no podía caer pero cayó y / si bien sabían mucho de esto ni papá ni mamá le enseñaron a cargar con el féretro lo que es cada vez más necesario para sonreír llorando por los pies

We made our costumes and they’re so cute together. We went to a haunted maze afterwards.

I just want to write. Lets continue…

Who am I?

I am a seaman, 35 years old. I work onboard a crude oil tanker. Sailing through high seas, visiting continent by continent. Carrying one of the 'black gold' in the world.

Yes, I have been sailing for last 17 years. I have been to asia, middle east, europe, america, africa and few more. 🌍

I am proud of myself, but at the sametime, the loneliness kills me. Maybe few years more. Thats what I wish. I earn a lot, but feels like all down to the drain. I have nothing now.

For this year. I'm restarting my life.

I've done a lot of mistakes. Mostly financially, then comes to family.

I lost everything. But Am I too late?

Nope, I believe I can come back stronger. I have career, I can re-earn back. Give me 5 years. Lets redo everything. Not restarting from zero... I got experiences, I got knowledge. 🧠

Whats right, whats wrong.

I married again last 3 months. With my new wife. I prays, she's the one.❤️

This is my new beginning. Maybe another story later.

I am writing this because I am overthinking alot.

I work at sea, far from people for 6 month. I am struggle mentally. But I know, life is hard. For everyone.

Those at land, earn lesser than me. They strive harder than me. Everyone have their own challenge. Their own struggle.

I give myself a challenge.

Lets writes for 100days.

Today is day 2. Sunday, my lazy day.

So, what is my plan?

We talk again tomorrow 🫡 Adios ~

#100daystoofload

Keywords: transmission – rust, echo, fingerprint.

Blue sky is turning into purple In optic sensors, neural chain. The echo signal loops in circle, Amplification of the pain. I clench my fist; my rusty fingers Remember softness of your hand, The warmth in sensors coils, lingers, A burning chip in no man’s land. A howling wind is blowing near, You used to laugh under the storm. Your fingerprint, left on the mirror, Still casting shadow, light and warm. Your coffee cup is old and dusty, An urn with ashes of the past, A clock on wall, its hands are rusty, A doomsday hour long had passed. Your books are left with open pages, The stories you will never tell, Will burn my circuitry for ages, The last of those who toll the bell. I send transmissions every hour, One day, you might receive my words. The latest volt of solar power: “Forever yours, in all the worlds”.

Content warning: body horror

The surgical steel is reflecting my eyes, My cloudy irises holding the form. A red-letter Y of a full-body size, The heart overheated is no longer warm. Confirm every sign before taking the blade, Be sure that my shell has no human inside: What if I’m still sleeping, my dreams in cascade— No visions are left for these eyes open wide. I am just a number, a case in your file, A minute for you, but the end of my dawn. Be gentle, my coroner, gift me a smile, The very last gesture before I am gone, Be gentle, my coroner, don’t drop my heart, Don’t break it against icy tiles of the floor. No blood in aorta, no breath to restart, The signal has faded inside empty core. The very last time close my eyes with your hand, The tower has fallen, has broken the chain. Another existence, a far-away land Is where everyone meets each other again.

The conference room at Amazon's Seattle headquarters fell silent in early 2025 when CEO Andy Jassy issued a mandate that would reverberate across the technology sector and beyond. By the end of the first quarter, every division must increase “the ratio of individual contributors to managers by at least 15%”. The subtext was unmistakable: layers of middle management, long considered the connective tissue of corporate hierarchy, were being stripped away. The catalyst? An ascendant generation of workers who no longer needed supervisors to translate, interpret, or mediate their relationship with the company's most transformative technology.

Millennials, those born between 1981 and 1996, are orchestrating a quiet revolution in how corporations function. Armed with an intuitive grasp of artificial intelligence tools and positioned at the critical intersection of career maturity and digital fluency, they're not just adopting AI faster than their older colleagues. They're fundamentally reshaping the architecture of work itself, collapsing hierarchies that have stood for decades, rewriting the rules of professional development, and forcing a reckoning with how knowledge flows through organisations.

The numbers tell a story that defies conventional assumptions. According to research published by multiple sources in 2024 and 2025, 62% of millennial employees aged 35 to 44 report high levels of AI expertise, compared with 50% of Gen Z workers aged 18 to 24 and just 22% of baby boomers over 65. More striking still, over 70% of millennial users express high satisfaction with generative AI tools, the highest of any generation. Deloitte's research reveals that 56% of millennials use generative AI at work, with 60% using it weekly and 22% deploying it daily.

Perhaps most surprising is that millennials have surpassed even Gen Z, the so-called digital natives, in both adoption rates and expertise. Whilst 79% of Gen Z report using AI tools, their emotions reveal a generation still finding its footing: 41% feel anxious, 27% hopeful, and 22% angry. Millennials, by contrast, exhibit what researchers describe as pragmatic enthusiasm. They're not philosophising about AI's potential or catastrophising about its risks. They're integrating it into the very core of how they work, using it to write reports, conduct research, summarise communication threads, and make data-driven decisions.

The generational divide grows more pronounced up the age spectrum. Only 47% of Gen X employees report using AI in the workplace, with a mere 25% expressing confidence in AI's ability to provide reliable recommendations. The words Gen Xers most commonly use to describe AI? “Concerned,” “hopeful,” and “suspicious”. Baby boomers exhibit even stronger resistance. Two-thirds have never used AI at work, with suspicion running twice as high as amongst younger workers. Just 8% of boomers trust AI to make good recommendations, and 45% flatly state, “I don't trust it.”

This generational gap in AI comfort levels is colliding with a demographic shift in corporate leadership. From 2020 to 2025, millennial representation in CEO roles within Russell 3000 companies surged from 13.8% to 15.1%, whilst Gen X representation plummeted from 51.1% to 43.4%. Baby boomers, it appears, are bypassing Gen X in favour of millennials whose AI fluency makes them better positioned to lead digital transformation efforts.

