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Will AI take your job? The answer is complicated, new economic research from AI company Anthropic suggests.

According to the author, two of the questions he gets most frequently when he tells people that he covers AI and writes a book on the subject is: am I going to lose my job? And, what should my kids study?

These questions are difficult to answer. He often falls back on saying that he doubts there will be mass unemployment, which is not the same thing as saying your particular job is safe. And he says that it is important to teach kids to be lifelong learners, which isn’t a very satisfying response.

So far, few people have lost their jobs directly due to AI. Even some of the layoffs that companies have ascribed to AI, such as the recent draconian layoffs at the payments firm Block, seem to be, at least partly, “AI-washing”—attributing layoffs to AI, because it makes a company look tech savvy, when the real reason is due to business headwinds or unrelated bad decisions. Block, for example, tripled its workforce during the pandemic, and many suspect it is simply trying to slim down a bloated workforce.

Every previous technology has, in the long-run, created more jobs than it has destroyed. But still, some insist that AI is different because it is being adopted so broadly and so quickly across different industries, and because it is hitting at the core of our competitive advantage over machines—our intelligence. As to the second question, about what kids should study, that’s tough too because while previous technologies have created more jobs than they’ve eliminated, exactly what those new jobs will be has always been difficult to predict in advance. It wasn’t obvious, for instance, when smartphones first appeared, that social media influencers would be a viable career.

A new research paper from economists Maxim Massenkoff and Peter McCrory at the AI company Anthropic assesses how exposed various professions are to AI by looking at the percentage of tasks in that field that the technology could potentially automate. They also try to gauge the gap between this total possible exposure, and the extent to which AI is currently being used to automate those tasks, a measure they call “observed exposure.”

The paper got a lot of attention on social media because the researchers included an eye-catching radar plot-style chart that highlights just how jagged AI’s impacts are, especially when it comes to observed exposure.

For instance, AI is having relatively large impacts on fields involving office administration and computers and math, but relatively little on things like life sciences and social sciences or healthcare, even though those two areas have relatively high potential exposures. Then there are those areas with very low potential exposure, such as construction and agriculture, where, in fact, Anthropic finds the observed exposure is, indeed, almost nil. Comparing the observed exposure findings to projections of job growth from the U.S. Bureau of Labor Statistics, the Anthropic researchers found that there was a correlation between higher observed AI exposure and lower BLS job growth forecasts for those fields.

But we need to question the agriculture finding given that predictive AI and robotics are potentially quite disruptive to agriculture and these technologies are already making inroads into farming. It’s just that this tech is different from the large language model-based systems that Anthropic is focused on. That said, maybe it isn’t bad advice for your kids to apprentice to a plumber, become an electrician, or try their hand at farming. The Anthropic paper notes that about 30% of American workers are not covered by the study because “their tasks appeared too infrequently in our data to meet the minimum threshold. This group includes, for example, Cooks, Motorcycle Mechanics, Lifeguards, Bartenders, Dishwashers, and Dressing Room Attendants.”

Even in fields where the total potential exposure is high, such as those involving computers and math, where theoretical exposure is 94%, the actual number of tasks being automated today is far lower, in this case 33%. Office administration had the highest observed exposure at about 40%, against a total theoretical exposure of 90%. (Although it is important to note that these are average figures across broad categories. When it comes to more specific job titles, the observed exposure is a lot higher: 75% for computer programmers, 70% for customer service representatives, and 67% for data entry jobs and for medical record specialists.)

The big question now is: how fast will the gap between observed AI exposure and theoretical AI exposure close? According to the author the answer is that it will vary a lot between different professions. The idea that the same levels of automation that has hit software developers in the past six months is about to hit every other knowledge worker in the next 12 to 18 months seems off to the author. He thinks it is going to take substantially longer. The Anthropic paper notes that so far, there’s very little evidence of job losses, even in the fields where the observed AI exposure is greatest, such as software development, although they do highlight a study from Stanford University that showed there were some signs of a hiring slowdown among younger software programmers and IT professionals. (Still, even that study could not entirely disentangle that slowdown from the possible unwinding of overhiring during the pandemic years.)

