Informed i’s Weekly Business Insights

For years, business leaders and corporate boards have viewed quantum computing (QC) as a threat—and for good reason: It has the potential to break today’s strongest encryptions. That moment, commonly known as Q-Day, will occur when quantum computers succeed in factoring exceptionally large numbers, undermining the math that public-key cryptography depends on.

Though business leaders are keeping Q-Day top of mind, they are viewing QC through a new lens—less a threat and more an opportunity. Many are spurring their companies to experiment with QC now so that they will be ready to deploy it at scale once quantum computers become mainstream, which could happen within the next five years.

The potential benefits for early adopters are considerable. McKinsey research suggests that QC could create multibillions of dollars in enterprise value in the coming decade—and that’s just for the industries the authors analyzed that are most likely to benefit. Providers of quantum computing have accelerated their engineering road maps and made algorithmic breakthroughs that suggest that scalable applications could arrive in a matter of years.

Unlike classical computers, which process information in a linear way, quantum computers leverage the principles of quantum mechanics to explore many potential solutions in parallel. While quantum computers themselves are extremely complex, they differ from classical computers in one fundamental way. Classical computers are built on units of information called bits, which can be represented by either a zero or a one. Quantum computers, on the other hand, are built on quantum bits, or qubits, which can represent any combination of zero, one, or both simultaneously. This form of nonlinear processing allows quantum computers to solve complex tasks far faster than even today’s most powerful supercomputers.

QC’s greatest strength lies in its ability to solve problems that overwhelm classical computers, such as advanced simulation and probabilistic modeling. These capabilities make QC attractive for applications such as drug discovery, material simulation, supply chain optimization, and financial modeling—all areas where early QC applications are gaining traction. Already today, some organizations claim that QC outperforms classical computing by a wide margin (what’s known as “quantum supremacy”) and that the technology has matured to the point where companies are deriving incremental value from it.

QC has yet to hit the mainstream, however, because of two key challenges: Qubits are fragile and prone to errors, and QC hardware is expensive to build and operate. These constraints mean that for most companies today, QC is best suited to a small number of high-value use cases, rather than as a replacement for classical computing. However, advances in software are quickly helping to address these limitations. Algorithms that mitigate and correct errors could soon enable even imperfect quantum computers to deliver high-impact results. This algorithmic leap means that raw quantum hardware could become less important, making large-scale QC use happen sooner than hardware road maps alone suggest.

CEOs don’t need to understand the intricacies of quantum computing to derive value from it. But they do need to know enough about the technology to understand where QC is headed and how it could affect their P&L. They also need to develop a clear view of the use cases that are relevant for their companies and partner with technology teams to map QC rollouts to measurable business results, such as cost savings or productivity gains.

Over the next ten years, it is expected that QC to evolve in two stages. In the first stage, limited use cases will develop in a hybrid approach with classical computing. During the second stage, so-called fault-tolerant quantum computers could unleash new scalable use cases that deliver significant value.

Three steps for QC success.  Many companies will want to ready themselves for the maturation of quantum computing, given its potential to create value. For forward-thinking companies, that preparation can begin now. The authors’ research shows that a strategic QC playbook balances prudence with risk-taking over three crucial steps.  Step one: Map exposure and opportunity.  Step two: Secure options on technology and talent.  And step three: Run targeted experiments during the readiness window.

At its core, the goal of education is to prepare individuals for employment and advancement. But as AI alters the nature of entry-level work, institutions can no longer assume students will gain practical experience after graduation. Increasingly, workforce readiness must be embedded directly into the educational experience itself.

Students themselves are signaling this need. More than half (56%) of graduates who feel unprepared for entry-level roles say they lacked job-specific skills, while 79% of Gen Z believe it’s important to have on-the-job learning experience during their post-secondary education. By leaning into seizing this opportunity to help close the emerging experience gap, institutions will not only educate students but ensure they are prepared for today’s workforce. Here’s how institutions can best accomplish this:

Embed experience directly into the curriculum.  Experiential learning must be built into the core of higher education, not treated as an add-on. That can take many forms, from immersive simulations and virtual or augmented reality tools that mirror real workplace scenarios to project-based learning that allows students to solve real business challenges as part of their coursework. As automation takes over more procedural and repetitive tasks, employers increasingly value skills such as judgment, adaptability, communication and problem-solving – capabilities best developed through hands-on experiences. 

