AI Insights
What AI Really Can Do Now: 6 Lessons for Harnessing Artificial Intelligence
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I’d be happy if by the time I retire, we have [artificial intelligence] systems that are as smart as a cat,” Yann LeCun, Meta‘s chief AI scientist, Turing Award winner and one of the founding fathers of deep learning, tells Newsweek as part of an ongoing series of conversations about the future of AI, “and that retirement is coming fast, by the way, so I don’t have much time.”
LeCun sees the extraordinary promise of AI on the horizon. But so far we haven’t seen this degree of success. While venture capital and corporate investment pours billions of dollars into AI dream factories promising revolutionary transformations—whether it’s curing cancer or finally taming the email inbox—a stark reality persists: Most artificial intelligence initiatives collapse under their own ambitions.
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The gulf between technological marvel and practical utility resembles a paradise island ringed by shipwrecks—the quest for supreme omniscience has left the tech landscape littered with sophisticated failures. In the pursuit of self-driving cars, Apple spent over $10 billion developing its autonomous car before abandoning the project entirely. GM burned close to $10 billion on its Cruise robotaxi unit before shutting it down in December 2024. Five years ago, Elon Musk said: “We’re headed toward a situation where AI is vastly smarter than humans and I think that time frame is less than five years from now.” But so far, we’re holding our own.
Against this backdrop of inflated expectations and deflating results, a more nuanced understanding has emerged from those like LeCun, who’ve spent decades wrestling with the actual mechanics of intelligent systems. To cut through the industry’s hype and identify what’s reliable, Newsweek has gathered a remarkable constellation of experts through its AI Impact interview series.
The urgency driving these conversations extends beyond the tech titans racing to build machine consciousness. Executives across all industries currently confront a complex calculus. What AI can actually accomplish today remains murky—pattern recognition and language processing reveal stunning breakthroughs, yet in practice, the limitations are glaring. More uncertain is if today’s astounding capabilities will continue to advance at such a mind-boggling pace. How much better will it get? And most uncertain of all: When will the AI revolution that changes everything actually arrive—is it coming in the 2030s, which OpenAI‘s Sam Altman predicts will be “wildly different from any time that’s come before”? Or is it already here? How do you invest wisely in a technology evolving faster than anyone can track, where the wrong bet means competitive extinction, yet the right approach remains maddeningly unclear?
AI promises to revolutionize how businesses operate—from automating back-office functions to optimizing supply chains and analyzing vast troves of data for strategic insights. Companies that master AI integration could gain insurmountable competitive advantages, while those that don’t risk obsolescence. However, the RAND Corporation found that more than 80 percent of AI projects fail—twice the rate of failure for information technology projects without AI. The comfortable option of caution has vanished; in a fast-changing landscape, the future demands decisions today.
From these wide-ranging dialogues, six essential lessons emerge from prognosticators and practitioners who have spent decades building, studying and deploying complex systems in the real world.
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Lesson 1: Humans Must Be in Control
Of all the dreams about artificial intelligence, none seduces Silicon Valley luminaries more completely than the vision of a human-less future where machines operate without oversight. Altman believes the technology he’s building will very soon do “95 percent of what marketers use agencies, strategists and creative professionals for today—easily, nearly instantly and at almost no cost be handled by the AI…. Images, videos, campaign ideas? No problem.” In a private meeting with lawmakers, Altman warns that “upwards of 70 percent of jobs could be eliminated by AI.”
These aren’t idle speculations. Enormous sums of money have been marshaled in this quest for human-free automation, yet results often fall short of promises. Robotics pioneer Rodney Brooks, former head of the MIT Artificial Intelligence Lab and a founder of iRobot, knows from decades of building real-world applications from frontier technologies, that to be widely adopted, even the most clever tools must leave room for humans. “People only accept new technologies when they don’t lose their sense of control,” he says in Newsweek‘s AI Impact interview series.
Brooks illustrates this principle by pointing to hospital delivery robots designed to transport dirty dishes and linens. He says he often sees these potentially labor-saving machines “turned off and pushed to the side” because medical staff, rushing through corridors doing life-saving work, encounter the robots blocking their path with no way to tell them to get out of the way. So after a while, the machines end up disabled and shunted aside.