McCrory and Massenkoff highlight a few of the reasons why observed AI automation may be lagging behind its potential. In some cases AI models are not yet up to the tasks involved, they write. But in many others, they note, AI “may be slow to diffuse due to legal constraints, specific software requirements, human verification steps, or other hurdles.”

The potential AI impact is also not uniform across the population: women are significantly overrepresented in AI exposed fields compared to men; exposed workers are more likely to be white or Asian, and they are also more likely to be highly educated and higher paid. Given that such groups are also often better able to organize politically, if we do start to see significant job losses among these workers, we may see a significant political backlash that could slow AI adoption. 

The Anthropic economists also note that economists’ track records when it comes to predicting occupational change is poor. 

In the end, the most honest answer to both questions—will I lose my job, and what should my kids study?—may be: I don’t know, and no one else does either. But it might not be a bad idea to learn something about plumbing.

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Asia is steadily reshaping the global economic landscape. The region is on a plausible trajectory to represent as much as 60 percent of Fortune 500 companies within the next decade and is already emerging as the world’s primary engine of trade.  Its large population, advanced-manufacturing depth, and high levels of digital adoption and public–private collaboration create fertile ground for experimentation. But these structural advantages do not fully explain Asia’s rising global influence. What truly differentiates the region is the way companies build on these conditions to create new business models—architectures that unlock asymmetric growth and, increasingly, incorporate AI into their design.

Over the past three to five years, six archetypes have repeatedly surfaced in the region across markets, sectors, and companies of various sizes. These models—which are broadly focused on harnessing trust, emotional resonance, and personalization, with AI as an accelerant—represent strategic choices made by leaders, not outcomes unique to Asia’s context and environment. Thus, global leaders in any market can apply these lessons.

The emergence of these novel business models is not accidental. Across markets, four structural dynamics created fertile ground for their development:  scale and speed, system-level collaboration, depth of digital services and regulation as a catalyst.

Across markets and sectors, six archetypes have helped spur growth for Asian companies of various sizes, resulting in CAGRs that far outpace the underlying market.

1. Emotion-first products: Turning affinity into recurring demand.  Emotion-first products are engineered from inception to create anticipation, identity, and community. Rather than treating emotion as a brand-building by-product, these companies treat it as a core economic driver.  Across Asia, companies have industrialized the mechanics of emotional engagement: scarcity, drops, fan rituals, collection loops, live cocreation, and rich narratives for their intellectual property.
2. Network-driven commerce: Scaling trust through creators and culture.  The second archetype turns trust—particularly trust in creators and communities—into a primary distribution channel. In Asia, this model has evolved far beyond influencer marketing to become a complete retail system where consumers have embraced shopping within chat, livestreams, and short video to normalize creator-led commerce.
3. Microsegments and microproducers: Personalization at industrial scale.  The microproduction archetype is about matching supply with demand at an extreme pace. Companies across Asia can deliver personalized or small-batch products at unit costs previously associated with mass production.
4. The knowledge economy: Using education to build trust and reduce customer-acquisition costs.  In Asia, some companies treat education not as corporate social responsibility or marketing but as a genuine point of difference to bolster the acquisition channel toward their core product offerings. They use free, high-quality knowledge to build trust, reduce customer-acquisition costs, and deepen engagement.
5. Conglomerates 3.0: Ecosystems connected by shared digital infrastructure.  Next-generation conglomerates are ecosystem-based organizations that move beyond pooling capital, common brands, and shared management expertise. Conglomerates 3.0 integrate multiple verticals through shared digital assets such as identity, payments, data, and loyalty to create a genuine incentive for customers to choose their products over competitors with singular or outdated offerings.
6. AI-native consumer platforms: Services built without human labor constraints.  Across education, entertainment, commerce, and customer service, Asia is leading the shift to AI-native consumer platforms—services delivered primarily or entirely by AI rather than by human labor.  