A 2025 IBM report quantified this leadership advantage: millennial-led teams achieve a median 55% return on investment for AI projects, compared with just 25% for Gen X-led initiatives. The disparity stems from fundamentally different approaches. Millennials favour decentralised decision-making, rapid prototyping, and iterative improvement. Gen X leaders often cling to hierarchical, risk-averse frameworks that slow AI implementation and limit its impact.

The Flattening

The traditional corporate org chart, with its neat layers of management cascading from the C-suite to individual contributors, is being quietly dismantled. Companies across sectors are discovering that AI doesn't just augment human work; it renders entire categories of coordination and oversight obsolete.

Google cut vice president and manager roles by 10% in 2024, according to Business Insider. Meta has been systematically “flattening” since declaring 2023 its “year of efficiency”. Microsoft, whilst laying off thousands to ramp up its AI strategy, explicitly stated that reducing management layers was amongst its primary goals. At pharmaceutical giant Bayer, nearly half of all management and executive positions were eliminated in early 2025. Middle managers now represent nearly a third of all layoffs in some sectors, up from 20% in 2018.

The mechanism driving this transformation is straightforward. Middle managers have traditionally served three primary functions: coordinating information flow between levels, monitoring and evaluating employee performance, and translating strategic directives into operational tasks. AI systems excel at all three, aggregating data from disparate sources, identifying patterns, generating reports, and providing real-time performance metrics without the delays, biases, and inconsistencies inherent in human intermediaries.

At Moderna, leadership formally merged the technology and HR functions under a single Chief People and Digital Officer. The message was explicit: in the AI era, planning for work must holistically consider both human skills and technological capabilities. This structural innovation reflects a broader recognition that the traditional separation between “people functions” and “technology functions” no longer reflects how work actually happens when AI systems mediate so much of daily activity.

The flattening extends beyond eliminating positions. The traditional pyramid is evolving into what researchers call a “barbell” structure: a larger number of individual contributors at one end, a small strategic leadership team at the other, and a notably thinner middle connecting them. This reconfiguration creates new pathways for influence that favour those who can leverage AI tools to demonstrate impact without requiring managerial oversight.

Yet this transformation carries risks. A 2025 Korn Ferry Workforce Survey found that 41% of employees say their company has reduced management layers, and 37% say they feel directionless as a result. When middle managers disappear, so can the structure, support, and alignment they provide. The challenge facing organisations, particularly those led by AI-fluent millennials, is maintaining cohesion whilst embracing decentralisation. Some companies are discovering that the pendulum can swing too far: Palantir CEO Alex Karp announced intentions to cut 500 roles from his 4,100-person staff, but later research suggested that excessive flattening can create coordination bottlenecks that slow decision-making rather than accelerate it.

From Gatekeepers to Champions

Many millennials occupy a unique position in this transformation. Aged between 29 and 44 in 2025, they're established in managerial and team leadership roles but still early enough in their careers to adapt rapidly. Research from McKinsey's 2024 workplace study, which surveyed 3,613 employees and 238 C-level executives, reveals that two-thirds of managers field questions from their teams about AI tools at least once weekly. Millennial managers, with their higher AI expertise, are positioned not as resistors but as champions of change.

Rather than serving as gatekeepers who control access to information and resources, millennial managers are becoming enablers who help their teams navigate AI tools more effectively. They're conducting informal training sessions, sharing prompt engineering techniques, troubleshooting integration challenges, and demonstrating use cases that might not be immediately obvious.

At Morgan Stanley, this dynamic played out in a remarkable display of technology adoption. The investment bank partnered with OpenAI in March 2023 to create the “AI @ Morgan Stanley Assistant”, trained on more than 100,000 research reports and embedding GPT-4 directly into adviser workflows. By late 2024, the tool had achieved a 98% adoption rate amongst financial adviser teams, a staggering figure in an industry historically resistant to technology change.

The success stemmed from how millennial managers championed its use, addressing concerns, demonstrating value, and helping colleagues overcome the learning curve. Access to documents jumped from 20% to 80%, dramatically reducing search time. The 98% adoption rate stands as evidence that when organisations combine capable technology with motivated, AI-fluent leaders, resistance crumbles rapidly.

McKinsey implemented a similarly strategic approach with its internal AI tool, Lilli. Rather than issuing a top-down mandate, the firm established an “adoption and engagement team” that conducted segmentation analysis to identify different user types, then created “Lilli Clubs” composed of superusers who gathered to share techniques. This peer-to-peer learning model, facilitated by millennial managers comfortable with collaborative rather than hierarchical knowledge transfer, achieved impressive adoption rates across the global consultancy.

The shift from gatekeeper to champion requires different skills than traditional management emphasised. Where previous generations needed to master delegation, oversight, and performance evaluation, millennial managers increasingly focus on curation, facilitation, and contextualisation. They're less concerned with monitoring whether work gets done and more focused on ensuring their teams have the tools, training, and autonomy to determine how work gets done most effectively.

Reverse Engineering the Org Chart

The most visible manifestation of AI-driven generational dynamics is the rise of reverse mentoring programmes, where younger employees formally train their older colleagues. The concept isn't new; companies including Bharti Airtel launched reverse mentorship initiatives as early as 2008. But the AI revolution has transformed reverse mentoring from a novel experiment into an operational necessity.

At Cisco, initial reverse mentorship meetings revealed fundamental communication barriers. Senior leaders preferred in-person discussions, whilst Gen Z mentors were more comfortable with virtual tools like Slack. The disconnect prompted Cisco to adopt hybrid communication strategies that accommodated both preferences, a small but significant example of how AI comfort levels force organisational adaptation at every level.

Research documents the effectiveness of these programmes. A Harvard Business Review study found that organisations with structured reverse mentorship initiatives reported a 96% retention rate amongst millennial mentors over three years. The benefits flow bidirectionally: senior leaders gain technological fluency, whilst younger mentors develop soft skills like empathy, communication, and leadership that are harder to acquire through traditional advancement.

