If youve worried that AI might take your job, deprive you of your livelihood, or maybe even replace your role in society, it probably feels good to see that the latest AI tools fail spectacularly. If AI recommends glue as a pizza topping, then youre safe for another day.
But the fact remains that AI already has definite advantages over even the most skilled humans, and knowing where these advantages ariseand where they dontwill be key to adapting to the AI-infused workforce.
AI will often not be as effective as a human doing the same job. It wont always know more or be more accurate. And it definitely wont always be fairer or more reliable. But it may still be used whenever it has an advantage over humans in one of four dimensions: speed, scale, scope and sophistication. Understanding these dimensions is the key to understanding AI-human replacement.
Speed
First, speed. There are tasks that humans are perfectly good at but are not nearly as fast as AI. One example is restoring or upscaling images: taking pixelated, noisy or blurry images and making a crisper and higher-resolution version. Humans are good at this; given the right digital tools and enough time, they can fill in fine details. But they are too slow to efficiently process large images or videos.
AI models can do the job blazingly fast, a capability with important industrial applications. AI-based software is used to enhance satellite and remote sensing data, to compress video files, to make video games run better with cheaper hardware and less energy, to help robots make the right movements, and to model turbulence to help build better internal combustion engines.
Real-time performance matters in these cases, and the speed of AI is necessary to enable them.
Scale
The second dimension of AIs advantage over humans is scale. AI will increasingly be used in tasks that humans can do well in one place at a time, but that AI can do in millions of places simultaneously. A familiar example is ad targeting and personalization. Human marketers can collect data and predict what types of people will respond to certain advertisements. This capability is important commercially; advertising is a trillion-dollar market globally.
AI models can do this for every single product, TV show, website, and internet user. This is how the modern ad-tech industry works. Real-time bidding markets price the display ads that appear alongside the websites you visit, and advertisers use AI models to decide when they want to pay that pricethousands of times per second.
Scope
Next, scope. AI can be advantageous when it does more things than any one person could, even when a human might do better at any one of those tasks. Generative AI systems such as ChatGPT can engage in conversation on any topic, write an essay espousing any position, create poetry in any style and language, write computer code in any programming language, and more. These models may not be superior to skilled humans at any one of these things, but no single human could outperform top-tier generative models across them all.
Its the combination of these competencies that generates value. Employers often struggle to find people with talents in disciplines such as software development and data science who also have strong prior knowledge of the employers domain. Organizations are likely to continue to rely on human specialists to write the best code and the best persuasive text, but they will increasingly be satisfied with AI when they just need a passable version of either.
Sophistication
Finally, sophistication. AIs can consider more factors in their decisions than humans can, and this can endow them with superhuman performance on specialized tasks. Computers have long been used to keep track of a multiplicity of factors that compound and interact in ways more complex than a human could trace. The 1990s chess-playing computer systems such as Deep Blue succeeded by thinking a dozen or more moves ahead.
Modern AI systems use a radically different approach: Deep learning systems built from many-layered neural networks take account of complex interactionsoften many billionsamong many factors. Neural networks now power the best chess-playing models and most other AI systems.
Chess is not the only domain where eschewing conventional rules and formal logic in favor of highly sophisticated and inscrutable systems has generated progress. The stunning advance of AlphaFold2, the AI model of structural biology whose creators Demis Hassabis and John Jumper were recognized with the Nobel Prize in chemistry in 2024, is another example.
This breakthrough replaced traditional physics-based systems for predicting how sequences of amino acids would fold into three-dimensional shapes with a 93 million-parameter model, even though it doesnt account for physical laws. That lack of real-world grounding is not desirable: No one likes the enigmatic nature of these AI systems, and scientists are eager to understand better how they work.
But the sophistication of AI is providing value to scientists, and its use across scientific fields has grown exponentially in recent years.