The irony is that even while touting an automated future, the limits of AI often mean that
humans are very much in the loop. When Elon Musk showed off his humanoid robot Optimus at a press event in 2024, the robots were remote controlled by humans. Before Cruise suspended operations, its “driverless” vehicles required remote human assistance every four to five miles.
“A 17-year-old can learn to drive a car in about 20 hours, even less, sometimes, largely without causing any accident,” LeCun tells Newsweek. “We have millions of hours of training data of people driving cars around, and we still don’t have self-driving cars,” he says. “So that means, in terms of understanding the world, we’re missing something really, really big.”
Despite having invented much of the underlying technology behind today’s large language models, LeCun argues they are fundamentally insufficient for achieving the autonomous capabilities that drive much of Silicon Valley’s AI hype. “If the path that my colleagues and I are on at [Facebook AI Research] and NYU…if we can make this work within three to five years, we’ll have a much better paradigm for systems that are controllable in the sense that you can give them goals, and they will, you know, by construction, the only thing they can do is accomplish those goals.”
Paul Marotta/Getty for TechCrunch
Lesson 2: Augment, Don’t Automate
“Right now,” Stanford neuroscientist David Eagleman tells Newsweek of the most successful AI deployments, “it’s all about co-piloting.” For individuals, he explains, “we can synergize with it and speed things up enormously.” This partnership model consistently outperforms automation attempts across multiple industries. Even among AI’s biggest proponents, augmentation appears to be winning the day. Despite AI writing 30 percent of the company’s code, Microsoft CEO Satya Nadella continues hiring engineers to focus on distinctly human qualities like “bringing clarity” to ambiguous situations. Google CEO Sundar Pichai treats AI as “an accelerator” that can eliminate tedious tasks rather than replacing human workers entirely.
Leon Neal/Getty
LLMs excel at generating options but cannot determine which one’s better—a determination that requires a world model including human values, contextual understanding and experiential wisdom—a capability that no model currently possesses. The technology can produce impressive outputs, but it lacks grounding to assess their appropriateness, quality or real-world implications. The solution, Eagleman suggests, lies in designing “AI systems to check on other AI systems” and creating “translators to dumb things down for us so that we can understand what is going on.”
The economic evidence decisively supports this collaborative approach. A 2023 study by Stanford economist Erik Brynjolfsson has shown why augmentation works: AI assistance delivered 14 percent productivity increases to customer service workers and 34 percent improvement for novice workers when used as a support tool rather than replacement technology.
Klarna CEO Sebastian Siemiatkowski learned this after receiving widespread attention for declaring “AI can already do all of the jobs that we, as humans, do” while replacing 700 customer service contractors with AI systems in February of last year. But soon after, he discovered that Klarna customers were being handed off in one-third of cases to human agents when the AI couldn’t resolve complex issues. Within months, Siemiatkowski acknowledged the AI resulted in “lower quality” customer experiences, prompting a switch to an augmented approach. The company has hired humans again and now uses AI to handle routine queries while the human agents tackle the most complex customer cases.
In May, Siemiatkowski said that cutting labor costs had “been a too predominant evaluation factor” because “what you end up having is lower quality.” He added that “investing in the quality of the human support is the way of the future for us.”
Daron Acemoglu, an MIT economist who won the Nobel Prize in 2024, has spent decades studying technology’s impact on workers and economic growth. He now warns that Silicon Valley has been following “the wrong direction for AI. We’re using it too much for automation and not enough for providing expertise and information to workers.”
Lesson 3: Pick Tasks AI Is Good At
Large language models can write poetry, summarize research papers and generate code with startling fluency. What took trillions of tokens, billions of parameters, petabytes of data and acres of GPU servers to discover was that the written word contains far more predictable patterns than anyone expected. “It’s astonishing how well that generates language,” admits Brooks. “I don’t think most people 10 years ago could have believed that would work so well.” The fundamental surprise: “What LLMs have shown us is we can emulate language with that thoughtless part.”
That’s a reference to Nobel Prize-winning psychologist Daniel Kahneman‘s Thinking, Fast and Slow, which divides cognition into automatic System 1 responses and effortful System 2 deliberation. LLMs function like System 1 processors, excelling at language tasks like writing, editing and translation—but are likely to fail at things that require System 2 deliberation, like abstract reasoning, creative problem-solving, and adapting to novel situations. The key question becomes: What types of problems can be solved with System 1-like processing alone?
