great joke lands because it breaks the rules. At this stage, AI can’t comfortably play this game. Artificially-generated humour is based on logic, structure, and formula, rather than opportunistic observations or experience of “in the moment” quips. 

Taking this further, the element of surprise is often the “drop” that differentiates a joke from a story – after all, that’s why we call the funny bit the “punch” line. For instance, funny folks rely on using words with double meanings, mockery, blatant lack of logic, or they derive value from emotion or shock, aiming to elicit reactions like, “How could they say that?!” or “Oh, that is so ME.” AI can recognise words with double meanings, but the other techniques rely on a blend of connection and nuance that is out of robots’ reach – for now. 

When AI is funny, it requires a human setup or command to deliver. Jokes sourced from databases tend to be tired – we’ve all experienced the childish excitement of shouting “Hey [insert digital assistant name here], tell me a joke” to our latest home device, only to be disappointed when it returns a flat, lacklustre response (and if you are among the few that haven’t yet, try asking Alexa to tell you a “yo mama” joke for confirmation). In fact, there’s a high chance that the “funny” AI-generated jokes are simply the outdated pieces of humour from past ages.

But what about memes?

Memes, of course, are a staple of online comedy, and they are currently immune to the powers of artificial intelligence. Typically, memes follow two types of humour – observational/satire and absurdist – and appear in the format of an image (or GIF) and caption (although in recent years, they have expanded to also take the form of short videos). While generating captions on demand may sound like a trivial task, it requires human guidance, based on the types of humour supporting the funniest memes. AI may be capable of choosing a suitable image and caption combination, but if robots run the whole process, it will result in banal phrases attempting to entertain, wrapped up as memes, with boring results. 

While it’s certainly intriguing for us to explore and dissect how AI could shape future content writing, it’s perhaps more realistic for now to continue to frame the technology as an assistive tool, as opposed to one that displaces content creators. AI may be able to help get topics started, clear the fog or initial writer’s block, and generate top level subjects capable of informing or inspiring human creation and thought, but, the technology is still regurgitating and repackaging existing content and is trapped within the boundaries of logic and predictability. These constraints mean AI will never replace or realistically, even come close to cracking us up, because comedy is original and AI, by definition, is not.  

• One way of measuring user engagement – assessing the average time a typical user spends on a given platform. (Pretty much everyone now is using iOS time in the app obtained either via data.ai or SensorTower as a suitable proxy metric for the US population; this methodology doesn’t work nearly as well outside the US.) Using time on a platform as a proxy for user engagement is yet another approximation and simplification, since engagement comes in many flavours, including, but not limited to, to the number of content items consumed, created, shared, commented on, etc. A single metric of minutes per day hides all the nuance associated with user behaviour. So, let’s be clear about the core assumption: a higher time on the platform is (generally) better than lower time on the platform as it (again) is considered a positively correlated factor to service monetisation.

• When it comes to DAU (daily average users), this becomes more curious: steady DAUs mean that the audience simply replicates itself (where new users roughly equal the churn), or (!) that a company has decided to fight bots, which exaggerated operational numbers. While it’s painful to admit that some growth comes from bots, but it’s very brave and necessary to trim the fat and restore accuracy in the numbers where applicable. (I don’t specifically mean Twitter, by the way, as it’s a very easy target to pick.) Similarly, unexplained DAU growth with no matching increase in engagement is a sure sign of either a paid marketing campaign with a much larger budget than usual (and - since it’s not fully organic - with lower user retention), or .. you guessed it … a malicious actor who found a way to mass-create accounts and imitate activity.

• Sadly, iOS 14.5 has made it much less profitable to attract iOS users due to severe limits to how they can be targeted and monetised, so if in the past it was possible to quickly discover anomalies in the iOS/Android acquisition/usage split, nowadays this signal is all but meaningless. Further, the proliferation of Android emulators coupled with proxy networks has led platforms to invest time and efforts into determining which users are human versus who are not monetisable.

• TikTok hasn’t eaten up into the Facebook’s/Instagram’s territory just yet – at least, judging by the marginally growing share of time users spent on Meta platform apps compared to 2019 (what I consider the last sane year for platform apps). It’s incredible that TikTok manages to coax an average user into spending 93 minutes per day glued to this app (+5% YoY).

• YouTube continues to hold its ground steadily and is increasing its share in the US TV viewing time. Shorts have at least some impact. 72 mins per day in Q3 2022 is excellent.

• Pinterest is slightly down (12 mins daily usage vs 13 mins pre-Covid). Looking at their revenues, the argument that more time in app leads to proportionally better monetisation is generally upheld.

• Snapchat’s usage has dropped from 27 mins / day (2019) to 20 mins (Q3 2022). In spite of this, Snap’s DAU is up 2% YoY.

• One may wonder if comparing messaging apps, social networks and video-centered apps is really comparing apples to apples or whether this is an attempt to establish some common ground for comparing large private (TikTok) and public (everyone else) companies. But it goes without saying that the ARPDAU (average revenue per daily average user) varies significantly between the platforms of different kinds.

P.S. As you probably know, our company FunCorp has invested in Yepp, a company developing an entertainment app focused on quality memes and funny content. We’re currently inviting entertainment content creators to monetise their content via our app, and have an affiliate program for referrals. You can read more here.

(Yes, I’m still working on the best sales pitch for the product, but it’s the real deal and I’m also contactable on my email or LinkedIn).

7/ Penelope’s Problem (Marriage)

• The secretary problem (Martin Gardner): how many people to interview so that to pick the best one? The answer is: interview the first 37% (taken at random), say no to all of them, but find the most suitable one and use this one as a benchmark for the next 63%. As soon as someone turns up better than this benchmark – hire him or her.

• That was a tame problem; the wild problem of, say, who to marry doesn’t have the best answer.

• The best car, the best tequila, the best flight– that’s not precise, but using some assistance from Google, TripAdvisor or WayAway it’s easy to get as close to the “best” as possible. There are (to my knowledge) no catalogues of would-be wives ranked and rated – at least in the Western countries. And no, Tinder doesn’t count.

• The complexity of a choice is due to the many characteristics of humans: it’s possible to find a better example on any of the characteristics of your current partner. They are very likely to be spread around as many different people as there are traits. Moreover, some characteristics in both partners change over time (looks, weight, education, experience, health, etc.), so the timing of the comparison also matters.

• The best spouse/career/city doesn’t exist. It’s a meaningless concept.

• Satisficing [MK: it’s really funny that I learned about it in the MBA program] (satisfying + sufficing) is about making a decision with limited information – usually by defining the basic thresholds (not something too basic like being literate or knowing how to tie shoes) and committing to the option where all parameters are above these thresholds. It’s not a utility maximisation exercise, it’s more of a hygiene thing.

• In marriage in particular the best is truly an enemy of the good enough. It’s neither practical nor possible to predict how every day of the future life together will look like. And you simply can’t anticipate what you’re going to enjoy.

• Marrying someone who shares the same interests is wonderful. Tradition shouldn’t be thrown out of the window, either: the same social class / background is also a good predictor of long and healthy marriages.