Major corporations including PwC, Citi Group, Unilever, and Johnson & Johnson have implemented reverse mentoring for both diversity perspectives and AI adoption. At Allen & Overy, the global law firm, programmes helped the managing partner understand experiences of Black female lawyers, directly influencing firm policies. The initiative demonstrates how reverse mentoring serves multiple organisational objectives simultaneously, addressing both technological capability gaps and broader cultural evolution.

This informal teaching represents a redistribution of social capital within organisations. Where expertise once correlated neatly with age and tenure, AI fluency has introduced a new variable that advantages younger workers regardless of their position in the formal hierarchy. A 28-year-old data analyst who masters prompt engineering techniques suddenly possesses knowledge that a 55-year-old vice president desperately needs, inverting traditional power dynamics in ways that can feel disorienting to both parties.

Yet reverse mentoring isn't without complications. Some senior leaders resist being taught by subordinates, perceiving it as a threat to their authority or an implicit criticism of their skills. Organisational cultures that strongly emphasise hierarchy and deference to seniority struggle to implement these programmes effectively. Success requires genuine commitment from leadership, clear communication about programme goals, and structured frameworks that make the dynamic feel collaborative rather than remedial. Companies that position reverse mentoring as “mutual learning” rather than “junior teaching senior” report higher participation and satisfaction rates.

The most sophisticated organisations are integrating reverse mentoring into broader training ecosystems, embedding intergenerational knowledge transfer into onboarding processes, professional development programmes, and team structures. This normalises the idea that expertise flows multidirectionally, preparing organisations for a future where technological change constantly reshapes who knows what.

Rethinking Training

Traditional corporate training programmes were built on assumptions that no longer hold. They presumed relatively stable skill requirements, standardised learning pathways, and long time horizons for skill application. AI has shattered this model.

The velocity of change means that skills acquired in a training session may be obsolete within months. The diversity of AI tools, each with different interfaces, capabilities, and limitations, makes standardised curricula nearly impossible to maintain. Most significantly, the generational gap in baseline AI comfort means that a one-size-fits-all approach leaves some employees bored whilst others struggle to keep pace.

Forward-thinking organisations are abandoning standardised training in favour of personalised, adaptive learning pathways powered by AI itself. These systems assess individual skill levels, learning preferences, and job requirements, then generate customised curricula that evolve as employees progress. According to research published in 2024, 34% of companies have already implemented AI in their training programmes, with another 32% planning to do so within two years.

McDonald's provides a compelling example, implementing voice-activated AI training systems that guide new employees through tasks whilst adapting to each person's progress. The fast-food giant reports that the system reduces training time whilst improving retention and performance, particularly for employees whose first language isn't English. Walmart partnered with STRIVR to deploy AI-powered virtual reality training across its stores, achieving a 15% improvement in employee performance and a 95% reduction in training time. Amazon created training modules teaching warehouse staff to safely interact with robots, with AI enhancement allowing the system to adjust difficulty based on performance.

The generational dimension adds complexity. Younger employees, particularly millennials and Gen Z, often prefer self-directed learning, bite-sized modules, and immediate application. They're comfortable with technology-mediated instruction and actively seek out informal learning resources like YouTube tutorials and online communities. Older employees may prefer instructor-led training, comprehensive explanations, and structured progression. Effective training programmes must accommodate these differences without stigmatising either preference or creating perception that one approach is superior to another.

Some organisations are experimenting with intergenerational training cohorts that pair employees across age ranges. These groups tackle real workplace challenges using AI tools, with the diverse perspectives generating richer problem-solving whilst simultaneously building relationships and understanding across generational lines. Research indicates that these integrated teams improve outcomes on complex tasks by 12-18% compared with generationally homogeneous groups. The learning happens bidirectionally: younger workers gain context and judgment from experienced colleagues, whilst older workers absorb technological techniques from digital natives.

The Collaboration Conundrum

Intergenerational collaboration has always required navigating different communication styles, work preferences, and assumptions about professional norms. AI introduces new fault lines. When team members have vastly different comfort levels with the tools increasingly central to their work, collaboration becomes more complicated.

Research published in multiple peer-reviewed journals identifies four organisational practices that promote generational integration and boost enterprise innovation capacity by 12-18%: flexible scheduling and remote work options that accommodate different preferences; reverse mentoring programmes that enable bilateral knowledge exchange; intentional intergenerational teaming on complex projects; and social activities that facilitate casual bonding across age groups.

These practices address the trust and familiarity deficits that often characterise intergenerational relationships in the workplace. When a 28-year-old millennial and a 58-year-old boomer collaborate on a project, they bring different assumptions about everything from meeting frequency to decision-making processes to appropriate communication channels. Add AI tools to the mix, with one colleague using them extensively and the other barely at all, and the potential for friction multiplies exponentially.

The most successful teams establish explicit agreements about tool use. They discuss which tasks benefit from AI assistance, agree on transparency about when AI-generated content is being used, and create protocols for reviewing and validating AI outputs. This prevents situations where team members make different assumptions about work quality, sources, or authorship. One pharmaceutical company reported that establishing these “AI usage norms” reduced project conflicts by 34% whilst simultaneously improving output quality.

At McKinsey, the firm discovered that generational differences in AI adoption created disparities in productivity and output quality. The “Lilli Clubs” created spaces where enthusiastic adopters could share techniques with more cautious colleagues. Crucially, these clubs weren't mandatory, avoiding the resentment that forced participation can generate. Instead, they offered optional opportunities for learning and connection, allowing relationships to develop organically rather than through top-down mandate.

Some organisations use AI itself to facilitate intergenerational collaboration. Platforms can match mentors and mentees based on complementary skills, career goals, and personality traits, making these relationships more likely to succeed. Communication tools can adapt to user preferences, offering some team members the detailed documentation they prefer whilst providing others with concise summaries that match their working style.

Yet technology alone cannot bridge generational divides. The most critical factor is organisational culture. When leadership, often increasingly millennial, genuinely values diverse perspectives and actively works to prevent age-based discrimination in either direction, intergenerational collaboration flourishes. When organisations unconsciously favour either youth or experience, resentment builds and collaboration suffers.