Context matters
Those are the four dimensions where AI can excel over humans. Accuracy still matters. You wouldnt want to use an AI that makes graphics look glitchy or targets ads randomlyyet accuracy isnt the differentiator. The AI doesnt need superhuman accuracy. Its enough for AI to be merely good and fast, or adequate and scalable. Increasing scope often comes with an accuracy penalty, because AI can generalize poorly to truly novel tasks. The 4 Ss are sometimes at odds. With a given amount of computing power, you generally have to trade off scale for sophistication.
Even more interestingly, when an AI takes over a human task, the task can change. Sometimes the AI is just doing things differently. Other times, AI starts doing different things. These changes bring new opportunities and new risks.
For example, high-frequency trading isnt just computers trading stocks faster; its a fundamentally different kind of trading that enables entirely new strategies, tactics, and associated risks. Likewise, AI hs developed more sophisticated strategies for the games of chess and Go. And the scale of AI chatbots has changed the nature of propaganda by allowing artificial voices to overwhelm human speech.
It is this phase shift, when changes in degree may transform into changes in kind, where AIs impacts to society are likely to be most keenly felt. All of this points to the places that AI can have a positive impact. When a system has a bottleneck related to speed, scale, scope, or sophistication, or when one of these factors poses a real barrier to being able to accomplish a goal, it makes sense to think about how AI could help.
Equally, when speed, scale, scope, and sophistication are not primary barriers, it makes less sense to use AI. This is why AI auto-suggest features for short communications such as text messages can feel so annoying. They offer little speed advantage and no benefit from sophistication, while sacrificing the sincerity of human communication.
Many deployments of customer service chatbots also fail this test, which may explain their unpopularity. Companies invest in them because of their scalability, and yet the bots often become a barrier to support rather than a speedy or sophisticated problem-solver.
Where the advantage lies
Keep this in mind when you encounter a new application for AI or consider AI as a replacement for, or an augmentation to, a human process. Looking for bottlenecks in speed, scale, scope, and sophistication provides a framework for understanding where AI provides value, and equally where the unique capabilities of the human species give us an enduring advantage.
Bruce Schneier is an adjunct lecturer in public policy at the Harvard Kennedy School.
Nathan Sanders is an affiliate at the Berkman Klein Center for Internet & Society at Harvard University.
This article is republished from The Conversation under a Creative Commons license. Read the original article.
Is America in a Second Gilded Age? Evan Osnos thinks so. In this episode of Most Innovative Companies, Osnos unpacks how extreme wealth, corporate influence, and political inequality are transforming American life. If youve ever wondered how the 1% really operate, this is the deep dive you need.
Cheap or free access to AI models keeps improving, with Google the latest firm to make its newest models available to all users, not just paying ones. But that access comes with one cost: the environment.
In a new study, German researchers tested 14 large language models (LLMs) of various sizes from leading developers such as Meta, Alibaba, and others. Each model answered 1,000 difficult academic questions spanning topics from world history to advanced mathematics. The tests ran on a powerful, energy-intensive NVIDIA A100 GPU, using a specialized framework to precisely measure electricity consumption per answer. This data was then converted into carbon dioxide equivalent emissions, providing a clear comparison of each models environmental impact.
The researchers found that many LLMs are far more powerful than needed for everyday queries. Smaller, less energy-hungry models can answer many factual questions just as well. The carbon and water footprints of a single prompt vary dramatically depending on model size and task type. Prompts requiring reasoning, which force models to think aloud, are especially polluting because they generate many more tokens.
One model, Cogito, topped the accuracy tableanswering nearly 85% of questions correctlybut produced three times more emissions than similar-sized models, highlighting a trade-off rarely visible to AI developers or users. (Cogito did not respond to a request for comment.) Do we really need a 400-billion parameter GPT model to answer when World War II was, for example, says Maximilian Dauner, a researcher at Hochschule München University of Applied Sciences and one of the studys authors.