8/ How to Get Over Yourself

• How much time should we spend with the others vs being alone? That’s a wild question.

• The answer requires the part of self-awareness stating that one is not the centre of the universe. Many of our actions are knee-jerk reactions to the triggers and being less serious about ourselves (or about our hurt ego) helps take a pause before responding with negativity.

• We can’t help but think of ourselves more than others, this becoming the main character of the drama called “the story of my life”, broadcast 24/7 for a single viewer. The fact that everyone else is a support character begs the conclusion that we won’t spend much time on describing or understanding them as well as ourselves or the ones very close to us.

• We relate to other people, but not on an equal footing (which explains why one usually can’t have a meaningful conversation with a celebrity).

• This focus on one’s life as a performance of one actor, leading to competitiveness and existential loneliness.

• Instead, focusing on oneself as a member of an ensemble, not a soloist, may work miracles for creating meaning. [MK: think of a Board meeting when every Director wants to hear him-/herself talk at the detriment of the outcome; sometimes not saying anything is better than being able to say something at all times.] Because of the parties’ self-centeredness, dialogs turn into two parallel monologues.

9/ Privilege Your Principles

I found $20 on the street and thought to myself: what would Jesus do? So, I went to a liquor store and turned the money into wine.

Knowing a few rules keeps me from memorising lots of facts.

• Ethical dilemmas challenge our desire for short-term gratification at the expense of the longer-term vision and feelings. Returning a lost wallet to the owner (if no one saw you pick it up) is irrational, but doing good feels good, and it’s more important to many. (Also a utilitarian approach, with money replaced with pleasure.)

• One can ignore the utilitarian aspect altogether and return the wallet no matter what because it’s the right thing to do. The tempting question is: at what price point would money prevail over honesty?

• Economic point of view states that everyone has a price, i.e., a monetary amount sufficient for a person to violate their principles. The direct result of this thinking are KPI-linked pay structures, the monetary and non-monetary rewards and punishments by companies and their managers.

• People are offended by the suggestion that they respond to incentives (a kind way of saying “money can buy their behaviour”). Then they may rationalise these behaviours in different ways (“my child was sick”, “no one would know”, “we only live once”). In the first case there will be conflicting identities of “I’m an honest person” and “I’m a parent who first and foremost must take care of my children”) – this may be painful as it leads to a cognitive dissonance. The realisation that one sold themselves too cheaply hurts for a long time.

• In some decisions the essence of the person is on the line; in such cases self-aware people decide to maintain their sense of selves and refuse the reward. [MK: I also love the saying that “culture is what people do when no one’s looking”. It’s a similar concept.]

• Privilege your principles. Your decisions define who you are. It’s above ethics, it’s about the sense of self. Principles are above costs and benefits.

• Once the principles are established, it’s not mentally hard to follow them instead of making a painful decision every time a choice presents itself. Rules prevent us from fooling ourselves.

• Principles help guide us towards who we want to become. Goodness is an acquired taste.

10/ Be Like Bill

• Most humans are uncomfortable about uncertainty and actively avoid it.

• Optionality is powerful. It’s the freedom to do something but not the obligation. Think of it as an ability to return the goods bought for a refund: this makes shopping so much less stressful! [MK: in other words, it’s an opportunity to either not take action or take a reversible action, like buying a refundable airline ticket.] Optionality costs more but is often worth it. Having more experiences to figure out what sticks is better than having less. Taking chances is more important than carefully planning [MK: at least, most of the time]. We delay decisions because we don’t want to make them, not because we’re looking for more information.

  • MK: at the same time, in value negotiations it’s important to build a list of options before making a decision. Postponing making a decision may be a good thing for better value discovery.

• What works for them may not work for you. Get your own experience with the object of desire, don’t take people’s word for it.

• Sunk costs are sunk. Many life choices don’t turn out the way they were expected to, and this is OK. What’s not OK is trusting someone known to have no integrity.

• Grit and persistence are overrated. It’s OK to change course if the old one isn’t working out. But regret about something that’s been done or not done is regret, nonetheless. The fear of regret feeds procrastination. Creating options may be the right solution to cure regret. Some dreams are unrealistic and not worth wasting time on them.

11/ Live Like an Artist

• Most people don’t have a predefined career (unless they’re born into a family dynastically specialising in a certain trade) when they start. Their life happens to them, and it’s OK.

• It’s wrong to think about things not under our control as being under no control. There’s still structure and reason for the things we don’t control, so the right thing to do is rely (and hope – to a degree) that things will be less hectic than they look.

• Startups, like art, follow the principle “you won’t know what you’re doing unless you start doing it”.

• Many good things in life are random and haven’t been planned when they started. The best conversations are not planned and take the twists and turns of their own; they’re about discovery and not information exchange.

/the end

1/ Wild Problems

• An example of a wild problem is whether to have a child or not: it’s a life-changing decision with all its pros and cons that are unclear at the time of making the decision (and which can’t be rationalised). It’s not possible to know which path to take; only in hindsight it becomes clearer. Talking about children – indeed, they consume time and resources, i.e., come with a sizeable opportunity cost.

• Tame problems are the ones that can be rationally addressed by experience and the body of knowledge, especially scientific. Problems can be clearly defined, and the results can be replicated.

• Wild problems are subjective and resist measurement and replication. Choosing a career or a lifelong partner are two very subjective decisions. (There’s no destiny, so we own our decisions.)

• The traditional approach of navigating uncertainty is measuring what you can and then trying to estimate what you can’t. But it can be a step in the wrong direction, as a choice to focus on one thing is a choice NOT to focus on something else, potentially more suitable or rewarding.

• TL/DR: there’s no right decision and the attempts to arrive at the right decision will always fail.

2/ Darwin’s Dilemma

• A rational approach is listing all pros and cons of the major decision and weighing them against each other, choosing the option with the bigger expected utility. Adding a score against each item helps compare important things with less important but more abundant. It boils down to comparing two numbers in the end, how hard can it be?

• TL/DR: it’s easy, but misleading.

3/ In the Dark

• Many wild problems require making irreversible decisions (burning bridges), result in changes of personality, circumstances, and the value system.

• Looking at others (say, trying to understand how the married life looks like) is not helpful, either, because only a certain part of one’s life is exposed – the positive one.

• Preferring emotions to rationality isn’t always wrong: rationality may end up being its opposite.

4/ This is Serious

• Creating a list of pros and cons may be useful not because of its outcome (a seemingly rational decision), but because of the process involved in making it. The contents and the framing tell a lot about what one really wants.

• An impulse reaction can be helpful in decision making: toss a coin and while it’s in the air think how you want it to land – and go for that option.

• Humans make decisions first and justify them with reasons later.

• There are more than just our future experiences at stake when we face wild problems.

• Utility is a very narrow way of looking at decisions and their outcomes. It ignores the craving for purpose and meaning, the urge to create legacy.

• Humans flourish by taking circumstances and making the most out of them in fulfilling our human potential. It means more than just accumulating pleasures and avoiding pain.

• Thus, wild problems are about the choices that give life meaning, about the chance to flourish. This can’t be quantified or put in a utility calculation equation.