While these archetypes evolved in Asia, their many success stories suggest that they are transferable. Leaders in other parts of the world can draw five lessons from Asia’s successes.  Build trust through creators, community, and education.  Create products that balance scale with emotional resonance.  Shift to network-driven distribution.  Share capabilities and assets across the entire business.  And use AI and digital rails across all dimensions.

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Innovation increasingly depends on partnerships. As complexity and specialization rise and technologies such as AI reshape workflows and product portfolios, no single team or company has all the capabilities, tools, or authority needed to move ideas from prototype to scale. Organizations must “partner or die,” as one executive told the authors. But sharing the driver’s seat is difficult. The more that innovation relies on collaboration across groups and firms, the more initiatives are likely to stall—or worse, fail—because the partnerships meant to deliver them break down.

The authors’ study of firms that get innovation right finds that a particular type of leadership—what they call “bridging”—drives collaboration effectively across boundaries.  Bridgers have strong emotional and contextual intelligence, which enables them to build the trust, influence, and commitment across partners that are essential to move innovation forward.  That’s because bridgers perform three critical functions: They curate partners, translate across boundaries, and integrate partners’ disparate efforts.

The relationships that bridgers build through these activities are critical to getting partners to take risks and invest their time and effort beyond their core responsibilities. Specifically, bridgers are skilled at fostering the following: mutual trust, mutual influence and mutual commitment.

Curating partners.  Bridging begins with selecting and attracting the right partners—the stakeholders who will be needed throughout the innovation process. That includes individuals who will provide access to key capabilities as well as those from whom support or buy-in is needed.  Bridgers foster broad and diverse personal networks they can leverage. When they are in exploration mode, they tend to cast a wide net; when a particular initiative is well-defined, they target their outreach.

Translating among partners.  Bridgers recognize that partners differ in their priorities, strengths, and tolerance for risk. Differences along these dimensions often create misunderstandings and operational friction, whether in the form of misread cues about why one party continues to press an issue or in substantive divergences in timelines or goals. To avoid conflicts and address those that cannot be prevented, bridgers translate across differences to build common understanding.

Integrating disparate intentions and ways of working.  As bridgers build shared understanding across partners, they also address the practical challenge of getting them to collaborate effectively. They help define a shared intention, or north star, and they coordinate partners’ efforts so that projects can proceed. This work is ongoing throughout the partnership; there is no one-time fix.

How to Develop Bridgers.  Companies looking to scale innovation quickly need bridgers in roles throughout the organization, from senior executives tasked with overhauling innovation internally to midlevel managers serving on innovative projects as the interface for their function or business unit.  But it can be difficult to persuade people to take on these leadership roles.  To identify potential bridgers among your employees, begin by looking to the people who already work successfully at boundaries: those who assemble cross-functional or other cross-group teams, build robust networks with peers and senior stakeholders, and volunteer in diverse activities outside the company. With some reflection, you may realize you already know who they are.  To develop potential bridgers, place individuals in roles that require them to work across functions, business units, or geographies so that they gain experience in contexts with different operating models and power dynamics. Encourage zigzag career paths and role rotations as well as involvement in external communities (such as in industry associations, local entrepreneurship, or communities of practice). Give them stretch assignments that teach them to work across differences.  Once bridgers take on leadership positions, give them air cover and step in with support when needed.  Finally, give bridgers visibility. Not only do they deserve it, but doing so will encourage others to embrace innovation.

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Many businesses launch on the strength of an innovative product or service. But somewhere along the line, most successful companies become more focused on protecting the business they have than in creating new streams of growth that position the company for the future. To enjoy long-term success, businesses need to develop the capacity for systematic strategic innovation, just as they have built out their operational capabilities.  Strategic innovation is defined as the discipline that transforms creative discoveries into new platforms of business that bring significant value to the market and the organization.  Unfortunately, this is a capability that few companies have managed to build.

Based on their research the authors have found a set of eight common practices that, when combined, build a sustainable strategic innovation capability.  These eight are summed in three domains:  Set Direction and Establish Commitment, Build the Capabilities, and Manage the Dynamics of the Innovation System Over Time.