Build deeper partnerships with employers.  Closer alignment with employers is critical to ensuring education keeps pace with workforce needs. Employers bring a real-time understanding of in-demand skills and evolving industry trends — insight that is invaluable for both educators and learners. This becomes especially important as AI accelerates how quickly workplace tools, workflows, and expectations evolve. Static degree programs alone cannot adapt quickly enough to keep pace with technological change without deeper employer collaboration.  For employers, these programs provide earlier access to emerging talent while helping ensure graduates enter the workforce with job-ready skills. For students entering AI-disrupted industries, these experiences are becoming even more valuable because they expose students to how professionals actually work alongside emerging technologies in real-world environments.

Redefine how outcomes are measured.  In many ways, AI is forcing higher education to confront a fundamental question: not simply whether students completed a program, but whether institutions truly prepared them for the realities of modern work.  Answering that question requires institutions to focus more closely on the outcomes that matter most — how well learners are prepared to enter and grow in the workforce. By tracking employment outcomes and career progression, institutions can gain clearer insight into their strengths and where gaps remain, creating a more informed path to continuously improve workforce readiness and close the experience gap. Ultimately, success is not only defined by what happens in the classroom, but by what happens after learners leave it.

AI is forcing a fundamental rethink of how workers gain experience, build confidence and transition into professional life. If entry-level work no longer functions as the training ground it once was, higher education has a critical role to play in helping fill the gap — but it cannot solve this challenge in isolation.  Preparing the next generation of workers must be a shared effort across educators, employers, and policymakers. That means policymakers expanding access to high-quality, workforce-aligned learning opportunities and employers investing more deeply in early-career development and partnerships with institutions.  The question is no longer whether AI will reshape the first rung of the career ladder. It already is. The real challenge is ensuring the next generation still has a way to climb.

A few months before he was awarded the Nobel Prize in economics in 2024, Daron Acemoglu published a paper that earned him few fans in Silicon Valley. Contrary to what Big Tech CEOs had been promising—an overhaul of all white-collar work—Acemoglu estimated that AI would give only a small boost to US productivity and would not obviate the need for human work. It’s okay at automating certain tasks, he wrote, but some jobs will be perfectly fine.

Two years later, Acemoglu’s measured take has not caught on. Chatter about an AI jobs apocalypse pops up everywhere from Senator Bernie Sanders’s rallies to conversations I overhear in line at the grocery store. Some previously skeptical economists have gotten more open to the idea that something seismic could be coming with AI.

On the one hand, the data is still on Acemoglu’s side; studies repeatedly find that AI is not affecting employment rates or layoffs. But the technology has advanced quite a bit since his cautious predictions. The author spoke with him to understand if any of the latest developments in AI have changed his thesis, and to find out what does worry him these days if not imminent AGI.

AI agents.  One of the biggest technical leaps in AI since Acemoglu’s paper has been agentic AI, or tools that can go beyond chatbots and operate on their own to complete the goal you give them. Because they can work independently rather than just answering questions, companies are increasingly pitching agents as a one-to-many replacement for human workers.  “I think that’s just a losing proposition,” Acemoglu says. He thinks agents are better thought of as tools to augment particular pieces of someone’s work than something malleable enough to handle a person’s whole job.  One reason has to do with all the various tasks that go into a job, something Acemoglu has been researching in his work on AI since 2018.  Whether or not agents will supercharge AI’s impact on jobs will come down to whether they can eventually handle the orchestration between tasks that humans do naturally.

The new hiring spree.  For years Big Tech has been offering staggering salaries to recruit AI researchers. But the authors asked Acemoglu about a different hiring spree he has noticed: AI companies are all building in-house economics teams.  Acemoglu has noticed colleagues getting snatched up for these roles too. “It makes sense,” he says: AI companies are well aware that public skepticism about AI, in large part due to job concerns, is growing. And they have strong incentives to shape the economic narrative around their technology (consider OpenAI’s latest proposal for a new era of industrial policy).  “What I hope we won’t get,” Acemoglu says, “is that they’re interested in economists just to further their viewpoints or further the hype.” That tension hangs over the emerging field of “AI economics”; it’s concerning that some of the most influential research about AI’s impact on work may increasingly come from the companies with the most to gain from favorable conclusions.