5/ The Pig and the Philosopher

• Purpose, meaning, dignity and sense of self are more important to the overall well-being than simple material pleasures. And these pleasures come and go, but the sense of who we are stays.

• Who you are and how you live is more important than your experience. A fulfilling life is not about having the pleasure tally larger than the pain tally.

• A life of fifty years of pleasure followed by twenty years of regret and shame is not equivalent to a life of twenty years of pain followed by fifty years of pleasure; timing does matter.

• Morality makes some wild problems tame: if there’s something a person won’t do under any circumstances – there’s no need to rationalise it.

• Outcomes of wild problems may not be consistent over time: there still can be good and bad days, and there’s always the “after” time: any outcome is transient and depends on when you assess it.

• Pain, especially when it’s in service of an ideal, can bring meaning. It’s the hard things that we tend to remember when we overcome hardships and challenges. Pain makes experience richer and fuller.

6/ Flourishing Matters

• There are many life decisions that are wild: whether to marry and/or have children, where to live, where to work, who to become friends with, whether or how to vote, whether to divorce, whether to join or leave a religion, crazy acts of kindness, etc.

• Where to live: it’s more about who we are (and/or willing to change accordingly) rather than the experience (which also matters, just not as much). There’s a national / regional / religious identity at play.

• Friendships: while there’s a transactional way of looking into them (you’re only as valuable as what you can do for me; I’m investing into this relationship), there’s an emotional part to it: how does this relationship make you feel regardless of the utility.

• Divorce: a rational way to look at it is when the well-being from being divorced exceeds the well-being from staying married. [MK: as easy as comparing two numbers!] Same as marriage is part of identity, divorce is also part of identity. Some people divorce because they think they will be better off in a new relationship (a utilitarian approach) or because they can’t flourish while being married.

• Religion for many people is about finding the truth, no matter the cost. It’s about belonging to a community. Leaving an organised religion is a painful, but completely rational, experience.

• Whether to prioritise utility or flourishing is a personal decision. [MK: If one chooses priority over flourishing, they get none in the end.]

Part 2.

9/ The Susceptibility of Science

• Scientists seek both truth and recognition (usually – for being the first to make a discovery, the priority rule). Reputations are built with publications, lectures and guest appearances. But not all science is good science, and many “discoveries” can’t be replicated and thus need to be put to rest. Social psychology is my pet peeve, but even serious topics like cancer biology are greatly affected.

• Scientists prefer positive hypotheses (A leads to B) over negative ones (A doesn’t lead to B) due to the fact that there are more negative hypotheses than positive, negative results are not as sexy as positive and may even look like a poorly run experiment.

• p-hacking is when scientists try to find strong correlations and are able to find the matching data sets. However, for the experiment to be honest, the choice of data sets and variables must be made before the data is crunched (a hypothesis instead of hacking the outcome). Otherwise, it’s possible to see statistically significant results without any real patterns.

• The papers that are published are a biased sample of all experiments conducted. Significant results are strongly overrepresented in the literature and non-significant results are underrepresented (kept in the researchers’ drawers). There are always false positives, and scientific papers are not an exception.

• In science between 5% and 15% of published results are negative. Are scientists less likely to publish negative results or are they choosing the experiments that are likely to generate positive results?

• News sources are happy to publish preliminary results and seldom report when the studies covered previously don’t pan out. Clickbait works, but negative results aren’t exciting.

• A single study doesn’t prove anything; it just slightly shifts the consensus towards accepting a hypothesis. Some science writers do the “cafeteria science”, picking a subset of studies that tells a consistent and compelling story.

• The most surprising studies are the ones that make the most exciting articles. Hm…

• When a measure becomes a target, it cases to be a good measure: when the number of publications is a measure, there are always unscrupulous publications that will accept these publications for a fee with minimum level of peer review. Articles like this pollute the internet and mud the waters making telling facts from fiction hard.

• These predatory journals can also be used to spread misinformation and lend credibility to articles as if they were peer-reviewed.

• Any scientific paper can be wrong, regardless of where it was published. Peer review doesn’t guarantee that published papers are correct. They check for the logic, reasoning, and the conclusions. They can’t recreate the lab experiments; they won’t debug the program and even delve deep in the data itself.

• A legitimate paper is written in good faith, carried out using appropriate methodologies and taken seriously by the scientific community. The journal has to be prestigious, and if one sees an extraordinary claim in a low-tier journal, this usually means that the paper is not legitimate. The more important the discovery – the more prestigious the journal will be. The topic must closely match the profile of the journal.

• It’s a good idea to check if the article was retracted. MK: I usually google the name of the article followed by “criticism” or “debunked”. This works surprisingly well for many articles.

10/Spotting Bullshit

• Question the source of information.

• Who is telling me this?

• How do they know it? (Fakes usually have one source and no cross-references.)

• What is this person trying to sell me? (People are selling all kinds of stuff from used cars to ideas and beliefs.)

• Beware of unfair comparisons. (There are more germs on airport security trays than on toilet seats. Yes, if this about respiratory viruses, no, if this about e-coli.) Triggering emotions is a powerful technique. Are we comparing apples and apples?

• If it seems too good or too bad to be true. This one is quite obvious. Dig to the source; a tweet is a starting point, not the final proof. If something is substantially down – what is up then? Context matters. Also, shocking facts on social media exist because they get spread faster and wider.

• Think in orders of magnitude. Lies are designed to lead away from the truth; bullshit is produced with gross indifference to the truth. Thus, lies are usually made plausible, while bullshit can smell bad from the beginning. It’s not hard to use mental math (Fermi estimation) and if the mental result is off the advertised number by a factor of 10 or more – maybe we’re dealing with bullshit.

• Avoid confirmation bias. It’s a tendency to notice, believe and share information that’s consistent with our pre-existing beliefs. Why double check information before sharing in social media if we know it’s true?

• Consider multiple hypotheses. Having an explanation doesn’t mean having the explanation; it’s completely possible to make wrong conclusions from the right data. It’s tempting to look at pieces of data obtained for the same period of time and imply causation, but it’s a shortcut that can turn wrong.

• Triangulate. If an extraordinary claim was made online, find its source and see if other media picked up the story independently. Sources can be fake or non-existent. When possible – get the original report and compare. Use a reverse image search on Google to check image sources. Beware of deepfakes. The age of the media matters: the longer it’s been around – the easier it is to make a judgement about their reputation and reliability. Repetition of a story doesn’t make it true. Read less news. Think more, share less.

11/ Refuting Bullshit

• Calling bullshit should be done responsibly, appropriately, and respectfully. Sometimes we produce bullshit and expect others to point us in the right direction, hence respect. Calling bullshit is a public activity, as the society will be better off without this piece of bullshit. There are cultural norms about it, of course. Many criticisms are better delivered in private and are simply not worth five minutes of fame and a lifetime of regret. After all, malice is more rare than incompetence or an honest mistake.

• It’s really bad to be proven wrong while claiming something to be bullshit. It’s much easier to come up with bullshit than to refute it. So preparation is a must.