For decades, Kahneman and Gary Klein, a psychologist who researches naturalistic decision-making, had a running disagreement about whether human intuition could be trusted. Klein championed expert fast decision-makers like firefighters and nurses. Kahneman emphasized systematic biases making intuition unreliable. In 2009, their dispute produced surprising agreement in a joint paper they wrote, “Conditions for Intuitive Expertise.” Klein was partially vindicated: Practiced experts do develop reliable intuition skills, but only when two conditions are met: “an environment that is sufficiently regular as to be predictable” and “an opportunity to learn the regularities by prolonged practice and feedback.”
It’s easy to extend these requirements to AI: regular patterns plus large datasets. For example, in January 2025, the Mayo Clinic reported that a model it had built to analyze pathology slides to diagnose cancer was not performing as well as human doctors. Despite 1.2 million tissue samples from 490,000 cases, the model did not have enough examples of each of the thousands of possible disorders that pathologists identify. For rare conditions, “you’ll find 20 samples over 10 years,” one of the researchers told MIT Technology Review—insufficient for pattern recognition.
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“Even defining what regular is is not trivial,” notes Dana-Farber Cancer Institute CEO Ben Ebert to Newsweek. He points to the 2017 release of an algorithm that its developers claimed could detect pneumonia “at a level exceeding practicing radiologists.” But there was a problem, Ebert explains, “if you took a chest X-ray from a different hospital [than where it as developed], it completely didn’t work.” The problem is that it had learned patterns specific to that hospital, not general disease patterns. “The thing with AI is that because of how it was trained, it doesn’t realize that there’s a systematic bias.”
While there’s no precise way to measure regularity or to ever say how much data is enough, the Kahneman-Klein framework provides some clear directional guidelines. For instance, the head of a law firm would be able to recognize that AI would be better applied on drafting contracts— standardized formats with plenty of training examples—but struggle coming up with novel legal arguments. AI succeeds where human expertise can develop and struggles where even experienced professionals must rely on intuition alone.
Lesson 4: Use AI to Generate Possibilities, Not Answers
Asked for his assessment of LLMs’ reliability, Brooks is blunt: “They’re bullsh****** until we can ground them in reality.” His colorful language captures a truth: LLMs excel at persuasive speech “without regard to the truth,” fitting American philosopher Harry Frankfurt’s definition of bullsh**.
When LLMs hallucinate, the consequences can be spectacular. In 2023, investors spotted an incorrect claim in a Google Bard promotional video about the James Webb Space Telescope, wiping $100 billion from the company’s market value in a single trading session. That same year, attorney Steven Schwartz discovered the danger of relying on ChatGPT when a federal judge spotted six fake court cases Schwartz cited which the AI had invented, earning the lawyer national notoriety and $5,000 in court fines.
This imprecision stems from fundamental LLM architecture rather than fixable bugs. Researchers at Apple published a study in June, “The Illusion of Thinking,” which found that advanced reasoning models “face complete accuracy collapse beyond certain complexities,” even when provided with explicit problem-solving instructions. Yet this weakness becomes a strength when marketing teams need to generate dozens of concepts instantly or strategic planners want to discover unconsidered possibilities—and even in precision-critical fields like medicine.
When Eli Van Allen, the chief of the division of population sciences at Dana-Farber Cancer Institute, was a medical resident, he and classmates watched House episodes over lunch, competing to beat Hugh Laurie at identifying conditions. The trick wasn’t coming up with obvious diagnoses, but rather recalling faint possibilities.
Van Allen sees similar value in AI’s diagnostic brainstorming, which surfaces possibilities human doctors might miss. Where physicians remember “diagnosis 965” but overlook “diagnosis 9652,” AI can “pull down all 10,000 possibilities instantly” and help clinicians ensure “that tree to be the right tree and not prune too many limbs early on.”
Teaching machines to stop hallucinating is fighting against the grain—the real trick is to teach humans how to harness AI’s creativity, transforming its most dangerous flaw into a valuable feature.