The results underscored the balance between accuracy and emissions. The least-polluting model tested, Qwen 7B, answered just one in three questions correctly but emitted only 27.7 grams of carbon dioxide equivalent. In contrast, Deepseeks R1 70B reasoning model answered nearly eight in 10 questions correctlywhile producing more than 70 times the emissions for the same workload.
The type of question also affects environmental impact. Algebra or philosophy prompts produced emissions up to six times higher than what a high school student would generate getting homework help.
Companies should be more transparent about the real emissions and water consumptions from prompts, says Dauner. But at the same time, users ought to be more awareand more judiciousabout their AI use.
For two decades, conventional startup wisdom followed a simple mantra: Build one killer feature, win a devoted audience, and expand later.
Startup luminaries like Paul Graham and Sam Altman championed this approach. They argued it was better to start small, focus narrowly, and earn the right to grow. Many of todays most iconic companies followed this path, launching with a single tool that eventually evolved into a suite.
But what if the problem youre solving doesnt fit neatly into one feature? What if starting small just means setting yourself up to rebuild everything later?
In an increasingly complex world, customers no longer have patience for point solutions or disconnected workflows. They expect products to understand how their systems work and to match that reality with integrated, end-to-end experiences. Thats why a new model is emerging.
The rise of the compound startup
The compound startup, a term popularized by Rippling CEO Parker Conrad, describes a company that builds multiple, deeply integrated products from day one. In a January interview with Y Combinator CEO Garry Tan, Conrad explained that the goal of a compound startup is to address systems of problems, not just isolated pain points.
It is a model built for how people and businesses actually operate. Most business functions dont exist in a vacuum. In Ripplings case, payroll connects to benefits, onboarding, compliance, IT provisioning, and more. Customers dont want to piece together tools to manage each of those functions. They want a system that works together out of the box.
At april, we didnt just stitch together a few tax tools. We built an entire suite of products from the ground up. Filing, forecasting, planning, optimizationall designed to work in sync, and all tailored to distinct taxpayer segments like investors, small business owners, gig workers, and everyday banking customers.
We chose to build in tax, one of the most complex, fragmented, and regulated categories in fintech, because the problem demanded a compound solution. Tax laws shift constantly. Each state and jurisdiction operates differently. We could have licensed a white-label provider and shipped faster. Instead, we built our own tax engine, became the first new nationally licensed e-file provider in more than 15 years, and now operate a full-stack platform with fewer external dependencies. That decision has given us speed, adaptability, and product depth our partners cant find elsewhere.
Why compound startups make more sense today
The shift toward compound startups isnt just philosophical. Its practical.
Todays challenges rarely sit in one lane. Managing personal finances touches tax, payroll, planning, and compliance. Running a business involves HR, inventory, scheduling, payments, and reportingall at once.
Point solutions force users to become their own system integrators. They juggle multiple tools, manage disconnected data, and learn mismatched interfaces. Compound startups flip that script. They build coherence into the product architecture itself and unlock several key advantages:
Unified data: Integrated platforms break down silos and allow smarter decision making across use cases. At april, data moves with consent across workflows, powering real-time tax insights, planning simulations, and filing automation.
Shared UX patterns: A consistent interface builds user trust and reduces friction. Most april users complete their return in under 23 minutesa far cry from the 13-hour average reported by the IRS.
Durable switching costs: When workflows span multiple integrated tools, the platform becomes stickier and more valuable as a whole.
Platform-wide network effects: When more users adopt more of the suite, value compounds across use cases.
Compound startups dont just solve tasks. They solve workflows. And that makes them more durable, more useful, and more differentiated in crowded markets.
The long-term payoff
Of course, this approach comes with tradeoffs. Building multiple products in parallel strains focus and burns capital faster. It forces earlier decisions around architecture, compliance, and team structure. Its not the right move for every startup.
But for founders tackling systems-level problems, the risk of starting too small is greater. You cant increment your way to coherence. Weve seen the payoff at april. Our compound architecture has allowed us to respond faster, deliver richer experiences, and scale without compromise.