• Reductio ad absurdum – a technique of extrapolating the assumptions to ridiculous conclusions. MK: There’s no better list of examples than here. The models chosen can be too simplistic for the task or not take into account physical world limitations.

• Be memorable – debunking bullshit with good humour leaves a lasting impression on everyone.

• Find counterexamples – if A implies B, then find the case where A is true, but B is not. It’s not easy to do on the spot, but the effect is spectacular.

• Provide analogies – that’s quite obvious, but beware of false analogies.

• Redraw figures – (that’s what I’m guilty of in at least one instance) the choice of charts can be misleading: cumulative data (which can’t go down by definition) over several periods of time can disguise the drop in absolute (i.e., non-cumulative) increases. In my case it was the number of flight bookings, but it works equally well with car sales.

• Create a null model (if possible) – if it’s assumed that X is a necessary condition for Y, a null model will demonstrate that Y can occur without X present.

• The issues of identity in people cloud their judgements: take away a wrong belief – and the person’s identity may fall apart. Hence, they will fight tooth and nail to keep their beliefs. Again, saying something many times doesn’t make it true. The hard trick is replacing the missing part of one’s belief system with new information.

• We can’t have ready-made arguments for most of the bullshit we see, so sometimes it’s just better to avoid the confrontation altogether.

• Being smart doesn’t need to be at the expense of others. There’s this “well, actually” type of sentence showing everyone how knowledgeable to person is. Such types of arguments don’t move the conversation forward, they focus on the person speaking rather than the topic being discussed. “Well, actually” people in many ways are no better than bullshitters: they don’t make others smarter.

/ the end

7/ Data Visualisation

• Most people are not used to interpreting charts and graphs, which poses an unsolvable problem as mass media publishes increasingly sophisticated data visualisations.

• Graph designers have a great deal of control over the message that the graphic conveys and the way the reader feels about it. Clever positioning of variables can create an illusion of correlation where there’s none, or gaps between variables can be made disproportionally large. A careless or malicious designer can ruin even the best analysis.

• In architecture the term “duck” is used to describe when form is put ahead of function (inspired by the “Big Duck”). In a similar fashion, data visualisation must be about the data first and foremost, with the decoration being less important. Ducks used to be native to the mass media, but recently have have started creeping into the scientific literature.

• “Ducks” are like clickbait. They’re not harmful, just capture attention for a few seconds. Looking cute becomes more important than the underlying data.

• Content marketers are seemingly obsessed with periodic tables of everything. As opposed to the chemical periodic table by Dmitry Mendeleev, which has had an enormous theoretical basis behind it, the modern “periodic tables of everything” are purely form over function, creating an illusion of classification and structure, but in fact are just loosely assembled pieces of information.

• Subway maps are masterpieces of visualisation of the most important information for the commuter. Graphic designers are obsessed with them, too, creating their own “subway maps of everything”. While the sequential structure of each “line” can make sense to describe progress over time, the choice of physical positioning of the lines is where the concept falls apart.

• Venn diagrams are often abused as an ornamental backdrop for numbers and words, but the actual intersections of the areas don’t mean anything at best and are misleading as usual. Again, it’s form over function.

• Using an object for visualisation where its parts are labelled with some text is questionable, too, as there has to be a clear logical link between the part of an object and the label.

• In charts always look at the axis labels and the scale (especially when they don’t start from 0). [MK: please make sure you read this review of another book.] Bar lengths can be made disproportionally longer to emphasise the gap on an emotional level. Also it’s a huge red flag when axis start with a negative number for values that can only be positive.

• Even if Y axis starts with 0, if the variation of the variable is small relative to the scale, this is deception. (e.g., plotting a change from 57 to 59 over time on an axis with the range of 0-100 – it’s simply not visible). Put kindly, the “graphical display choices are inconsistent with the story”.

• Displaying two variables on two Y axis (left and right) on a graph is likely to tell a misleading story if both axes don’t start with 0. (It’s easy to smoothen the slope.)

• Uneven and varying scales on the X axis are a sign of data massaging.

• “Binning” (averaging the data within a range) can disguise variance in data and make an illusion of data uniformity.

• The principle of proportional ink means that the representation of numbers as physically measured on the surface must be directly proportional to the numbers. E.g., if the Y axis starts at 50 and has two bars on it: one is 100 and another one is 150, this show the second bar to be twice as tall as the first one (instead of 50%). This has far-reaching consequences.

• Bar charts are designed to reflect magnitudes, line graphs tell stories about changes (hence their axes don’t necessarily need to start with 0 if they’re not shaded).

• 3D bar charts are useful for displaying two independent variables, but not one (a 2D chart is better suited for it). No need to unnecessarily impress the viewer. The use of perspective makes it harder to assess the relative sizes of the chart elements.

• “It’s all right to decorate construction, but never to construct decoration” © Robert Venturi.

8/ Calling Bullshit on Big Data

• The challenge in machine learning is generalisation – the ability to identify patterns not seen before. Overfitting is when noise is treated as a signal, making the model unreliable.

• Complicated models fit the training data well, but simpler models often perform better on test data. The trick is finding the simpler model, which will not ignore useful information.

• No algorithm can overcome flawed training data. The process of obtaining and cleaning the training data is long, expensive and is still performed by humans who are biased. Adding more variables isn’t always a good idea because the model need to be retrained with more data, which is expensive to collect and clean; in medical research there may not be enough patients for a certain combination of symptoms or genes. It’s the “curse of dimensionality”.

• And then we have fake news, which can be considered “fake” in 2020, but are perfectly correct in 2022. Training sets need to evolve, too.

• Sometimes algorithms pick on external cues in analysing objects (environment, layout, etc.), which makes them unusable in other conditions. (E.g., classify an animal as a wolf because the background is full of snow due to the fact that the training data contained lots of images of wolves in the snow.)

• The book also touches on the algorithmic accountability (the user of the algorithm is responsible for its outputs) and algorithmic transparency (the person needs to know what factors went into the decision about them). But even then, there’s an issue of interpretability: it’s hard to impossible to understand how exactly the decision is made.

• Data doesn’t have to be directly biased to be picked up by an algorithm: it’s possible to tell one’s race or gender with a high certainty by looking at the university attended, societies the person is a member of or a hobby with skewed demographic representation.

• Big data is not better, it’s just bigger.

Part 5.

Rob Henderson - Raising the Cost of Social Support.

Activists use two tactics to cancel their adversaries and get people fired. Sometimes I’m happy I didn’t pursue my PhD 20 years ago and focused on the commercial sector instead.

Time and money

• Raising the cost of social support. Making life hell for the people around the target – calling, attacking, intimidating them purely because of their association with the person.

• Most colleagues (usually they’re colleagues but can be friends or club members) are afraid to stand up against this abuse because they can become the next target. There are tangible reputational consequences of doing so, which is an unbearable loss for the people.

• Fighting activists is time-sucking; defending the target is hard because of it. Time is money, and the longer it takes to come up with a line of defence – the more expensive it is to the defenders. Raising the opportunity cost of defending the target, unfortunately, is a strategy that works well – so far.