David M. Benett/Dave Benett/Getty for Klarna
Lesson 5: Solve Human Problems
Venture capitalists have poured billions into AI companies convinced that extraordinary technology will inevitably find extraordinary uses. Their build-first mentality has created a graveyard of startups that died searching for problems to solve, dazzling users with technical sophistication while leaving them wondering what they’re supposed to do with it.
Apple’s Genmoji lets users create custom emojis from prompts like “a taco riding a skateboard.” But user reactions that started as “incredibly fun, creative and a great way to add more expression” soon became “the magic wore off quickly.” Suno AI can produce blues tracks that sound like they came straight from a Delta juke joint—but why? Google’s NotebookLM generates podcasts from any text filled with realistic human-like vocal quirks—authentic pauses, laughter, casual banter—that impress. Yet Cornell law professor Michael C. Dorf discovered after feeding it his writing that the results “sounded like a conversation among people who read my columns, lacked legal training, were reasonably smart and got about half of what I was saying but didn’t really follow a number of key points.” Does anyone need their meeting presentations turned into podcasts?
JOHN MACDOUGALL/AFP via Getty
Tech giants may have resources to indulge viral novelties like OpenAI’s Studio Ghibli image generators, but few outside Silicon Valley do. “The gritty people who run the multitrillion-dollar logistics of the world are not going to be spending billions of dollars based on glitziness. They’re going to be based on return on investment,” Brooks says. Lasting value comes from “understanding who your customers are and where their pain points are and how you are uniquely qualified to fix one of those things for them.” Netflix‘s recommendation engine helps people find something to watch when they’re faced with an overwhelming number of choices. GitHub’s Copilot reduces the tedium of writing boilerplate functions by autocompleting repetitive code patterns. However remarkable AI’s capabilities may be, successful deployments start with clearly defined human problems—not the reverse.
Lesson 6: Embrace Creative Partnership
When the pandemic isolated legendary production designer Rick Carter from his usual creative collaborators—directors such as Steven Spielberg, James Cameron and J.J. Abrams—he made a startling discovery while experimenting with AI video tools like Midjourney. “I can prompt it and even make mistakes, and it comes back with things that…I’m just going to call it an adjunct to what I am thinking,'” Carter tells Newsweek in the AI Impact interview series. Carter discovered something crucial: AI works best as a creative conversation partner rather than a creative generator. “It starts to interface with how I’m seeing things, and it stimulates me to move further in that direction.”
Michael Tran/FilmMagic
His experience echoes decades of collaborating on films like Jurassic Park, Avatar and Star Wars: The Rise of Skywalker. “You’re being prompted, as a production designer, by the director,” he explains. “And then there’s a dialogue. It’s back-and-forth.” Carter says of Spielberg: “Steven makes a point of not knowing what he’s going to do.” He once asked Spielberg why he did it this way and the director replied, “Well, if I know what I’m going to do, then it’s like having a job at Denny’s, and I’m just servicing an order.”
Academic research demonstrates how this back-and-forth creative dialogue amplifies human capabilities. German researchers Jennifer Haase and Sebastian Pokutta found that true “co-creativity” is “a fusion of human creativity with advanced AI capabilities, where both entities contribute significantly to a shared creative product.” In a study published in Nature, DeepMind and Oxford researchers found that AI-mathematician partnerships achieve “surprising results by leveraging the respective strengths” when AI serves as “a test bed for intuition”—quickly verifying which hunches about mathematical connections “may be worth pursuing and, if so, guidance as to how they may be related.” This collaborative approach led to breakthroughs including “one of the first connections between the algebraic and geometric structure of knots” and progress on a 40-year-old unsolved problem in representation theory. As Carter discovered, AI may lack its own creative heart, but it can amplify yours—if you engage it as a partner in dialogue rather than a generator of finished ideas.
iStock/Getty
Extending Human Judgment
When OpenAI chose the name ChatGPT, the acronym carried deeper significance. While officially “Generative Pre-trained Transformer,” the letters also evoke the concept of a “General Purpose Technology”—an economic term reserved for innovations capable of transforming entire civilizations. Writing, metalworking, electricity: These foundational advances reshaped the very structure of human society. Few doubt AI belongs in this pantheon, yet like those earlier revolutions, its ultimate applications remain tantalizingly unclear.