AI apps.  The author doesn’t think of AI as hard to use; most of us interact with it via chatbots that use plain language. But Acemoglu says we should consider how it compares with the sort of software that kicked off earlier tech transformations, like PowerPoint for slide decks and Word for documents.   “We have not seen the development of apps based on AI that have the same usability,” he says. Even if anyone can chat with an AI model, it tends to take a while for the average worker to get practical and productive use out of it. That’s part of the reason why AI has not yet shown any seismic impact on the job market or the economy. One of the key signals Acemoglu is watching, then, is the creation of apps that make AI easier to use. 

But he acknowledges that for a while, we’re going to see all sorts of conflicting evidence about AI.

Your CEO just resigned. Or was fired. Or died.  What now? Tomorrow you’re going to need someone in charge. If you don’t have a successor ready, you’ll probably decide to appoint an interim CEO—someone who can steer the ship until you find a permanent replacement.

That’s an understandable decision, especially if a sudden departure has forced your hand. But it can be costly. Boards often think of interim appointments as safe, limited, and temporary, but investors and analysts aren’t fond of them. Stock prices drop the moment the word “interim” appears in press releases, and things frequently go downhill from there: In the years following an interim CEO appointment, companies typically lose hundreds of millions of dollars, and those losses often lead to restructuring and workforce reductions.  Nevertheless, boards are increasingly making that choice.

Yet even though interim appointments often drain value and cut profitability, they can also serve as powerful tools for stabilization or transformation. The questions we need to understand is when are they most useful? And what separates success from failure?

Costs and Benefits.  Interim appointments are among the most disruptive forms of leadership transition. And the impact doesn’t end when the permanent CEO arrives. It lingers.  The author’s study found that interim periods are disruptive whether the CEO is meant to be a steady caretaker or a bold change agent. That’s because interim appointments tend to lead to the following events:  Strategic decisions stall.  The company’s best people leave or shut down.  Investors and analysts are generally unimpressed. And interim CEOs shift their focus to short-term performance.

The type of interim CEO that companies need varies with the situations they’re in. The authors have identified four main types. Finding each kind of executive has its own challenges and opportunities. Let’s explore them in more detail.  A) The fixer.  This is the kind of leader you need when a CEO is forced to step down while the company is struggling.  B)  The steward.  This type of leader is needed when a CEO is forced to step down while the company is performing solidly.  C)  The stabilizer.  This is the kind of leader you look for when a CEO departs voluntarily while the company is struggling.  And D) The caretaker.  This is the best type of leader to look for when a CEO departs while the company is performing well.

Four Key Questions.  Whether planned or unexpected, interim CEO appointments carry real risks of disruption. Strong succession planning helps prevent unnecessary interim appointments. But if an interim appointment is unavoidable, boards should use the opportunity strategically. You need to carefully consider four key questions.  What will be the interim CEO’s mandate?  Where will the interim CEO come from?  How long should the interim CEO stay?  And should an interim appointment be a tryout for the permanent job?

Interim Readiness.  Given the risks of appointing an interim CEO, it’s often wise to proceed directly with a permanent appointment. But that’s not always possible, and even if it is, interim appointments can be used as deliberate instruments for stabilization or transformation. To do that successfully, however, you need to build interim readiness into your governance alongside traditional succession planning. In practice, boards should develop internal candidates by giving real assignments, maintaining relationships with external prospects year-round, and constantly updating their view of what the next CEO must achieve.  The key is to take four strategic actions.  Assess transition readiness regularly.  Match interim situations to capabilities.  Prepare interim playbooks.  And mobilize communications and people.

The poet Walt Whitman famously wrote, “Do I contradict myself? / Very well then I contradict myself, / (I am large, I contain multitudes.).” He recognized that our minds are not monolithic but composed of multiple, interdependent parts that operate in a dynamic relationship. Just as our bodies function as complex living systems with many organs playing a role in keeping us healthy and adaptive, our minds are composed of conscious and unconscious parts that function in a dynamic relationship with one another.  