Problematic opinions

• People are labelled in the “Adjective – Profession – Name” format. “A controversial professor John Smith” is a good example. Read the paragraph about headlines in “Calling Bullshit, Pt. 1” and you’ll get a good idea how powerful and damaging false summaries can be.

• Once a person is labelled “provocative” / “problematic” (or a synonym of these words), their activities and outputs (articles, publications, opinions) are marked similarly with obvious negative social consequences.

• It immediately becomes risky to get associated with such person by supporting / agreeing with them or expressing similar views.

Richard Waters - Venture capital’s silent crash: when the tech boom met reality.

• So far investors and founders are resisting the full implications of a market downturn unless the runway of their business is unacceptably short. In such cases companies have to raise at any valuation (read – down rounds) to survive.

• FOMO in VCs caused investing at almost any price as well as performing inadequate due diligence. [MK: when my old company raised Round B in July 2022, the due diligence process took several months, but it was a non-standard case.]

• The investors’ assumption was that VC as a class outperformed public stock investment in the long run, hence the right strategy was always to hold and double down. Each new private round allowed VCs to mark up their holding in their companies demonstrating excellent paper returns to their LPs. [MK: It wasn’t uncommon for new rounds to happen solely to mark up the price.]

• This created a perverse incentive to keep companies private for as long as possible while not asking for profitability.

• The lack of suitable investment options caused capital to flow into initiatives with high uncertainty and very long payback periods (potentially longer than the 7-8 years’ VC fund life). Examples are quantum computing, driverless cars, commercial space and space exploration. The risks are obvious: once LPs become less risk-seeking and stop writing checks, these “moon shots” won’t be financially sustainable.

• The biggest disappointments (at least in the US) are ultrafast delivery, fintechs, blockchain ventures. But the clock is ticking for most unprofitable companies, and since even profitable businesses have seen their valuations severely slashed, no one will be unaffected.

• Timing for VC is everything, and newly raised funds are likely to show unprecedented losses instead of the super profits they had anticipated.

• Profitable companies and those with enough cash on their balance sheet obviously have better prospects of weathering this turmoil. However, expensive and clearly unprofitable initiatives for customer acquisition and market share grabbing will have to be put on hold until VCs develop a new FOMO. Thus, no 3x YoY growth for SaaS startups any time soon.

Evan Armstrong - Snapchat is Possibly Screwed

• Snapchat claims to be a "camera" company, but they rely on phone cameras, and their own hardware experiments have all failed (Spectacle, drone). Poor execution and a deliberate distraction for retail investors to obtain the innovation premium.

• A "Decade of Innovation" haven't led to meaningful differentiated user growth. Their growth is mostly driven by the release of their Android app in 2018 and the expansion in the "Rest of the World" meaning mostly India again thanks to their Android app. User growth lags TikTok and even Meta.

• Their user monetisation substantially lags Meta and Twitter with no clear path to at least match them. Here's why they are unlikely to improving their ad revenue in a meaningful way:

  • Ad performance (how well the average ad does on the platform and how measurable are those results). It's determined by targeting and attribution. After ATT (Apple's App Transparency Tracking) Snapchat's targeting has deteriorated. It was mediocre to start with (13-34 year-olds make for a not so profitable user bunch), and also the timing of ad serving (between clips) isn't good for e-commerce and transactional advertising. Claiming in Oct 2021 that Apple's changes haven't affected the business, while going 180 degrees 6 months later is an indication that Snapchat doesn't understand the business they're in. Note: bringing attribution in-house may ease the pain.

  • Ad inventory (what ad placements are available and what the volumes are). Snapchat offers lots of video formats (including AR), but most of them are not scalable: ad copy production should be easy to create. Video is harder to produce than photos and images. Also, video ads also require iterations upon iterations to work; it's simply way more expensive to do it with video. They're pushing only AR ads, which limits their customer base to large companies. SMBs won't bother. But as Snapchat's audience is smaller than Meta's, large advertisers will run out of addressable audience much more quickly. Snap's traffic can be cheaper than Meta's, but the absolute volumes are way lower.

  • Ad price. As with most real-time auctions, the more bidders there are - the higher the ad prices. Snap doesn't use auction for its AR ads thus leaving money on the table. Brand (non-transactional) ads are broken by the macro conditions.

5/ Numbers and Nonsense

• (let’s get the obvious out of the way) Not everything can be reliably quantified (think about the number of stars in our galaxy). Sampling helps to get an idea of the number / range, provided the sample was representative: don’t estimate the average height of the population by only taking measures on a basketball court. Data never lies but often misleads.

• The choice how to represent a numerical value (“framing”) sets a context for that value. MK: framing is one of the many things are skilled Board Director should look for when reading management presentations.

• An example is cocoa being advertised as 99.9% caffeine-free. Well, the strongest coffee (surprise!) is also 99.9% caffeine-free, as caffeine is that potent that one needs just a tiny dose of it.

• MK: Most numbers in advertising and marketing presentations can be thrown away as they mislead (or set the wrong context) more than guide.

• Percentages (absolute and relative) are the #1 tool to mislead. 1% and 2% are absolute 1% apart, but 1% is 50% (relative) of 2%. Using percentages and percent points carelessly opens room for lots of manipulations.

• It’s helpful to keep Goodhart’s law in mind: when a measure becomes a target, it ceases to be a good measure. [MK: this partially explains why most KPIs don’t work.] Measuring things alter people’s behaviours, and not always in the intended ways.

• Mathiness – the introduction of a smart-looking math formula with variables that can’t be quantified (despite the claims to the contrary) and even the relationships between which is hard to establish. Taste = (Satisfaction from oranges + Colour of a lemon) / dollars spent on fruits last year. Another issue with these things that are presumably quantifiable is the dimension: in what units do we measure taste, satisfaction, colours, etc.?

• One can argue that mathiness is used to demonstrate a metaphor, but math is not and should not be used for something that’s not precise.

• Zombie statistics are numbers that are cited badly out of context, severely outdated or simply made up – but they’re still being quoted now. Think of the “10 000 hours to become a pro” or “walking 10 000 steps a day” or “drinking 2 litres of water a day”, etc. Simple figures and statistics are particularly prone to be spread and never die.

6/ Selection Bias

• What you see depends on where you look. Samples must be random in respect to the feature we’re interested about. Results may also be different because of the sampling (actual behaviour and beliefs vs stated behaviour and beliefs).

• The sentiment on Facebook is not neutral: people self-sensor and beautify their posts to look happy and successful – one needs to take this into account. Also, “100% of internet users said they used internet”.

• A majority of studies in social psychology is conducted on WEIRD (Western, Educated, Industrialised, Rich, Democratic) populations, and the cheapest tests are run on students. This must not be discounted.

• Different populations vary widely in their perception of the Müller-Lyer illusion, with American undergraduates being disproportionally affected by it.

• Car insurance companies run ads claiming that an average person switching to their policies will save $500 p/a. Here the trick is that they count those who have actually switched, and most of them had been with an insurer who charged more. However, if we assume that everyone shops around and buys the cheapest suitable insurance, they simply won’t switch and won’t dilute the statistical savings. In other words, those who switch are NOT a random sample of drivers. Most people don’t shop for insurance every year.