The future is compound
The startup playbook is evolving because the problems were solving have evolved. Systems are messier. Users expect more. Point solutions cant keep up.
Founders shouldnt be afraid to build big from the start. The world doesnt need more single-purpose tools. It needs products that actually solve the full problem. The future isnt just compound. Its integrated, full-stack, and built to scale from day one.
Ben Borodach is cofounder and CEO at april.
Something powerful is unfolding in womens sports, and its being driven by the fans. Theyre not asking for more flashy campaigns or superficial endorsements. Theyre calling for something deeper: genuine, athlete-led engagement that reflects the values they believe in. In an era of constant noise, what theyre truly seeking is trustin the athletes, in the brands, and in the stories being told.
This latest U.S. Womens Sports Report from Parity dives deep into the fandom, perception, and commercial landscape of womens sports and reveals one central truth: Trust is the defining currency of this movement.
The trust factor is rising fast
For years, professional women athletes have carried the torch of authenticity. Theyve spoken out on social issues, built communities online, and connected with fans through their personal stories and values. Today, that authenticity is paying dividendsnot just in follower counts, but in consumer trust.
Our latest data shows that 68% of all U.S. sports fansnot just womens sports fanssay they trust professional women athletes. That number jumps to 74% among men who watch womens sports, and 84% among daily or weekly viewers. Perhaps most surprising: Even among American sports fans who say they never watch womens sports, trust is surging. A whopping 58% of these never-watchers trust women athletes, up six points from last year.
This matters. Because in a fragmented media environment where consumer attention is scarce, trust is what cuts through the noise. Trust makes fans more likely to pay attention to your message. Trust makes them more likely to buy.
When a woman athlete promotes a product, fans believe she genuinely supports it. Thats not just a feel-good narrativeits a performance indicator for any brand trying to build equity in 2025.
Fans want brands that get it
Sponsorship in womens sports isnt just about slapping a logo on a jersey or airing a pre-roll ad. According to our findings, fans are looking for brands to show up in ways that matterto be part of the story, not a sales pitch.
What does that look like in practice?
It means partnering directly with athletes and empowering them to tell stories. It means doing your homework to find the right athlete match. It means investing in content that feels real, not overly produced. It means prioritizing causes that athletes and fans care about and using your platform to support them.
In other words, it means moving beyond transactional sponsorship toward transformational partnership.
And yes, it also means backing up your brand values with measurable action. In an era where fans are increasingly savvy, performative allyship doesnt cut it.
The fan base is surging and shifting
Beyond trust, the 2025 report confirms an evolution in fandom that many in the industry have felt coming.
Viewership is climbing, but the real story is whos tuning in. Younger audiences, multicultural fans, and even self-described casual viewers are engaging more than ever. And theyre doing more than watchingtheyre buying the merch, sharing highlights and content, and showing up in person.
Fandom is also becoming more localized and loyal. As new teams continue to debut and leagues expand, regional pride is taking hold. For brands, this opens the door to connect not just at scale, but meaningfully within communitiesthrough the athletes and teams their fans care about most.
The competitive landscape is getting smarter
In a maturing market, not all brands are moving at the same pace. Leaders in apparel, health and beauty, and food and beverage are already raising the bar. Theyre treating womens sports not as a side project but as a core brand pillar. Theyre allocating real dollars, innovating around athlete collaborations, and tracking impact in real-time. Other categories that womens sports fans are most interested in seeing step up: Travel among women watchers, and technology among men who watch.
Lets be clear: This has never been just about doing the right thing. Its about smart, strategic business. Brands that commit early to womens sports stand to win the heartsand walletsof fans who are paying close attention to whos showing up.
But with rising consumer expectations, the margin for error is slim. If youre not showing up authentically, fans will notice. And theyll move on.
What this means for brands
So what do fans really want from brands in womens sports?
They want trustearned through consistent, credible athlete partnerships.