There's evidence that age-diverse teams produce better outcomes when working with AI. Younger team members bring technological fluency and willingness to experiment with new approaches. Older members contribute domain expertise, institutional knowledge, and critical evaluation skills honed over decades. The combination, when managed effectively, generates solutions that neither group would develop independently. Companies report that mixed-age AI implementation teams catch more edge cases and potential failures because they approach problems from complementary angles.

Research by Deloitte indicates that 74% of Gen Z and 77% of millennials believe generative AI will impact their work within the next year, and they're proactively preparing through training and skills development. But they also recognise the continued importance of soft skills like empathy and leadership, areas where older colleagues often have deeper expertise developed through years of navigating complex human dynamics that AI cannot replicate.

The Entry-Level Paradox

One of the most troubling implications of AI-driven workplace transformation concerns entry-level positions. The traditional paradigm assumed that routine tasks provided a foundation for advancing to more complex responsibilities. Junior employees spent their first years mastering basic skills, learning organisational norms, and building relationships before gradually taking on more strategic work. AI threatens this model.

Law firms are debating cuts to incoming analyst classes as AI handles document review, basic research, and routine brief preparation. Finance companies are automating financial modelling and presentation development, tasks that once occupied entry-level analysts for years. Consulting firms are using AI to conduct initial research and create first-draft deliverables. These changes disproportionately affect Gen Z workers just entering the workforce and millennial early-career professionals still establishing themselves.

The impact extends beyond immediate job availability. When entry-level positions disappear, so do the informal learning opportunities they provided. Junior employees traditionally learned organisational culture, developed professional networks, and discovered career interests through entry-level work. If AI performs these tasks, how do new workers develop the expertise needed for mid-career advancement? Some researchers worry about creating a generation with sophisticated AI skills but insufficient domain knowledge to apply them effectively.

Some organisations are actively reimagining entry-level roles. Rather than eliminating these positions entirely, they're redefining them to focus on skills AI cannot replicate: relationship building, creative problem-solving, strategic thinking, and complex communication. Entry-level employees curate AI outputs rather than creating content from scratch, learning to direct AI systems effectively whilst developing the judgment to recognise when outputs are flawed or misleading.

This shift requires different training. New employees must develop what researchers call “AI literacy”: understanding how these systems work, recognising their limitations, formulating effective prompts, and critically evaluating outputs. They must also cultivate distinctly human capabilities that complement AI, including empathy, ethical reasoning, cultural sensitivity, and collaborative skills that machines cannot replicate.

McKinsey's research suggests that workers using AI spend less time creating and more time reviewing, refining, and directing AI-generated content. This changes skill requirements for many roles, placing greater emphasis on critical evaluation, contextual understanding, and the ability to guide systems effectively. For entry-level workers, this means accelerated advancement to tasks once reserved for more experienced colleagues, but also heightened expectations for judgment and discernment that typically develop over years.

The generational implications are complex. Millennials, established in their careers when AI emerged as a dominant workplace force, largely avoided this entry-level disruption. They developed foundational skills through traditional means before AI adoption accelerated, giving them both technical fluency and domain knowledge. Gen Z faces a different landscape, entering a workplace where those traditional stepping stones have been removed, forcing them to develop different pathways to expertise and advancement.

Some researchers express concern that this could create a “missing generation” of workers who never develop the deep domain knowledge that comes from performing routine tasks at scale. Radiologists who manually reviewed thousands of scans developed an intuitive pattern recognition that informed their interpretation of complex cases. If junior radiologists use AI from day one, will they develop the same expertise? Similar questions arise across professions from law to engineering to journalism.

Others argue that this concern reflects nostalgia for methods that were never optimal. If AI can perform routine tasks more accurately and efficiently than humans, requiring humans to master those tasks first is wasteful. Better to train workers directly in the higher-order skills that AI cannot replicate, using the technology from the start as a collaborative tool rather than treating it as a crutch that prevents skill development. The debate remains unresolved, but organisations cannot wait for consensus. They must design career pathways that prepare workers for AI-augmented roles whilst ensuring they develop the expertise needed for long-term success.

The Power Shift

For decades, corporate power correlated with experience. Senior leaders possessed institutional knowledge accumulated over years: relationships with key stakeholders, understanding of organisational culture, awareness of past initiatives and their outcomes. This knowledge advantage justified hierarchical structures where deference flowed upward and information flowed downward.

AI disrupts this dynamic by democratising access to institutional knowledge. When Morgan Stanley's AI assistant can instantly retrieve relevant information from 100,000 research reports, a financial adviser with two years of experience can access insights that previously required decades to accumulate. When McKinsey's Lilli can surface case studies and methodologies from thousands of past consulting engagements, a junior consultant can propose solutions informed by the firm's entire history.

This doesn't eliminate the value of experience, but it reduces the information asymmetry that once made experienced employees indispensable. The competitive advantage shifts to those who can most effectively leverage AI tools to access, synthesise, and apply information. Millennials, with their higher AI fluency, gain influence regardless of their tenure.

The power shift manifests in subtle ways. In meetings, millennial employees increasingly challenge assumptions by quickly surfacing data that contradicts conventional wisdom. They propose alternatives informed by rapid AI-assisted research that would have taken days using traditional methods. They demonstrate impact through AI-augmented productivity that exceeds what older colleagues with more experience can achieve manually.

This creates tension in organisations where cultural norms still privilege seniority. Senior leaders may feel their expertise is being devalued or disrespected. They may resist AI adoption partly because it threatens their positional advantage. Organisations navigating this transition must balance respect for experience with recognition of AI fluency as a legitimate form of expertise deserving equal weight in decision-making.

Some companies are formalising this rebalancing. Job descriptions increasingly include AI skills as requirements, even for senior positions. Promotion criteria explicitly value technological proficiency alongside domain knowledge. Performance evaluations assess not just what employees accomplish but how effectively they leverage available tools. These changes send clear signals about organisational values and expectations.

The shift also affects hiring. Companies increasingly seek millennials and Gen Z candidates for leadership roles, particularly positions responsible for innovation, digital transformation, or technology strategy. The IBM report finding that millennial-led teams achieve more than twice the ROI on AI projects provides quantifiable justification for prioritising AI fluency in leadership selection.