• Universities are scored higher if their average class size is 20 or under. It’s not uncommon to see classes of 140 students, though. This stats can be tricked by unis offering lots of small (boring and useless) classes, which again would dilute the average class size. Gaming the system is not that hard!

• There’s a mathematical paradox that most likely, my friends have more friends that I do. (No, I’m not a bad person.) This is thanks to the fact that some people have disproportionally more friends / followers than me, and I know a lot of them. Large numbers ruin averages.

• Observation selection effects occur when the observer is present to report on a variable. Statistically there are more drivers in the slow lane than in the fast lane, because the distance between cars in the fast lane is bigger. Buses stuck in traffic statistically make the average wait time longer, even if they depart in even intervals.

• Why are hot guys such jerks? Not necessarily because of their high status because of the looks and as such – lack of negative consequences. In fact, Berkson’s Paradox tells that optimising for two traits (attractiveness and niceness) creates a negative correlation between them; if both people optimize for these traits, they will end up in a narrow band of possible combinations of attractiveness and niceness when these traits are the opposites of each other. The key here is optimizing for both traits. Same works with talented developers being unbearable.

• Data censoring is a phenomenon closely related to selection bias. Even if the original sample is selected at random, part of it doesn’t make it to the final analysis (e.g., clinical trial patients with side effects drop off). Is it true that rock and rap musicians die at the age half of the blues, jazz and country musicians (30-35 vs 60-70 y/o)? While the data suggests so, it’s important to understand that the data has ONLY the info about dead people; most rock and rap musicians are still alive and well and haven’t made it into the statistics. Yet. These genres are relatively young (30-50 years), so the death statistics mostly contain the indulgent and unfortunate musicians.

• Wellness programs in corporations are aimed at preventing diseases and / or health conditions and complications. Corporations try to encourage people to do more of a good thing (moving, playing sports, eating healthy, sleeping well, etc.) and less of a bad thing (smoking, drinking, drugs, etc.). The pretence is, of course, employee well-being, while it’s, of course, insurance premiums reduction (a valid business case with a thick layer of S/ESG bullshit on top).

• Do these programs work? The selection bias here is obvious: since participation in all these programs is voluntary, statistically the healthier people choose to opt in. Does it constitute a selection bias? Sure! A proper analysis with control groups showed no meaningful impact on health, absenteeism, retention or health care costs.

• But why such a difference between the expected and observed outcomes? The people who participated were already healthier and were less likely to leave the firm. [MK: I’ll make an important distinction: the program was introduced for existing employees; wellbeing programs are being used as part of the company benefits offered to would-be employees, even if they won’t use these benefits.]

Part 4.

3/ The Nature of Bullshit

• Bullshit is not outright or white lies. It’s what people create when they try to impress or persuade you, without any concern about the truthfulness or correctness of what they say. In science it can mean the text that’s so obscure that it’s impossible to get to the main point without permanent brain damage.

• Test: if you can negate a sentence and its meaning doesn’t change, it’s definitely obscure and is bullshit.

• Persuasive bullshit – conveys an exaggerated sense of competence or authority.

• Evasive bullshit – avoids directly answering a question due to the unpleasant truth.

• Bullshit disregards the truth by overwhelming, distracting or intimidating the listener. Adding unnecessary references to an article (if you question me – you also question other authoritative figures, who do you think you are?), using jargon that doesn’t fit a reader’s vocabulary – this is all bullshit.

• Some things are easier to challenge than others. There are basic questions any curious person should be able to ask: explain the choice of the sample group, what specific method to arrive at the conclusion is being used, etc. Some things are very hard to verify independently (DNA sequencing, COVID treatments or climate change claims), hence the reader has to eat off the author’s hand, as disbelief will be treated as a challenge of the scientific establishment. Claims like this rely on an almost unverifiable black box.

• Data is being fed into the black box, which does its unverifiable magic, leading to an output that later gets interpreted by a researcher. Most problems with black boxes are related to either the data (garbage in – garbage out) or the model / black box. Sometimes the output is completely off the logical range of values – even then sometimes this is clouded by the sophistication of the black box.

• Using the right data as an input is critical (to state the obvious), but time and time again models are being trained on poorly collected and/or cleaned data leading to all sorts of self-delusion (and billions of VC dollars being wasted). If a team of PhDs builds a perfect model, but the data is being collected by $1.5/hr contractors, it’s a dictionary definition of stupidity and carelessness.

• The book tells a story about a system to tell a criminal from a good citizen by their photo; the criminals’ photos were mugshots provided by the police, and the good citizens provided their professional photos showing them in the best light. What could go wrong??? The system ended up being an expensive smile detector.

• Let’s repeat the ultimate truth: extraordinary claims require extraordinary evidence.

3/ The Nature of Bullshit

·      Bullshit is not outright or white lies. It’s what people create when they try to impress or persuade you, without any concern about the truthfulness or correctness of what they say. In science it can mean the text that’s so obscure that it’s impossible to get to the main point without permanent brain damage.

·      Test: if you can negate a sentence and its meaning doesn’t change, it’s definitely obscure and is bullshit.

·      Persuasive bullshit – conveys an exaggerated sense of competence or authority.

·      Evasive bullshit – avoids directly answering a question due to the unpleasant truth.

·      Bullshit disregards the truth by overwhelming, distracting or intimidating the listener. Adding unnecessary references to an article (if you question me – you also question other authoritative figures, who do you think you are?), using jargon that doesn’t fit a reader’s vocabulary – this is all bullshit.

·      Some things are easier to challenge than others. There are basic questions any curious person should be able to ask: explain the choice of the sample group, what specific method to arrive at the conclusion is being used, etc. Some things are very hard to verify independently (DNA sequencing, COVID treatments or climate change claims), hence the reader has to eat off the author’s hand, as disbelief will be treated as a challenge of the scientific establishment. Claims like this rely on an almost unverifiable black box.

·      Data is being fed into the black box, which does its unverifiable magic, leading to an output that later gets interpreted by a researcher. Most problems with black boxes are related to either the data (garbage in – garbage out) or the model / black box. Sometimes the output is completely off the logical range of values – even then sometimes this is clouded by the sophistication of the black box.

·      Using the right data as an input is critical (to state the obvious), but time and time again models are being trained on poorly collected and/or cleaned data leading to all sorts of self-delusion (and billions of VC dollars being wasted). If a team of PhDs builds a perfect model, but the data is being collected by $1.5/hr contractors, it’s a dictionary definition of stupidity and carelessness.

·      The book tells a story about a system to tell a criminal from a good citizen by their photo; the criminals’ photos were mugshots provided by the police, and the good citizens provided their professional photos showing them in the best light. What could go wrong??? The system ended up being an expensive smile detector.

·      Let’s repeat the ultimate truth: extraordinary claims require extraordinary evidence.

4/ Causality

• Faking social confidence is claimed to be a helpful skill, especially for teenagers. It’s not considered bullshit per se. There is association between self-confidence and the age of a first love kiss, but it’s irresponsible to think that one leads to another (both ways); these things can have a common root. Our inbuilt desire for making sense of the world via pattern recognition can backfire.