This uncertainty is not unprecedented. If you somehow managed to transport an electrical generator to the 1850s, few would have any idea what it is or what to do with it, even though at that point scientists had studied electricity for centuries. Electric lighting, motors, telecommunications—those were all still faint visions of a far distant future.
The gap between technological capability and practical deployment has always challenged human imagination, but the patterns emerging from our AI Impact conversations reveal the true promise of AI lies not in replacing human judgment but in extending it. Where automation dreams crash against real-world complexity, augmentation thrives by preserving what humans excel at while amplifying capabilities through machine partnership.
These principles—maintain human control, foster collaboration over replacement, target domains with sufficient regularity for learning, generate possibilities rather than answers, solve genuine human problems and encourage creative dialogue—cut through promotional fog to reveal a pragmatic yet transformative path forward, not toward a human-free utopia, but toward sophisticated new partnerships between mind and machine.
Illustration by Thomas Kuhlenbeck/Ikon Images
Correction 6/25/2025, 3:48 pm: Corrected Eli Van Allen’s title
AI Insights
Ascendion Wins Gold as the Artificial Intelligence Service Provider of the Year in 2025 Globee® Awards
- Awarded Gold for excellence in real-world AI implementation and measurable enterprise outcomes
- Recognized for agentic AI innovation through ASCENDION AAVA platform, accelerating software delivery and unlocking business value at scale
- Validated as a category leader in operationalizing AI across enterprise ecosystems—from generative and ethical AI to machine learning and NLP—delivering productivity, transparency, and transformation
BASKING RIDGE, N.J., July 7, 2025 /PRNewswire/ — Ascendion, a leader in AI-powered software engineering, has been awarded Gold as the Artificial Intelligence Service Provider of the Year in the 2025 Globee® Awards for Artificial Intelligence. This prestigious honor recognizes Ascendion’s bold leadership in delivering practical, enterprise-grade AI solutions that drive measurable business outcomes across industries.
Ascendion Wins Gold as the Artificial Intelligence Service Provider of the year in 2025 Globee® Awards
The Globee® Awards for Artificial Intelligence celebrate breakthrough achievements across the full spectrum of AI technologies including machine learning, natural language processing, generative AI, and ethical AI. Winners are recognized for setting new standards in transforming industries, enhancing user experiences, and solving real-world problems with artificial intelligence (AI).
“This recognition validates more than our AI capabilities. It confirms the bold vision that drives Ascendion,” said Karthik Krishnamurthy, Chief Executive Officer, Ascendion. “We’ve been engineering the future with AI long before it became a buzzword. Today, our clients aren’t chasing trends; they’re building what’s next with us. This award proves that when you combine powerful AI platforms, cutting-edge technology, and the relentless pursuit of meaningful outcomes, transformation moves from promise to fact. That’s Engineering to the Power of AI in action.”
Ascendion earned this recognition by driving real-world impact with its ASCENDION AAVA platform and agentic AI capabilities, transforming enterprise software development and delivery. This strategic approach enables clients to modernize engineering workflows, reduce technical debt, increase transparency, and rapidly turn AI innovation into scalable, market-ready solutions. Across industries like banking and financial services, healthcare and life sciences, retail and consumer goods, high-tech, and more, Ascendion is committed to helping clients move beyond experimentation to build AI-first systems that deliver real results.
“The 2025 winners reflect the innovation and forward-thinking mindset needed to lead in AI today,” said San Madan, President of the Globee® Awards. “With organizations across the globe engaging in data-driven evaluations, this recognition truly reflects broad industry endorsement and validation.”
About Ascendion
Ascendion is a leading provider of AI-powered software engineering solutions that help businesses innovate faster, smarter, and with greater impact. We partner with over 400 Global 2000 clients across North America, APAC, and Europe to tackle complex challenges in applied AI, cloud, data, experience design, and workforce transformation. Powered by +11,000 experts, a bold culture, and our proprietary Engineering to the Power of AI (EngineeringAI) approach, we deliver outcomes that build trust, unlock value, and accelerate growth. Headquartered in New Jersey, with 40+ global offices, Ascendion combines scale, agility, and ingenuity to engineer what’s next. Learn more at https://ascendion.com.