Insights from psychotherapy have profoundly influenced how we think about leadership and organizational culture. Emotional intelligence and psychological safety both began as clinical concepts before psychologist Daniel Goleman and Harvard professor Amy Edmondson, respectively, put them on the map as essential to leadership. The idea that the mind as comprising multiple parts with an integrative self at the center is not uncommon in management and leadership theory.  MIT Sloan’s Deborah Ancona and leadership expert and executive coach Dennis Perkins have shown that “ghosts” from past childhood experiences influence how executives lead.1 (Ancona’s book on “family ghosts” at work will be published next year.) London Business School professor Herminia Ibarra writes in her book Working Identity that “we are not one true self but many selves and that those identities exist not only in the past and present but also, and most importantly, in the future.”

There are many tools to help students and executives bring unconscious patterns and habits into conscious awareness, where they can take charge of their present behaviors and future development as leaders.

The Internal Family Systems Methodology.  Originally developed in the 1980s as a clinical therapy model by one of us (Richard), Internal Family Systems (IFS) offers leaders a simple framework for accessing and working with their inner parts to achieve greater functionality and flourishing, personally and professionally.  Six-step IFS process are:  

Our inner parts function as distinct subpersonalities, influencing how we think, feel, and act. One part may want to avoid conflict, another may want to push hard for results, and another may prefer to simply escape. Leaders often experience this as inner tugs-of-war. Recognizing that we all have these parts and that they often disagree is the first step toward deeper self-understanding in leadership.

Perhaps the most important principle behind IFS is the idea that all parts are trying to help us — even the ones whose behaviors we dislike. No part is inherently “bad.”  IFS suggests that these parts are trying to serve a purpose — whether protecting against perceived threats, fulfilling old survival vows, or motivating us through fear. The behavior may be unhelpful, but the underlying drive is not malicious. Based on decades of clinical experience, Richard has found that once these parts are truly heard, appreciated, and relieved of their burdens, they can completely transform. Inner critics can become wise advisers, workaholics can turn intowor reasonable motivators, and rageful parts can set healthy boundaries.

Leaders don’t have to (and should not) indulge in every impulse that arises from within, but they can learn to listen and respect their parts’ underlying positive intentions. A common adage in IFS is “All parts are welcome; all behaviors are not.” We must be able to draw firm boundaries around harmful behaviors while continuing to explore our underlying motivations.

For leaders, this insight is powerful. By shifting from judgment to curiosity about their own inner voices, they not only reduce inner conflict but also build the muscle to extend that same compassion and discernment outward — to colleagues, teams, and organizations.

Like Jung’s theory of mind, IFS contains the idea of a core organizing force in the psyche called the self. The self is like an orchestra conductor — a calm and centered presence guiding our parts to work together harmoniously and developing each one’s potential so that together they can express the best of who we are.  IFS describes self-led leadership as having eight recognizable qualities (known as the 8 C’s): compassion, curiosity, clarity, creativity, calmness, confidence, courage, and connectedness. When these qualities are present, it signals that a person is leading from the self, not from a reactive part. In high-pressure moments, a leader grounded in self can hold contradictory evidence or objectives, resist panic, and take decisive action rather than being directed by anxious or controlling parts.

A Guide to Using IFS in Leadership.  Understanding the three core IFS principles — that our minds comprise multiple parts, that no parts are bad, and that we can develop access to our wiser self to guide them — gives leaders a new perspective on their inner conflicts. It provides a starting point for learning from their parts and responding to challenges with steadier action.  To put these principles into practice, IFS practitioners guide individuals through a process to identify the parts of themselves that are active in each situation, approach them with curiosity and compassion, and help shift them into healthier roles. This process unfolds in two stages: becoming aware of the part and forming a new relationship with the part. 

Conveying wise words in a manner that they are heard, internalized, and put into practice is a challenge for all mentors. Mentors provide feedback, give encouragement, ask great questions, and engage in thought-provoking discussions. And sometimes they attempt the most anxiety-inducing activity: advice-giving.