• We’re interested in causation in a practical sense: what do we need to do to cause something to happen, change or not happen?

• People’s brains are wired to make sense of the world, trying to fit all the available information into a believable story containing cause and effect. Bullshit happens when this cause and effect is based only on correlation. Media may not care what readers believe after reading bullshit articles, they need sales and/or clicks. Even medical journal articles (which are supposedly the gold standard of published content) suggest causation in the absence of adequate evidence.

• Prescriptive or causal claims are the worst: if people A do X, it doesn’t mean that doing X turn people B into people A. [MK: just look at all the “rules of successful people”.] Is there believable causal evidence to back the prescriptive claim?

• There are examples that are of interest to most of us: there is in fact an association between poor sleep and Alzheimer’s disease. What’s not proven is the correlation. Another one is “red wine prevents heart disease” – hits very close to home.

• It’s important not to confuse trends (where minimum wage is higher, poverty is lower) with prescriptions (let’s increase minimum wage to reduce poverty).

• The (in)famous marshmallow test (a child can have a marshmallow now, but if s/he waits some time, s/he can have two; the kids choosing the latter option were more likely to be successful in life) simply demonstrates a correlation but doesn’t suggest causation. [MK: I’ve seen numerous instances when this causation was firmly assumed.] Delaying gratification (and alienating children in the process) is not the right course of action regardless of what magazines suggest. This particular test had a huge input data problem: it didn’t account for the socioeconomic status of the children’s families. The value of a single marshmallow and the certainty of obtaining one is vastly different for children who can have sweets any time they want and those for whom candy is rare and precious. [MK: I’ve just run this experiment with my 9-year-old daughter and she chose to wait. Maybe because she’d had a couple of pieces of chocolate an hour earlier.]

• Correlation doesn’t need to be +1 or -1 all the time. There are probabilistic causes (A increases the chances of B in a causal manner), sufficient causes (if A happens, B always happens – that’s what I meant by correlation being -1 or +1), and necessary causes (B won’t occur without A present). Bullshit happens when sufficient and probabilistic causes are confused for one other.

• MK: the book goes on to explain the selection bias and random sampling. Sometimes random sampling (say, for life-threatening diseases) is unethical and can’t be used, but it’s true that not taking medication to block fever (Nurofen, Panadol, etc.) decreases the time to recovery. I’d add that this conclusion ignores the aspect of the patient’s comfort, which may be more important than the time one spends in bed sick.

Part 3.

1/ Bullshit Everywhere

• A theory of mind is needed for bullshitting – being able to put oneself in the place of their mark. What sort of impression they’ll make, and thus adjust the message or behaviour accordingly. This applies to both humans and other living creatures.

• “Full-on bullshit is intended to distract, confuse, or mislead—which means that the bullshitter needs to have a mental model of the effect that his actions have on an observer’s mind.” (a quote)

• Bullshit exists because people want something from others, see bullshit as the means to obtain this something and use the available language tools to carry out the plan.

• Being caught lying results in social sanctions, including the loss of relationship and trust, and even physical assault. Paltering, however, is using technically correct statements to lead to the wrong conclusion or decision. The social consequences of paltering are less severe. There seems to be an implied benefit of a doubt, as what people actually say often is not what they intend to say.

• Implied meaning (implicature) allows to say things without saying them. E.g., “this movie wasn’t terrible” to mean “it was so-so”. There’s huge wiggle room to say something and claim that the conclusion was not implied (paltering). The choice of words is the message in itself, and it matters.

• Weasel wording is using the gap between literal meaning and implicature to avoid taking responsibility for things. It helps save face, avoid negative consequences or having to deliver on the promise. E.g., “… up to 50% in savings” when the median savings are 5%. Or when companies are trying to pretend they’re not using child labour.

• Bullshit can be delivered via self-regarding signals (“I’m X”) or other-regarding signals (“the world is Y”), which, with a very few exceptions, is common to humans only. Describing the world (or what you do) send signals not just about the object you’re describing, but about you as well – the character, social status, sense of humour, etc.

• Sometimes bullshit exists without regard for truth, but with a good story as an objective. To most consumers it doesn’t matter; what matters is the attention and the laughs.

• Brandolini’s principle: “The amount of energy needed to refute bullshit is an order of magnitude bigger than [that needed] to produce it.” (quote). One of the most notable examples is that “vaccines cause autism”. This claim has been refuted many times over (at a huge monetary cost to society), but still there are people believing it wholeheartedly.

• It also takes less intelligence to create bullshit than clean it up. Sadly, it also spreads faster than the attempts to curb or refute it. Many members of the attention economy – either due to low intelligence or lacking moral qualities - will stop at nothing to get their share of clicks, page views and likes. Add to it an unfortunate habit of mass media to publish bullshit stories on their front pages and later (when enough damage has already been done) to publish corrections, retractions and apologies in the much-harder to spot sections.

2/ Medium, Message and Misinformation

• Technology has amplified the bullshit problem, with social media platforms offering virtually unlimited ways to spread out all kinds of info. Everyone has their shoebox, and the amount of information produced far exceeds our capacity to consume and process it.

• The lower the cost of accessing information – the wider the range of topics available and the lower the objective quality of such information. This leads to distraction, and distraction is a form of misinformation.

• The old media was monetised via subscriptions. The new media is mostly clicks-driven. Optimising content for clicks (clickbait, “empty calories”) is completely different from optimising for a long-term financial and emotional relationship with a newspaper and its journalists.

• Even major newspapers are prone to the manipulation of headlines, often even contradicting the contents of the main article. An arms race of headlines (in search for the new bottom) has another unpleasant effect: many people only read the headlines and not articles themselves, hence they get a feeling of being in control of the content they consume, but the opposite is true. To publishers it doesn’t make a lot of sense to put the main message into the headline, as this reduces the chance this article will be opened (the fear of telling too much too soon).

• Successful headlines don’t convey facts, they promise an emotional experience. “… it will make you fall in love”, “… you will gasp”, “… this will melt your heart” and so on.

• The victim is the professional reporting: simply stating the truth is no longer enough.

• Balanced reporting is no longer possible because people prefer echo chambers and reinforcing their own views. Hence the obvious partisanship in reporting in most mass media. Social media amplifies partisan and hyperpartisan content, as it attracts the highest engagement and shares.

• The content people read and share first of all sends a signal about these people – their value system, beliefs and affiliations. It’s much less about the content rather than sending the right signal. The medium IS the message.

• Communication becomes affirming the commitment to our group and echo chamber. As such, who is talking becomes more important than what’s being said.

• Content personalisation, a crown jewel of all social media platforms, is about increasing content engagement metrics while optimising for the particular user’s time on the platform (reducing churn) and (recently) navigating the minefield of public scrutiny and compliance, trying not to get caught amplifying the wrong message or indoctrinating teens into violence or suicide.

• Recommendation algorithms tend to be biased towards amplifying increasingly extreme and radical content. Sadly, it’s a one-way road.

• Misinformation is spreading false claims that are not deliberately designed to deceive. Disinformation includes the intent to deceive. In the 24-hour a day cycle whoever publishes the breaking news first gets the most clicks, so fact checking can wait. It’s a clear example of perverse incentives.