1. Adopt a common language for defining the innovation landscape. Establishing a framework and terminology to discuss innovation activities is critical because different kinds of innovation happen on different timelines and need to be managed differently.
2. Set the domains of innovation intent. Senior leadership must scope out and articulate the opportunity areas where the company will focus its innovation efforts. The scale of ambition has to be big, and the company needs to be creative about how it can influence those opportunity spaces.
3. Treat strategic innovation as a permanent function. Executives often try to boost innovation through special initiatives like a call for moonshot ideas or offering incentives to participate in an innovation contest or hackathon. This is like trying to create new growth platforms via suggestion box. To become an enduring organizational capability, strategic innovation needs to have two important characteristics. First, it must be treated as a full function supported by a comprehensive set of management practices. Those practices must then be designed to support one another.
4. Develop each domain into a portfolio of opportunities. Successful innovators spread their bets across several different domains of innovation intent simultaneously to hedge against risk. Within each domain, the strategic innovation team aims to identify and explore a range of opportunities. Operating at the domain level is crucially important for realizing the potential of strategic innovation, because it yields a portfolio of related opportunities rather than a pipeline of independent projects that are each subjected to a go/kill decision.
5. Develop the three organizational competencies of discovery, incubation, and acceleration.
6. Clearly define innovation roles. The work described above requires distinct skills, and companies will need to create career paths to both develop and retain new business creation talent. That means moving away from scenarios where new business opportunities are led by project champions whose careers will be sidelined if their venture fails, and designing career paths that allow people to rise to positions of influence in the strategic innovation function.
7. Proactively manage four dimensions of uncertainty. Mature companies are good at managing risk to mitigate the likelihood of an undesirable outcome. In the world of strategic innovation, however, it’s important to investigate uncertainties about technology, market structure and receptiveness, resourcing, and the organization itself in order to carve out new business territory.
8. Tune the innovation function. While financial pressures can dampen an organization’s appetite for investing in strategic innovation, adjusting budgets and activities rather than sidelining the function is critical. Unfortunately, innovation is often the first to go in challenging times: The portfolio of opportunities is discarded, and accumulated expertise is lost. Continual stops and starts prevent organizations from developing the necessary expertise and reaping the benefits of their investments. Instead of abandoning big bets, companies can adapt the size and pacing of a portfolio to what’s feasible in a more constrained environment.

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The headlines have been relentless. A war in Europe that refuses to end. A conflict reshaping the Middle East. Oil markets swinging on a single statement from Riyadh. Tariff announcements arriving before markets open, each one rewriting supply chain assumptions that took years to build. Political fragmentation spreading across democracies that once seemed settled. If you are a leader trying to think clearly right now, the noise is considerable. Strategic forecasting seems woefully inadequate.

So here is the question nobody in your last strategy meeting asked directly: how much of this should actually change what you do on Monday morning?  Not what you should read, or monitor, or discuss at the next offsite. What you should do. Differently. Because of the macro.

For most leaders, in most organizations, the honest answer is less than the volume of attention being paid to it would suggest. And the gap between how much macro turbulence consumes leadership attention and how much it actually warrants is one of the most expensive and least examined inefficiencies in organizational life today.

Forces that dominate news cycles, executive briefings and strategy offsite conversations. They are large, visible and genuinely consequential.  And for most people, almost entirely uncontrollable.  Here is the deeper paradox. While people compulsively monitor macro forces they cannot change, research reveals they simultaneously avoid the specific future knowledge that might actually require them to act.

A landmark study by Gerd Gigerenzer of the Max Planck Institute for Human Development, published in Psychological Review, surveyed more than 2,000 adults across Germany and Spain and found that 86 to 90 percent of people would not want to know about upcoming negative events in their lives. More striking still, 40 to 77 percent preferred to remain ignorant even of upcoming positive events. Only one percent of participants consistently wanted to know what the future held. Gigerenzer called this deliberate ignorance, and his framing is instructive: not wanting to know is not a failure of curiosity or intelligence. It is a widespread, stable, cross-cultural feature of how humans relate to uncertainty.