Yet organisations risk overcorrecting. Institutional knowledge remains valuable, particularly the tacit understanding of organisational culture, stakeholder relationships, and historical context that cannot be easily codified in AI systems. The most effective organisations combine millennial AI fluency with the institutional knowledge of longer-tenured employees, creating collaborative models where both forms of expertise are valued and leveraged in complementary ways rather than positioned as competing sources of authority.

Corporate Cultures in Flux

The transformation described throughout this article represents a fundamental restructuring of how organisations function, how careers develop, and how power and influence are distributed. As millennials continue ascending to leadership positions and AI capabilities expand, these dynamics will intensify.

Within five years, McKinsey estimates that AI could add $4.4 trillion in productivity growth potential from corporate use cases, with a long-term global economic impact of $15.7 trillion by 2030. Capturing this value requires organisations to solve the challenges outlined here: flattening hierarchies without losing cohesion, training employees with vastly different baseline skills, facilitating collaboration across generational divides, reimagining entry-level roles, and navigating power shifts as technical fluency becomes as valuable as institutional knowledge.

The evidence suggests that organisations led by AI-fluent millennials are better positioned to navigate this transition. Their pragmatic enthusiasm for AI, combined with sufficient career maturity to occupy influential positions, makes them natural champions of transformation. But their success depends on avoiding the generational chauvinism that would dismiss the contributions of older colleagues or the developmental needs of younger ones.

The most sophisticated organisations recognise that generational differences in AI comfort levels are not problems to be solved but realities to be managed. They're designing systems, cultures, and structures that leverage the strengths each generation brings: Gen Z's creative experimentation and digital nativity, millennial pragmatism and AI expertise, Gen X's strategic caution and risk assessment, and boomer institutional knowledge and stakeholder relationships accumulated over decades.

Research from McKinsey's 2024 workplace survey reveals a troubling gap: employees are adopting AI much faster than leaders anticipate, with 75% already using it compared with leadership estimates of far lower adoption. This disconnect suggests that in many organisations, the transformation is happening from the bottom up, driven by millennial and Gen Z employees who recognise AI's value regardless of whether leadership has formally endorsed its use.

When employees bring their own AI tools to work, which 78% of surveyed AI users report doing, organisations lose the ability to establish consistent standards, manage security risks, or ensure ethical use. The solution is not to resist employee-driven adoption but to channel it productively through clear policies, adequate training, and leadership that understands and embraces the technology rather than viewing it with suspicion or fear.

Organisations with millennial leadership are more likely to establish those enabling conditions because millennial leaders understand AI's capabilities and limitations from direct experience. They can distinguish hype from reality, identify genuine use cases from superficial automation, and communicate authentically about both opportunities and challenges without overpromising results or understating risks.

PwC's 2024 Global Workforce Hopes & Fears Survey, which gathered responses from more than 56,000 workers across 50 countries, found that amongst employees who use AI daily, 82% expect it to make their time at work more efficient in the next 12 months, and 76% expect it to lead to higher salaries. These expectations create pressure on organisations to accelerate adoption and demonstrate tangible benefits. Meeting these expectations requires leadership that can execute effectively on AI implementation, another area where millennial expertise provides measurable advantages.

Yet the same research reveals persistent concerns about accuracy, bias, and security that organisations must address. Half of workers surveyed worry that AI outputs are inaccurate, and 59% worry they're biased. Nearly three-quarters believe AI introduces new security risks. These concerns are particularly pronounced amongst older employees already sceptical about AI adoption. Dismissing these worries as Luddite resistance is counterproductive and alienates employees whose domain expertise remains valuable even as their technological skills lag.

The path forward requires humility from all generations. Millennials must recognise that their AI fluency, whilst valuable, doesn't make them universally superior to older colleagues with different expertise. Gen X and boomers must acknowledge that their experience, whilst valuable, doesn't exempt them from developing new technological competencies. Gen Z must understand that whilst they're digital natives, effective AI use requires judgment and context that develop with experience.

Organisations that successfully navigate this transition will emerge with significant competitive advantages: more productive workforces, flatter and more agile structures, stronger innovation capabilities, and cultures that adapt rapidly to technological change. Those that fail risk losing their most talented employees, particularly millennials and Gen Z workers who will seek opportunities at organisations that embrace rather than resist the AI transformation.

The corporate hierarchies, training programmes, and collaboration models that defined the late 20th and early 21st centuries are being fundamentally reimagined. Millennials are not simply participants in this transformation. By virtue of their unique position, combining career maturity with native AI fluency, they are its primary architects. How they wield this influence, whether inclusively or exclusively, collaboratively or competitively, will shape the workplace for decades to come.

The revolution, quiet though it may be, is fundamentally about power: who has it, how it's exercised, and what qualifies someone to lead. For the first time in generations, technical fluency is challenging tenure as the primary criterion for advancement and authority. The outcome of this contest will determine not just who runs tomorrow's corporations but what kind of institutions they become.

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Sources and References

1. Deloitte Global Gen Z and Millennial Survey 2025. Deloitte. https://www.deloitte.com/global/en/issues/work/genz-millennial-survey.html

2. McKinsey & Company (2024). “AI in the workplace: A report for 2025.” McKinsey Digital. Survey of 3,613 employees and 238 C-level executives, October-November 2024. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work

3. PYMNTS (2025). “Millennials, Not Gen Z, Are Defining the Gen AI Era.” https://www.pymnts.com/artificial-intelligence-2/2025/millennials-not-gen-z-are-defining-the-gen-ai-era

4. Randstad USA (2024). “The Generational Divide in AI Adoption.” https://www.randstadusa.com/business/business-insights/workplace-trends/generational-divide-ai-adoption/

5. Alight (2024). “AI in the workplace: Understanding generational differences.” https://www.alight.com/blog/ai-in-the-workplace-generational-differences