They want authenticityreal stories from real athletes, not ad speak.
They want actionsupport that drives visibility, investment, and change.
They want presencebrands that show up locally, passionately, and with purpose.
And they want commitmentnot just one campaign, but a long-term vision.
At Parity, we work with hundreds of professional women athletes, and we see this every day. When brands show up with respect, integrity, and shared purpose, the impact is exponential. Fans take notice. Athletes engage more deeply. And everyone wins.
The moment is here
If your brand isnt already investing in womens sports, the window is still open. The fan base is here. The athletes are ready. The trust is high.
The question is: Will you meet the moment?
Because in 2025, trust is not just a value. Its a strategic foundation. And in womens sports, it might just be your competitive edge.
Leela Srinivasan is CEO of Parity.
In partnership with Omnicom, Jeff Beer sits down with Patty Morris, the head of brand at State Farm; Erwin Dito, the VP of global brand leadership at McDonalds; and Jae Goodman, the founder and CEO of Superconnector Studios, to discuss why effectiveness and efficiency are not the same thing in the advertising world.
Jeff Beer speaks with Giles Hedger, the global consumer planning director at Diageo; Rachel Rix, the chief growth officer at Ketchum; and James Kirkham, the founder and CEO at Iconic, about how AI and algorithms are making culture vanilla.
At Cannes, Fast Companys Jeff Beer sits down with Meghan Signalness (global head of media, marketing planning & operations and agency leadership at Philips), Rei Inamoto (founding partner at I&CO), and Erin Lanuti (chief innovation officer at Omnicom Public Relations Group) for a conversation on how marketing and product teams can work better together.
Caregiving is the invisible cornerstone of our economyand its in crisis. Recent data from Pivotal, an organization founded by Melinda French Gates to advance social progress, reveals staggering statistics:
67% of caregivers face personal financial strain due to caregiving responsibilities, often accruing credit card debt.
82% of voters agree that policymakers should prioritize investments to ease caregiving for families.
65% of caregivers report that caregiving benefits would influence their job decisions.
Unpaid caregiversthose who leave paid work or drastically reduce their hours to care for loved onesoften shoulder immense responsibilities with little to no institutional support. Recent data suggests that 44.58 million caregivers in the U.S. perform the equivalent of an estimated $873.5 billion worth of labor each year. With that much money at stake, this is far more than a personal issue; its a structural challenge impacting our economy, workforce, and well-being.
At Catapult Design, we see caregiving as more than a policy challenge; its an opportunity for design innovation. By centering on caregivers lived experiences, we can design inclusive solutions that ease their burdens and restore dignity to this vital, yet often-invisible labor force.
Unpaid care work, including childcare, elder care, and household management, contributes an estimated $10 trillion annually to the global economy. However, that staggering number isnt reflected in GDP calculations. Many caregivers, predominantly women, often give up paid employment, career advancement, and financial independence, creating cascading impacts on their mental and physical healthparticularly when caregiving responsibilities include supporting individuals with significant health challenges.
This isnt just a policy issue. Its a systemic challenge with far-reaching implications for economic stability, workforce resilience, and social equity.
Caregivers need human-centered solutions
At Catapult Design, we believe caregiving requires not only policy reform, but also innovative, systemic solutions. Human-centered design (HCD) offers a way to reimagine caregiving by centering the real needs of caregivers.
HCD is a collaborative, empathetic approach to problem-solving. It prioritizes listening to and learning from the people most impactedin this case, caregivers. By understanding their lived experiences, we can create practical, inclusive, and scalable solutions that not only address immediate challenges, but also support long-term equity and dignity.
In a recent project, Catapult Design addressed two issues caregiver often faceburnout and financial instability. Through deep engagement and co-creation, we identified solutions ranging from improved operational tools to peer-support networks. Small changes like these had a significant impact, reducing stress and increasing retention. The findings of this project underscore a key principle: Caregivers must be active participants in designing a world that works for them. Their insights are essential to creating systems and tools that reflect their realities and aspirations.