• Social media is very good for spreading propaganda, too, as the person sharing the message, is trusted higher than a random stranger. But the goal of propaganda is no longer to convince of specific untruths, it’s about using the “firehose strategy” – sending lots and lots of messages to the point of fatigue, when readers are no longer able or willing to separate fact from fiction. This leads to the erosion of public trust towards institutions as well as lower participation in the democratic processes.

• Fake news is much less about propaganda than about generating advertising revenue. There’s no limit to how low people can go, and the money doesn’t stink.

• Bots and fake accounts (with realistic photos generated by AI) can spread the right messages to their unsuspecting subscribers or just can send the right spam (vote in a certain way, hack search engines’ suggestions, etc.). Let’s not forget about deepfakes.

• Tech companies try to fight misinformation and disinformation, while in the process becoming arbiters and judges, which is a dangerous road to walk. Governments try to stop some messages and topics from spreading, which may be considered hampering free speech.

Part 2.

Part 2.

5/ Roles in the Psychopath’s Drama

• Starting a new job usually involves lots of uncertainty, but also excitement and happiness for new employees; it’s also the time when psychopaths can strive.

• Existing employees logically believe that the new staff has been screened not only for the skills, but also for the values, attitude, and honesty – something that psychopaths are very well mimicking. The same is true for the ability to get along with colleagues for the sake of everyone rowing the boat in one direction.

• There’s also a well-founded assumption that people go to work for an exchange of their goal-oriented efforts for fair pay (the amount of both varies, but that’s the logic nonetheless); psychopaths are focused on getting the highest reward for the minimum amount of work or poor performance, as well as on maintaining a favourable appearance.

• They are doing it through the psychopathic fiction: a fictional story about themselves that makes them look like a good employee who delivers as well as a good peer. Once this fiction is established, it’s easy to hide the negative traits (including underperformance). Companies are quite open about the standards they want to encourage and the role models for the employees to follow; the job of a psychopath then becomes effectively mimicking – not becoming! - such people to tick all the desired boxes in the fake persona.

• The first step is establishing 1:1 relationships with the key people who will ultimately provide protection and cover. And then the typical “assessment – manipulation – abandonment” cycle gets in full swing again.

Assessment

• Meet as many people to create a generally good first impression. Collect as much info on the company, strategy and the short-term and long-term usefulness of the person they’re meeting. It’s helpful to know the interactions and communication patterns between the employees.

• There are lots of sources of power in the organisation, but the most important one is the informal power; informal leaders have the management’s ear – and they are precisely the people the psychopath must befriend first to his/her advantage.

• Getting to the people with the positional power is usually hard as they’re busy, but in the beginning (the “honeymoon period”), when a person just learns the ropes, both formal and informal leaders are more accessible to them. Breaking the chain of command in the beginning is not a corporate crime, so a corporate fraudster (who may or may not be a psychopath, though) has a period of time to establish themselves as an ambitious and enthusiastic player. Peers may not fully like the narcissistic part of the persona, but being friendly and open helps a lot to establish a person as a likeable one.

• The dark triad of dangerous personalities: narcissism, Machiavellianism, and psychopathy. Narcissists, as we know, are arrogant, self-centred, and consistently self-enhancing. High in Machiavellianism means being cynical and manipulative for self-interested goals. Subclinical psychopathy is characterised by cold emotions, thrill-seeking, anti-social behaviour.

• Psychopaths create their networks of “pawns” – people within the organisation possessing access to information, resources, contacts, etc. – for further calling on services. These “pawns” are charmed by them to an extent that any negative information about psychopaths is treated as “jealousy”. Pawns do change over time, but psychopaths find it easy to charm the new ones.

• Not everyone is charmed, though. Especially the people who psychopaths don’t spend much time on are the ones who can see through the wolves in the sheep skin, but it takes time and effort.

• The end game is the increased pay / bonus / promotion / derailment of a career of others or a mix of the above. This is achieved via most of the time spent on the manipulation of peers and the management to the point of even having them do the majority of the psychopath’s job or sharing their achievements with him/her.

• The manipulation involves two factors: a) the extensive use of clever impression management techniques, and b) the use of secrecy.

Impression management involves manipulating communication networks to enhance reputation and create conflicts and rivalries. Misinformation is a big part of the con job.

Secrecy can be used strategically: communicating information in confidence (even knowing that this information will spread) increases trust. Running personal errands without others knowing also creates an opportunity for manipulation.

• The strongest challenges for manipulation are the people with such traits as narcissism, assertiveness, and dominance; unsurprisingly, they tend to be found at the highest ranks in organisations. They also tend to believe they can’t be conned, that they’re smarter and stronger than others. Top ranks of firms are full of narcissistic people who fail to seek assistance and advice until it’s too late.

• Chatting with informal leaders and volunteering gossip often leads to gossip in return, and this gossip can be used strategically to pretend one is in the inside circle of knowledge leading to even more useful info and gossip.

Detractors

• The “extras” – those not being actively manipulated – are able to see the inconsistencies, lies and distortions. They just prefer to “mind their own business” to interfere or raise alarm, or don’t want to risk their position if the psychopath in a higher-up.

• It’s not uncommon for detractors to initially like the con person, only to get disillusioned about the person’s behaviour or reliability at some point.

• Organisations also have “policing” roles, which include internal audit and other roles looking for an independent assessment of people’s performance and their use of company funds. Corporate cons avoid this organisational police or counter it by doubling down on manipulating the top managers, leading to the complaints and red flags being swept under the carpet.

• MK: Large corporate fraud is perpetrated by the people who are in a position to handle large sums, i.e., top managers. It’s the HR’s and the Board’s role to ensure top managers display and act with integrity and make decisions in line with the Board-established risk tolerance and appetite.

• MK: it was said before that 1/3 of people tend to like the psychopath’s façade, 1/3 are indifferent and 1/3 are in the detractors’ camp. The winning strategy for the psychopath is to keep being likeable: this indoctrinates the people in the “allies” camp even further and seeds doubts in the people from the “detractors” camp.

Abandonment and Confrontation

• Many people don’t believe they’ve been conned even after they’ve been abandoned. [MK: look no further than stablecoins.] When in the workplace environment someone gets abandoned, they’re still around the psychopath who’s turned cold on them. The natural response of a normal person is “what did I do?”. And the most common victim response is silence for the shame at being conned. Sometimes people even regretted losing attention by the psychopath.

• Over time, psychopaths increasingly need to manage the growing discrepancy in the views by them by fellow employees. Experienced psychopaths have countermoves ready to discredit their attackers in front of the higher-ups. If done successfully, this discourages others from launching their offensives – a learned helplessness. As a second-order effect, the visibly immunity of a psychopath puts a severe dent in the credibility of the management.

• Over time a psychopath can ascend into a higher role (CEO-1 or CEO-2), and the organisational power structure gets modified to support their claim to power. [MK: I’ve been in such situation once and witnessed it happening in front of my eyes. It ended up being incredibly hard and expensive to depose the person in question.]

Part 4.