6. WorkTango (2024). “As workplaces adopt AI at varying rates, Gen Z is ahead of the curve.” https://www.worktango.com/resources/articles/as-workplaces-adopt-ai-at-varying-rates-gen-z-is-ahead-of-the-curve

7. Fortune (2025). “AI is already changing the corporate org chart.” 7 August 2025. https://fortune.com/2025/08/07/ai-corporate-org-chart-workplace-agents-flattening/

8. Axios (2025). “Middle managers in decline as 'flattening' spreads, AI advances.” 8 July 2025. https://www.axios.com/2025/07/08/ai-middle-managers-flattening-layoffs

9. ainvest.com (2025). “Millennial CEOs Rise as Baby Boomers Bypass Gen X for AI-Ready Leadership.” https://www.ainvest.com/news/millennial-ceos-rise-baby-boomers-bypass-gen-ai-ready-leadership-2508/

10. Harvard Business Review (2024). Study on reverse mentorship retention rates.

11. eLearning Industry (2024). “Case Studies: Successful AI Adoption In Corporate Training.” https://elearningindustry.com/case-studies-successful-ai-adoption-in-corporate-training

12. Morgan Stanley (2023). “Launch of AI @ Morgan Stanley Debrief.” Press Release. https://www.morganstanley.com/press-releases/ai-at-morgan-stanley-debrief-launch

13. OpenAI Case Study (2024). “Morgan Stanley uses AI evals to shape the future of financial services.” https://openai.com/index/morgan-stanley/

14. PwC (2024). “Global Workforce Hopes & Fears Survey 2024.” Survey of 56,000+ workers across 50 countries. https://www.pwc.com/gx/en/news-room/press-releases/2024/global-hopes-and-fears-survey.html

15. Salesforce (2024). “Generative AI Statistics for 2024.” Generative AI Snapshot Research Series, surveying 4,000+ full-time workers. https://www.salesforce.com/news/stories/generative-ai-statistics/

16. McKinsey & Company (2025). “The state of AI: How organisations are rewiring to capture value.” https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai

17. Research published in Partners Universal International Innovation Journal (2024). “Bridging the Generational Divide: Fostering Intergenerational Collaboration and Innovation in the Modern Workplace.” https://puiij.com/index.php/research/article/view/136

18. Korn Ferry (2025). “Workforce Survey 2025.”

19. IBM Report (2025). ROI analysis of millennial-led vs Gen X-led AI implementation teams.

20. Business Insider (2024). Report on Google's management layer reductions.

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Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

In Summary: * This Saturday had its high points: both college football games I cheer for won their respective games, and that brought smiles to my face, but the highest point was the wife taking me out for lunch at a favorite restaurant. Since I'm basically home bound, every opportunity to be out around other people enjoying my self is more precious than you can probably imagine.

Prayers, etc.: * My daily prayers.

Health Metrics: * bw= 220.02 lbs. * bp= 135/85 (62)

Exercise: * kegel pelvic floor exercise, half squats, calf raises, wall push-ups

Diet: * 06:20 – toast and butter * 07:05 – lasagna * 12:00 – big 3-plate meal at a buffet restaurant, lunch with the wife

Activities, Chores, etc.: * 06:00 – bank accounts activity monitored * 06:30 – read, pray, listen to news reports from various sources * 11:00 – tuned into a Big 10 Conference College Football Game, Penn St. Nittany Lions at Ohio St. Buckeyes * 11:30 to 13:30 – lunch with the wife at a favorite restaurant * 13:00 – back home and back to the Ohio St. game * 14:15 – after the Ohio St. win, I've tuned in the IU Sports Radio Network ahead of this afternoon's Big 10 Conference College Football Game, Indiana Hoosiers at Maryland Terrapins GO HOOSIERS! * 17:55 – after the big IU win, I'm catching up on news reports from various sources, reading, praying, etc. * 19:00 – Listening to Gregorian Chant while wrapping up my night prayers.

Chess: * 10:10 – moved in all pending CC games

Listening to Gregorian Chant while wrapping up my night prayers.

and the adventure continues ...

It's not just Linux that has a browser problem, but somehow the whole world. Browsers are becoming increasingly complex with lots of features that I don't need.

I just want to view websites.

GNOME Web (formerly known as Epiphany) is a wonderfully minimalist browser.

It's not perfect:

There are no extensions (at least not via dconf settings, and even then they don't work well), but I don't really need any.

It has a built-in ad blocker that can only be customized via dconf, but that's okay too.

It's just a totally uncluttered browsing experience.

The only thing I miss is SpeedDial. I haven't been able to do without it since Opera 8.

But there's HTML, CSS, and JavaScript. And thanks to the AI correcting my amateurish code, I now have SpeedDial as my start page.

If you want to take a look, you can find it on Codeberg: Tools for GNOME

The following features are included:

• Detection of whether a search term or a URL will be entered
• Search with Kagi (can be easily changed)
• Separate URL.js – enter the URL and image there
• Navigation with arrow keys

A zine chronicling the Conquering the Barbarian Altanis D&D campaign.

This issue details sessions 88, 89, and 90.

Adventurers explore a crypt, a barrow, and a temple.

You can download the issue here.

Overlord's Annals zine is available as part of the Ever & Anon APA, issue 5:

#Zine

For many, this month is when gift-giving season officially begins in the United States thanks to Black Friday, which is quickly consuming most of November in many cases. As a result, even though online shopping is something most of us engage in year-round, now is a particularly important time to discuss how to safely shop online. Use the following tips and tools to help keep your holiday season as stress free as possible. We can't help you with your crazy uncle and his insane conspiracy theories, but we can help make sure your credit card number doesn't get stolen while finding the perfect gift online.

• Use payment-masking services. While credit cards offer benefits (such as rewards points or purchase protections), the astronomical rise in data breaches and e-commerce compromise makes online payments risky. Instead, I recommend utilizing pre-paid cards, gift cards, or alias payment services like Privacy.com in the US or Revolut in Europe to safeguard your real data from theft. The effects of a data breach could be as minimal as having to get a new card or as serious as maxing out your credit card, stealing your identity, or worse. And sure, while you can dispute those charges and order a new card, the point is to minimize stress, right? Why would you say “I can always buy new tires” when you could just avoid hitting the nail in the road in the first place? It's worth noting that some banks already offer digital masked cards, so you might even be able to take advantage of this without any extra effort, though you should be aware that using the services listed here offer additional privacy benefits on top of the security ones. You can find a few more masked payment service recommendations here.