Engineering to the Power of AI™, AAVA™, EngineeringAI, Engineering to Elevate Life™, DataAI, ExperienceAI, Platform EngineeringAI, Product EngineeringAI, and Quality EngineeringAI are trademarks or service marks of Ascendion®. AAVA™ is pending registration. Unauthorized use is strictly prohibited.
About the Globee® Awards
The Globee® Awards present recognition in ten programs and competitions, including the Globee® Awards for Achievement, Globee® Awards for Artificial Intelligence, Globee® Awards for Business, Globee® Awards for Excellence, Globee® Awards for Cybersecurity, Globee® Awards for Disruptors, Globee® Awards for Impact. Globee® Awards for Innovation (also known as Golden Bridge Awards®), Globee® Awards for Leadership, and the Globee® Awards for Technology. To learn more about the Globee Awards, please visit the website: https://globeeawards.com.
SOURCE Ascendion
July 7, 2025
Today, with uncertainty a seemingly permanent condition, executives need to weave adaptability, resilience, and clarity into their operating plans. The best executives will implement strategies that don’t just sustain their businesses; they enable growth.
Cloud computing is the backbone of modern enterprise innovation, but with speed and scalability comes a growing storm of cyber threats. Cloud adoption continues to skyrocket. In fact, by 2028, cloud-native platforms will serve as the foundation for more than 95% of new digital initiatives. The traditional perimeter has all but disappeared. The result? A significantly expanded attack surface and a growing volume of threats targeting cloud workloads.
Studies tell us that 80% of security exposures now originate in the cloud, and threats targeting cloud environments have recently increased by 66%, underscoring the urgency for security strategies purpose-built for this environment. The reality for organizations is stark. Legacy tools designed for static, on-premises architectures can’t keep up. What’s needed is a new approach—one that’s intelligent, automated, and cloud-native. Enter AI-driven cloud detection and response (CDR).
Why legacy tools fall short
Traditional security approaches leave organizations exposed. Posture management has been the foundation of cloud security, helping teams identify misconfigurations and enforce compliance. Security risks, however, don’t stop at misconfigurations or vulnerabilities.
- Limited visibility: Cloud assets are ephemeral, spinning up and down in seconds. Legacy tools lack the telemetry and agility to provide continuous, real-time visibility.
- Operational silos: Disconnected cloud and SOC operations create blind spots and slow incident response.
- Manual burden: Analysts are drowning in alerts. Manual triage can’t scale with the velocity and complexity of cloud-native threats.
- Delayed response: In today’s landscape, every second counts. 60% of organizations take longer than four days to resolve cloud security issues.
The AI-powered CDR advantage
AI-powered CDR solves these challenges by combining the speed of automation with the intelligence of machine learning—offering CISOs a modern, proactive defense. Organizations need more than static posture security. They need real-time prevention.
Real-time threat prevention detection: AI engines analyze vast volumes of telemetry in real time—logs, flow data, behavior analytics. The full context this provides enables the detection and prevention of threats as they unfold. Organizations with AI-enhanced detection reduced breach lifecycle times by more than 100 days.
Unified security operations: CDR solutions bridge the gap between cloud and SOC teams by centralizing detection and response across environments, which eliminates redundant tooling and fosters collaboration, both essential when dealing with fast-moving incidents.
Context-rich insights: Modern CDR solutions deliver actionable insights enriched with context—identifying not just the issue, but why the issue matters. It empowers teams to prioritize effectively, slashing false positives and accelerating triage.
Intelligent automation: From context enrichment to auto-containment of compromised workloads, AI-enabled automation reduces the manual load on analysts and improves response rates.
The path forward
Organizations face unprecedented pressure to secure fast-changing cloud environments without slowing innovation. Relying on outdated security stacks is no longer viable. Cortex Cloud CDR from Palo Alto Networks delivers the speed, context, and intelligence required to defend against the evolving threat landscape. With over 10,000 detectors and 2,600+ machine learning models, Cortex Cloud CDR identifies and prevents high-risk threats with precision.
It’s time to shift from reactive defense to proactive protection. AI-driven CDR isn’t just another tool—it’s the cornerstone of modern cloud security strategy. And for CISOs, it’s the shield your organization needs to stay resilient in the face of tomorrow’s threats.
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