3 ways to address the caregiving crisis
For employers, policymakers, and community leaders, recognizing and supporting caregivers is both an ethical and economic imperative. Insights from our work at Catapult Design highlight three ways HCD can address the caregiving crisis:
Make caregiving visible. In our projects, weve seen how platforms that share caregivers stories can help elevate their contributions and shift societal perceptions. Amplifying these voices encourages recognition and investment in support systems.
Reduce barriers. Our work has shown that even small interventionslike improved scheduling tools or accessible financial support systemscan reduce stress and make caregiving more manageable.
Foster collaboration. By involving caregivers directly in design processes, we ensure that solutions are tailored to their real needs and have community buy-in, which leads to more sustainable and impactful outcomes.
Moving Forward
Caregiving isnt just a personal challengeits a societal one, deeply tied to economic stability and equity. By focusing on the real needs of caregivers, HCD can help create innovative, inclusive, and dignified solutions that alleviate burdens and recognize caregiving as a critical component of our economy and society.
Were actively working on this issue and are eager to collaborate with those who share our vision.
Angela Hariche is CEO of Catapult Design.
Every day we wake up to new problems. Climate change. Hate crimes. Genocide. Terrorism. Assassinations. Corruption. A new war. An endless war. Government break downs, shutdowns and letdowns. The world produces a lot of bad news, around the clock.
I am in the purpose business. I go to work every day wanting to make the world a better place. So I live in the tension between what is happening and what needs to happen. And, over the years, what this business has taught me is that every time the world gets worse, the need for purpose gets stronger.
Ask yourself, will the world need more or less climate action tomorrow than it needed yesterday? What about five years from nowwill we need a more urgent climate response than today or less? My guess is more. And its not just climate. Ask the same question about every other issue you care about. Will gun violence get worse or better as we loosen gun safety laws? Will income inequality increase or decrease?
An increasing demand for purpose
Things will get worse, and the need, the demand for better will increase. And, as your Econ 101 professor surely explained, necessity drives demand, and demand drives supply. So, as the situation gets worse, the demand for purpose increases and the supply of purpose is likely to increase along with it.
Think about it like this. In the market for goods and services, consumers of all ages are more likely to support brands with purposebuilt on sustainability, fair prices, good business practices, and positive contributions to society. People are willing to pay more for these products and services, and their willingness to engage with these brands over others increases each year. And so, more brands will continue to invest in purpose because there is increasing demand for it in the market.
In the labor market, the same holds true. With each passing year, the labor force expresses stronger desire to work for companies that respect their individuality, support their community, act as good stewards of the environment, pay fair wages, provide good benefits, and make them proud to tell their friends and families about. This isnt likely to change any time soon.
And, finally, in the market for public goods and servicesthe market for things we want our government to buy and do for usAmerica is getting the civics lesson we all wish we had in 10th grade. Thanks to DOGE, we are learning that our government isnt full of bureaucrats draining our coffers. Its actually full of brilliant scientists, hard-working park rangers, nuclear security professionals, dedicated public health experts, and literally millions of others doing things we take for granted. And its true, most Americans do take these things for granted and rarely think about thembut that doesnt mean we dont want them. We do. I actually think we want more of these things than we realize, and between now and 2026, we will find out exactly how much more.
An increased demand for a market response
Customers, employees, and citizens around the world are increasingly demanding that brands make better products, with better methods that deliver better outcomes for everyone involved in the process. Businesses would do well to recognize this demand for what it isthe manifestation of the invisible hand of the market expressing itself to force a market response. The response the market is looking for isnt for companies to run away from the necessity of the momentits to step up to it. To meet the moment with all the power and might the corporate world loves to preach about.
Smart businesses and investors shouldnt be asking themselves whether purpose is dead. The real question is, how do we radically scale purpose so it can actually meet the demand of our times?
Drew Train is chief executive officer of OBERLAND.