• Use cash. Sometimes shopping in-person can have its perks, too. You can save money on shipping, get it quicker, and you don’t have to wonder if your loved one will come home and find it on the porch before you can get home and hide it. When shopping in person, cash remains king. Your bank will absolutely sell data about your shopping habits based on when, where, and you much you spend on your card to advertisers. As a practical bonus, if you’re buying a gift for someone who has access to your bank statements (such as a significant other), cash can help shield your purchases and keep the gift a surprise. Additionally, holiday spending and gift giving is often a source of debt in the new year, so using cash will help you stick to your budget and start the new year off on a positive note.

• Use alias email addresses. These services forward emails to your inbox while hiding your real email address, providing both privacy and security with convenience. By using different email addresses for each site, you make it slightly harder to be tracked across sites, but all your emails arrive in one place. This also helps with spam – if the company won't respect opt-outs or if they sell your email address and you start getting tons of spam, you can simply disable the alias once your item arrives. And as a bonus, it improves your security as it changes your login on each site and makes it harder for credential stuffing attacks if your email gets exposed in a data breach. My top recommendations for providers are Addy.io and SimpleLogin but some new players have entered the space this year. You can check them out here.

• Secure your accounts. Be sure to use strong passwords with a good password manager and use two-factor authentication (2FA) on all your accounts that offer it. This will prevent attackers from accessing your accounts, where they can then do things like buy stuff using your stored card or see your information like name and shipping address to threaten and extort you. I know the holidays are a hectic time for most people with travel and family and such, but it also usually means some paid time off. Take advantage of some of that down time and set aside an hour or two to pick a good password manager, change your passwords and password habits, and enable 2FA. You can find more information on all of that here.

• Use a PO Box. PO Boxes can serve tons of great purposes that you didn’t even know you needed. To start, they can be pretty inexpensive, in some places as little as $20/year. They can be handy because your packages don’t sit unguarded on your porch while you’re at work, instead sitting safely inside the building. And of course, you don’t have to worry about some stranger on the internet snagging your home address, whether that’s the random seller on Etsy, the rogue employee at Amazon, or the cybercriminal who hopefully didn’t steal your information because you already implemented my other advice.

• Use reputable websites. These days there are tons of websites and apps promising to help you score a great deal on something by taking you to some website you’ve probably never heard of. While some of these are legitimate, others aren’t. The last piece of stress you probably want to pile onto the chaos of the holidays is having your data stolen. It’s just not worth saving an extra $10 on shipping. That said, I'm also vehemently opposed to Amazon for a number of reasons, so when I say “stick to reputable sites” I’m not advocating for getting everything on Amazon to play it safe. I prefer to buy directly from the manufacturer when possible using alias cards and email addresses, but there’s also big box stores, department stores, and pretty much anyone else. Not to say that Target or Etsy aren’t evil, I’m simply trying to make it clear that this isn’t a call for readers to continue to feed the abusive Amazon monopoly, it’s a warning to be wary of those dime-a-dozen deal sites that may or may not be properly securing your data.

• Beware scammers. Scammers are always drawn to opportunities to make money and the holidays are a great opportunity for them to take advantage of the increased online financial activity and the surrounding chaos to try to sneak in phishing attacks like “there’s a problem with your Amazon order, click here to correct it” or “here’s your receipt for your order of an iPad Pro/Samsung Galaxy” or “low balance” alerts from your bank (all designed to get your login credentials or card numbers directly). The best way to avoid these scams is to slow down, take a deep breath, and think. Ask yourself “did I even use this site recently?” For example, if you don’t bank with Bank of America, then how would you be getting a low-balance alert? Even if you suspect the alert is legitimate, go directly to the website and log in. Do not click the link in the email no matter what. If there really is an issue, there will be a message waiting in your inbox or a pop-up as soon as you login asking you to correct the issue. As an extra measure, you can call customer service to verify – but again, make sure you get the customer service number from an official source like the retailer’s website or the back of your credit card. Be careful if you “Google” the site as a way to find their customer service number, there have been cases where scammers abuse ads or even AI summaries to direct people to fake websites with fake customer service numbers.

• Don’t quit on December 26. The thing about these habits is that they’re great any time, not just around the holidays. Shopping is something we do all the time, all year, and these strategies can be implemented there, too. You can pay cash at the grocery store or when getting gas. You can use payment-masking services to pay for your subscription services or bills. Even a PO Box can be a neat thing to have on hand if you rent and move in the same area frequently, if you need an address on file for work, or freelance and need somewhere to send checks or a return address for merchandise you sell.

I hope these tips help keep you safer online this holiday season, and good luck finding that perfect gift!

So many audiobooks this month!

1. The Hitchhiker's Guide to the Galaxy by Douglas Adams [audio 5h 51min]: This was the first time I've listened to the audiobook version narrated by Stephen Fry. All I can say is that it’s excellent! I've read this a couple of times before (years ago now) and I truly enjoyed the audiobook experience.

2. His Secret Illuminations (The Warrior's Guild #1) by Scarlett Gale, 442p: Such a cool change for a romance novel: the female protagonist is a big and experienced warrior while the male protagonist is a sheltered, innocent monk. The POV is from the monk, Lucien, and he absolutely adores Glory (also known as the “She-Wolf”) in the few chances he had to look at her while at the monastery. It's a sweet, slow-burn romance with emphasis on consent and respecting boundaries. With Glory, Lucien goes out of the monastery and sees the outside world for the first time. He feels overwhelmed at first, but Glory is always there to help him. Lucien is a scribe, and he has some cool magic abilities that are useful for them to track down some missing manuscripts. It was a nice, refreshing read.