Psychologists remind us that we all have authority hang-ups of varying severity. The protégé’s built-in resistance to advice can create a challenge in teaching lessons that increase competence and/or improve performance. And for advice-giving to work, you must be ready for your protégé to choose not to take it. It is what makes advice-giving different than simply giving a directive. Pay attention to the sequence of the following four steps; it is crucial to your success.

Step 1: Start with the “Why” of Advice Giving.  For advice-giving to work, you must be clear in your rationale. Ambiguity clouds the conversation and risks leaving your protégé more confused than enlightened. Being clear up front about the purpose of your advice can help focus your thoughts into laser-like counsel.

Step 2: Get Agreement on the Focus.  Make sure your protégé is as eager to improve as you are to see improvement. You may learn your protégé has already determined what to do and has little need for your advice. What do you do if you think there is something the protégé needs to learn but the protégé is unwilling? Many lessons get “taught” (but not learned) under this exact scenario. As Abraham Lincoln said, “A person convinced against his will is of the same opinion still.” Have patience and find a more fruitful teachable moment.

Step 3: Ask Permission to Give Advice.  This is the most important step. Your goal at this point is twofold: (1) to communicate advice without causing protégé resistance, and (2) to keep ownership of the challenge with your protégé. This does not mean asking, “May I have your permission to…?” Rather, you might say something like, “I have some ideas on how you might improve if that would be helpful to you.”  The essence of resistance is control. Few of us are thrilled at being told what to do. By keeping ownership with the protégé, you eliminate the perception of being controlled.

Step 4: State Advice in First Person Singular.  Phrases like “you ought to” quickly foster resistance. By keeping your advice in the first-person singular — “What I have found helpful” or “What has worked for me” — helps eliminate the shoulds and ought-tos. First person singular helps your protégé hear your advice unencumbered by defensiveness or resistance. Remember, the goal is not to convey your wisdom; it is the valuable outcome of your protégé’s improved performance or increased competence.

Giving advice is a bit like playing a pinball machine: you must push and pull the machine to get the ball to go in the preferred direction if you want to raise your score. However, if you push and pull too much, the pinball machine flashes “tilt,” and the game is over. Effective mentors recognize the challenge of “teaching to create change” and meet that challenge by coupling wisdom with encouraging sensitivity. They keep the ball in play as long as they can by the judicious application of pushes and pulls, nudges and bumps, increasing the ultimate score — their protégé’s.

Entrepreneurship doesn’t feel linear.  From the outside, business growth can look like a steady upward line. From the inside, it rarely feels that way. You are operating in an environment where many variables are constantly shifting. Market conditions change. Timelines move. Feedback evolves. What works one week may not work the next. As a result, your experience as a founder is not emotionally flat. There are moments where things feel aligned and clear. And moments where they don’t.  That fluctuation is not necessarily a sign that something is wrong. It is often a reflection of the uncertainty that comes with building something in real time.

Why the swings can feel so intense.  One reason these swings feel amplified is proximity. When you are building something, you are close to it. You care about it. You are responsible for it. You are thinking about it beyond standard working hours. So when things go well, it feels meaningful. And when things don’t, it can feel equally significant in the opposite direction.  This is especially true in earlier stages, where each decision, conversation or outcome can feel like it carries more weight. Over time, as systems, teams and processes develop, some of that intensity can become more manageable, but it does not disappear entirely.

1. The risk of reacting to extremes.  According to the author early on, he found himself reacting too strongly to both ends of the spectrum. A positive outcome felt like confirmation that everything was working perfectly. A setback felt like a signal that something was fundamentally off. Neither interpretation was fully accurate.  A single good day does not define long-term success. A single difficult day does not define long-term failure. The challenge is learning not to over-index on short-term signals.
2. Building stability within uncertainty.  What has helped me most over time is focusing less on how things feel day to day and more on how we operate consistently. That includes showing up even when clarity is limited, making decisions with the information available and continuing to move forward without waiting for perfect conditions.  This does not remove uncertainty, but it creates a level of internal stability that is not entirely dependent on daily outcomes.
3. Separating signal from noise.  Not every issue requires a major response. Not every positive result represents a lasting trend. Part of developing as a founder is learning to distinguish between what is meaningful and what is temporary. That often comes from repetition.  You start to recognize patterns. You develop a better sense of what actually impacts the business. You become less reactive to short-term fluctuations. This does not mean ignoring problems. It means responding proportionally.