The reason, his research suggests, is not laziness. It is the anticipation of regret. People intuit that knowing creates obligation, that awareness of a coming difficulty demands a response, and that response carries its own costs and anxieties. Better, for most people, to remain in a state of open possibility than to have the future closed down into a known shape that requires action.

Gigerenzer invoked the Cassandra myth deliberately. In Greek mythology, Cassandra could see the future but was cursed so that no one believed her. His research suggests the modern version of the curse is self-imposed. We don’t want the prophecy. We would rather maintain the feeling of open possibility than face a known future that demands a response.

Read alongside what we know about worry, this creates a portrait of the human being that should unsettle every risk management professional in the room. We over-monitor the macro forces we cannot influence, while systematically avoiding the specific future information that might actually change our decisions. We worry expansively and prepare selectively. The result is an enormous expenditure of cognitive and emotional energy that produces neither better decisions nor better outcomes.

Worry, it turns out, is not a form of preparation. It is preparation’s counterfeit.

What Actually Works.  If the critique lands, the practical question becomes unavoidable: what does genuine preparation for macro disruption look like, and why do so few organizations achieve it?

The first answer is signal detection at the micro level — and the reason most organizations fail at it is not ignorance, it is hierarchy. The macro forces that will materially affect your organization almost always arrive as micro signals first. A supplier conversation that changes tone. A customer segment that starts behaving differently. A manager in a market you rarely watch closely who raises something uncomfortable in a town hall. These signals exist. They routinely fail to travel upward because the organizational structure does not reward surfacing anomalies before they become crises. People learn, often correctly, that raising uncertain bad news creates more problems for the messenger than staying quiet. The organizations that detect macro shifts early are not the ones with better geopolitical analysts. They are the ones where distributed judgment is genuinely safe.

The second is building decisions that are robust across scenarios rather than optimized for one. Most strategic decisions are made as if the present will persist with minor variations. The more honest discipline is to ask: what would need to be true about the future for this decision to be wrong? That question is organizationally threatening because it exposes the assumptions underneath a strategy that leadership may have spent considerable political capital defending. Which is precisely why it so rarely gets asked. Scenario robustness is not a planning technique. It is an act of institutional honesty that most organizations find structurally difficult.

The third is what might be called organizational nervous system health — the relational and cultural infrastructure through which macro signals become real responses. Trust between teams. Communication that does not slow down as it moves through layers. The psychological safety to raise bad news without delay. Leaders who treat uncertainty as information rather than threat. These factors determine how quickly an organization can reorient when the environment shifts. They are consistently underinvested in, not because leaders do not understand their importance, but because they are hard to measure, slow to build and invisible on a balance sheet. They also never appear on a risk register, which is itself part of the problem.

The Real Question.  The provocation at the center of this piece is not that macro forces do not matter. They do.  The provocation is that the way most individuals and organizations relate to those forces is almost perfectly calibrated to produce anxiety without producing resilience. We worry about what we cannot control, avoid specific knowledge that would require action, and invest in planning processes that create the feeling of preparedness without the substance of it.

The macro will always be beyond reach. The micro is where preparation either happens or it does not. The gap between those two levels is not a planning problem. It is a judgment problem, a culture problem and ultimately a leadership problem.  Better strategic plans will not close it. Better organizations might.

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During the 2008 financial crisis, Tom Hardin spent 18 months undercover (TipperX) for the FBI in the largest U.S. insider-trading investigation. He faced experienced professionals, coaxing them to reveal information they were actively trying to hide.  He wore a wire in coffee shops, offices, and conference rooms, often with his pulse so loud in his ears he could barely hear his own questions.

This experience taught him something most leaders misunderstand. People do not avoid telling the truth because they are dishonest. They avoid it because they are afraid of what the truth will cost them. Under pressure, even the most confident professionals retreat into performance. What comes out of their mouths is not deception in the traditional sense; it is self-protection.

The same dynamic appears in organizations every day. Early warnings get softened. Bad news arrives late. Teams present the safest possible version of reality because they do not know which truths will damage their standing. Leaders often misinterpret this as a communication failure. In reality, it is a safety failure.