3. The Restaurant at the End of the Universe (Hitchhiker's Guide to the Galaxy #2) by Douglas Adams [audio 5h 47min]: This was the first time I've listened to the audiobook narrated by Martin Freeman. It’s impressive how Freeman voices all the characters so distinctly.  It’s excellent! Highly recommended!

4. The Rook (The Checquy Files #1) by Daniel O'Malley, 512p: Definitely a page turner! It's an amnesia/mystery/special powers/secret organization plot with a female lead character. I enjoyed the pace of the book: it kept me interested until the end. The sense of humour is delicious and reminded me of Dr. Who. I still don't know how to say the lead character name, Myfawny Thomas, but I really liked her! It was interesting to get to know her by the letters she wrote to herself. But you gotta have an open mind and turn on your “suspension of disbelief” mode at full power. Lots of crazy things happen and the characters have all kinds of unimaginable powers.

5. Technofeudalism: What Killed Capitalism by Yanis Varoufakis [audio 7h 39min]: Interesting read exploring how our global economy is changing with the rise of tech giants. The author presents his theory of how Capitalism has been turned into Technofeudalism, where powerful tech companies act like feudal lords, controlling digital platforms and data instead of land. Users and smaller businesses are like “serfs” who provide data and labour, often for free, to gain access to these platforms, which then extract value from them. I'll admit that I didn't grasp all the economic concepts, but Varoufakis makes the subject accessible through his conversational approach. The book is framed as if he's explaining these ideas to his father, which helps break down complex theories into more digestible pieces.

6. Two Can Play by Ali Hazelwood [audio 4h 24min]: This was a nice cozy novella with nerdy protagonists working in the video game industry. The story centers on team members from rival gaming companies who are unexpectedly forced to collaborate during a wintery team-building retreat. The novella leans into the miscommunication trope, with nice banter and book loving nerdery. It’s a quick read with low-stakes drama and high levels of geeky chemistry.

7. Neuromancer (Sprawl #1) by William Gibson, 271p: I first read this book 12 years ago, and honestly, I remember finding it confusing. This time around I think I'm grasping more of it, but still, I don't know what exactly is going on half of the time. I can see how this book is a cultural reference to the whole cyberpunk genre, but, wow, it is a strange ride. It's gritty, it's dark and, honestly, the writing style doesn't capture me too much.

#readinglist #books #reading

I was never confident enough to go into yolo mode in Claude Code after I had such a bad experience with Cursor…or I failed quite hard with the prompts. 🤷

Last weekend, I wanted to try it out for a task where I didn't want to sit next to Claude, watch it, and prevent it from failing. So I’ve made a new attempt. “We” planned the task together; I tried to be as precise as I could be with the prompt and got a first draft as a result. Then we iterated and refined the plan until I was confident with it.

On the weekend, we had planned some family time. It was the best opportunity to let Claude go wild with the planned task. Later in the evening when I was back with the family, I was a little excited about how it worked out. I took a first look, and Claude had completed the task. The result was working, and the tests were green. So this looked promising. I peeked at some files, and it looked good.

After I did a full review on the next day, I was quite happy with the result. There were some small adjustments where Claude was not following the rules exactly, but this is ok. Like checking a PR from another colleague.

Maybe I should try this more. One idea would be that after a finished workday, I go into yolo mode with Claude and review the results on the next day. But before I can do this with confidence, I need to refine my Claude settings. The settings are there, but I never touched them, really. There is potential here. :)

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50 of #100DaysToOffload
#log #dev #claude
Thoughts?

Fallen.

Das Laub fällt.

“Fall” – Herbst.

Alles wird neu.

Loslassen, fallen,

neu werden.

Der November ist viel schöner

als sein Ruf.

Abschied nehmen.

Trauern.

Neu beginnen.

Der Trost der Natur,

ihr Versprechen: es gibt

kein Ende.

Alles beginnt immer wieder

von Neuem.

Und wir sind im Kreislauf -

immer und ewig – mitten drin.

Die Küste begrüßt den November mit einem spektakulären Sonnenaufgang. Alle Möwen haben sich auf der Nordermole am Leuchtturm versammelt, um ihn zu sehen.

Am Strand tollen die Hunde. Heute ist ihr Glückstag. Endlich dürfen sie sich wieder im weissen Sand wälzen.

Das Wasser hat noch immer 12 Grad. Die Stege sind abgebaut. Die Winterschwimmer suchen sich ihren Weg an der Barrikade vorbei, erklimmen die Ufersteine, balancieren bis zu einem schmalen Sandstreifen und tauchen dann ins eisige Nass.

Einige Tage lag ein rosa Badeanzug über einem Stein. Als sei er vom Sommer übrig geblieben. Jetzt ist er weg. Der Sommer ist vorbei. Genau wie der goldene Oktober.

Es ist November.

Der stille Monatsfreund, der die Hektik des Küstenjahres endgültig hinter sich lässt.

Auf dem Büchertisch stapeln sich “Rebellinnen zu Fuß”, Rilkes Winter und Wintergeschichten von Tania Blixen. Im Libby Regal warten Hörbücher für die Weihnachtszeit. Schon verrückt! Hörbücher zu hören, hat das Fernsehen fast vollständig abgelöst.

Vor einem Jahr im November habe ich angefangen, Klavier zu spielen. Seit dem spiele ich beinahe jeden Tag. Aktuell mühe ich mich mit “Ave Maria” und einem Stück von Stevie Wonder. Die Musik ist jetzt schon manchmal in den Fingern, wenn die auch weiterhin kräftig daneben hauen. “Für Elise”, Motivation und Ziel, ist weiterhin nicht in Sicht. Aber was soll´s.

Der Weg dahin ist voller Musik.

I am not afraid.

“Most of the old moles I know wish they had listened less to their fears and more to their dreams.”

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People, you know, survive. You gotta survive. But then you have to know when to step away from that survival. Because see, life is not—it's never going to be the same. It's always changing. And it's not always comfortable. And you have to take a chance and a risk and the chance and risk you have to take is on you, right? It's investing in you... The universe is ready to catch you. But you have to take the step

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#book #quote #poetry #motivation

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