Why perspective matters.  One of the most useful adjustments is zooming out. Day to day, things can feel inconsistent. Over longer periods of time, patterns become clearer. Progress often becomes more visible when viewed over months or years rather than days. What felt like volatility in the moment can look like steady progress in hindsight.

Practical ways to stay grounded.  While every situation is different, a few approaches can help create more stability: Avoid making major decisions based on a single day’s outcome. Give yourself time to assess situations with more context.  Maintain consistent routines. Even when business conditions fluctuate, your habits can stay steady.  Focus on controllable actions. Effort, preparation, and decision-making are always within your control, even when outcomes are not.  Document progress over time. Keeping track of key developments can provide a more accurate view than relying on memory alone.  And expect variability. Fluctuations are not exceptions. They are part of the process.

Entrepreneurship rarely feels smooth. It moves in waves. Some days will feel like progress is obvious. Others will feel unclear or uncertain. The goal is not to eliminate those swings.  It is to build the ability to operate through them without overreacting to either extreme. Because over time, that steadiness is what allows you to keep moving forward.  And in many cases, continuing to move forward is what creates the opportunity for everything else to follow.

How can you get in on the AI startup boom? At the Web Summit conference in Vancouver, two seasoned VCs, Salil Deshpande, general partner at Uncorrelated, and David Cohen, co-founder of Techstars, attempted to answer that question.  

Is the AI boom winding down? Not at all, the investors said. “It’s pretty early,” Cohen said. “What is it, 1 percent of the world that’s actually using these tools? That’s going to be much more mainstream.”  The way we use AI will change too, he said. “We’re going to shift from a chat-based understanding of AI to the actual implementation of intelligence more broadly. I think it changes everything over a very long road.”  That long road ahead makes it hard to guess which startups will succeed and which will fail, the VCs said. But there are some areas that seem ripe for growth where these heavy hitters are currently investing.

1. Infrastructure software.  The lower you go in the software stack, the better, the VCs agreed. Other than that, Deshpande said, he asks a series of questions. First, is your product built on a database? “If it’s not, then I think you’re good, because you’re probably doing something lower. You’re probably not application software, you’re probably doing something lower.”  The problem with application software is twofold, the investors said. For one thing, the underlying AI software changes so frequently that you’re in constant danger of being disrupted. And, the VCs said, you’re always in danger of competition from what they termed “a 15-year-old with a laptop.”
2. Software where you own the data.  If your product is built on a database, all is not necessarily lost, Deshpande said. “The next question is: Whom does the data belong to?” In some cases, he said, you may generate data but not own it. For example, if you drive a Tesla, your driving data belongs to Tesla, not you, he said. On the other hand, Salesforce doesn’t own users’ data, but it’s hard for those users to migrate their data out of Salesforce because of the insights Salesforce brings to that data. “So that’s worth something,” he said. “There are other types of data schemas that are much harder, and data that’s more valuable, more voluminous. So I think that’s probably the key.”  In today’s world, the VCs agreed, data is much more valuable than code. “If you’ve just got software, it’s probably going to be easy to compete with,” Cohen said.
3. The infrastructure that supports AI.  “AI is going to be a very good customer of infrastructure for the next 20 to 30 years,” Deshpande said. “So that’s racks and machines and chips and power and cooling. All of that feels like it’s going to be good.” Anything AI needs is going to be a good investment, he added.  Because software startups have been under attack from the giant AI companies, Deshpande said he’s been focusing on hardware instead: “I’ve made half a dozen hardware investments now.”

There’s one other thing, Cohen noted. Entrepreneurs willing to bootstrap or otherwise bypass the VC ecosystem have a huge opportunity to build successful companies on a smaller scale using existing AI.  “Most investors say the thin wrapper around AI that just solves a particular problem—those all get wiped out, and we’ve seen a lot of that,” Cohen said. “But if the context is that you’re an individual who understands this technology and wants to go to the dry cleaners around your city and automate them, you could probably do pretty well investing in that. For venture capital, that’s going to be less scalable. That’s not going to create a billion-dollar outcome.”