In high-pressure environments, people instinctively calculate the cost of candor before they speak. If telling the full truth could threaten their credibility, standing, or future opportunities, they edit or they delay.

Hardin explores this dynamic in his book, Wired on Wall Street: The Rise and Fall of Tipper X, One of the FBI’s Most Prolific Informants, and from those high-stakes conversations, he distilled a practical communication model to help slow decisions, build psychological safety, and surface risks early—T.R.A.C.E:

T – Time the Silence.  Most leaders move too fast. They ask a question and quickly fill the pause. They respond before the other person has fully processed what they want to say.  Hardin learned that silence isn’t awkward—it’s diagnostic. When he stopped filling conversational gaps, people often stepped into the quiet with what they had been holding back. Silence gave them time to decide that honesty was worth the risk.  In one-on-ones or reviews, Hardin suggests you pause longer than feels comfortable. Count to five before responding. What follows the silence is often more honest than the first answer.

R – Reflect the Emotion.  People don’t open up when they feel interrogated. They open up when they feel understood.  In undercover conversations, acknowledging stress or pressure lowered defensiveness. When something felt normal, it became speakable.  Before solving the issue, reflect on what you’re hearing. “It sounds like this timeline is creating pressure,” or “That seems frustrating.” Emotional acknowledgment reduces perceived threat and builds trust.

A – Align Without Judgment.  Direct questions can unintentionally trigger self-protection.  “Why is this behind schedule?” can feel like an accusation. When people sense judgment, they retreat into safer answers.  Hardin found that subtle alignment invited more candor than pointed inquiry.  Replace accusatory framing with contextual alignment.

C – Cue Subtly.  Truth rarely responds well to force.  Rather than pushing harder, Hardin often used neutral observations to open guarded conversations. A simple remark sometimes elicited more disclosure than a direct question.  Offer observations instead of challenges. “I noticed this changed from last week.” Then stop talking. The space you create often determines whether someone steps forward.

E — Echo for Clarity.  The most important moment in any conversation comes after someone takes a risk and tells you something real.  Hardin observed that the first reaction determines whether candor compounds or shuts down. Tone and pacing matter more than leaders realize.  When you receive difficult information, respond steadily. Repeat key points back for clarity. Your composure teaches your team whether honesty is safe.

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The authro was recently asked by a woman entrepreneur in her collective how she had achieved such lasting success. Although she acknowledges that timing and luck played a role, she believes her hard work and strong desire to succeed were most influential.  After reflecting on her nearly 30 years of experience, during which she has closed over $1.6 billion in transactions, including $1.4 billion in commercial real estate and $280 million since founding her marketing agency during The Great Recession, she had a response to her question in the form of a 4 part framework.

Ambition plus joy.  Most successful entrepreneurs will share that starting and scaling a business is one of the most difficult and rewarding challenges they have faced, the author is no exceuption. It takes unmatched focus and ambition. What she has learned is that unchecked traditional ambition alone will burn you out. However, when it’s coupled with joy, it becomes a way to achieve long-term sustainable success. This integration is what she calls Joyful Ambition™, a new way to deploy ambition and still accomplish your goals without regret and burnout.

Integrity.  A client  she has worked with for over seven years once told me, “Thank you, Bianca. You always show up no matter what, and that’s one of the things I value most about working with you and your agency.”  Integrity is not only what you say and do for your clients and customers, but it’s also how you run your life and business. If you lack integrity in one area, you most likely lack it in other areas of your life. Integrity is foundational to lasting success. As entrepreneurs, it is our job to have a clear north star and build lasting trust in every interaction if we desire longevity and success.

Relationships.  Relationships.  Building and maintaining relationships is an invaluable skill because trust compounds. And the same holds true whether you’re closing your first sale or landing a billion-dollar deal: People do business with people they trust. A relationship has always been the keystone to every major opportunity he has had in business. Great entrepreneurs live by a people-first philosophy that includes their family, friends, team and clients.  During some of the most challenging times in business, her success was always sustained by fusing her ambition with joy, reinforcing it with integrity that built trust capital, and grounding it in relationships that endure and generate new opportunities.