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Navigating AI: The AI revolution is colliding with reality.

May 20, 2025

This week’s AI Trailblazers newsletter dives into the tension shaping today’s AI landscape - from Klarna and Duolingo’s AI-first strategies to rising deepfake threats and signs that reasoning models may be nearing their limits. These aren’t hype stories, they’re about trust, speed, and the cost of progress in an industry racing ahead.

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Key Topics We’re Tackling:
➤ Moving AI from pilot to scale, what actually works
➤ The Future of Search in the AI era
➤ Embedding AI: from marketing to product to ops
➤ Building AI-first cultures in legacy systems
➤ How AI Agents are redefining workflows
➤ Turning ethical AI from principles into practice
➤ Where marketing, media, and tech converge to win

Our past summits have featured senior leaders, both as speakers and guests, from Adobe, Amazon, Axios, Barclays, Bloomberg, Cisco, Citibank, Comcast Ventures, Diageo, Insight Partners, MasterCard, Mondelez International, Morgan Stanley, Nextdoor, PepsiCo, Publicis Groupe, Salesforce, Skadden, The New York Times, The Wall Street Journal, Uber, Unilever, the United Nations, among others.

AI First, Regret Second? Klarna and Duolingo Feel the Heat

As companies race to embrace artificial intelligence, some early adopters are facing unexpected backlash. Fast Company reports that Klarna and Duolingo, two high-profile brands that leaned heavily into “AI-first” strategies, are now dealing with the consequences—from customer service concerns to viral public criticism. While both remain committed to using AI, their experiences reveal the tension between corporate cost-cutting ambitions and consumers’ desire for authentic, human-centered experiences.

  • Klarna Walks Back AI-Only Strategy
    Once a vocal champion of AI-first operations, Klarna is reversing course and hiring again. CEO Sebastian Siemiatkowski acknowledged that prioritizing cost over quality led to diminished customer service. Now, the company aims to blend AI with a stronger human presence to improve support and restore trust.

  • Duolingo Faces Social Media Backlash
    Duolingo’s recent pivot to an AI-first model has sparked intense criticism online, especially on TikTok. Users have flooded the brand’s content with complaints about job losses and the diminishing human touch in education. Some have even deleted the app in protest, saying language learning should remain people-powered.

  • Public Pushback Reveals Consumer Skepticism of AI
    Despite company reassurances that AI is a tool, not a replacement, many users feel disconnected and concerned about the growing automation. Duolingo insists that human experts still guide the content, but the message hasn't resonated with its passionate user base. The uproar highlights a growing divide between corporate enthusiasm and customer expectations.

  • AI Excitement Meets Human Reluctance
    While executives see AI as a cost-saving solution, studies show many workers, especially Gen Z, feel it devalues their education and job prospects. Harvard research suggests people resist technology that feels like it reduces their control. The backlash at Klarna and Duolingo underscores the need for companies to balance innovation with empathy and transparency.

AI Trailblazer Takeaways: Klarna and Duolingo’s pivot to AI-driven strategies sparked public backlash, revealing a key fault line in today’s tech transformation: customers still crave human connection. While both companies championed automation as a path to efficiency, users pushed back—citing degraded service, job losses, and a loss of trust. The lesson? AI may unlock operational gains, but without transparency, empathy, and a human-centered touch, it risks alienating the very people it’s meant to serve. For innovation to stick, it must feel like an upgrade not a trade-off.

When AI Lies, Only Proof Survives

As AI-driven deepfake attacks grow more sophisticated and widespread, traditional cybersecurity measures are no longer enough to protect against impersonation threats. From voice phishing to synthetic identities in job interviews, bad actors are exploiting trust gaps in virtual communication platforms like Zoom and Teams. The Hacker News argues that a new approach is urgently needed—one that prioritizes verified identity and device integrity over guesswork.

  • AI-Powered Deepfake Attacks Are Surging
    Cybercriminals are increasingly using AI tools to impersonate trusted individuals in real-time, fueling a dramatic rise in social engineering attacks. Voice phishing, or “vishing,” spiked by 442% in 2024, and deepfakes are being used to infiltrate organizations, including via fake job interviews. These evolving threats are making traditional trust assumptions dangerously outdated.

  • Why Deepfake Threats Are Harder to Stop
    Three key factors are accelerating the deepfake threat: AI tools make impersonation cheap and scalable, virtual collaboration platforms assume trust, and current defenses rely too heavily on probability. Even advanced detection tools struggle to keep up with the realism of modern deepfakes. The core problem is that current systems can't deterministically prove identity or device integrity in real time.

  • Prevention Requires Verified Trust, Not Guesswork
    To stop deepfake attacks before they begin, organizations must shift from detection to prevention. This involves using cryptographic identity verification, device compliance checks, and visual trust indicators. These strategies remove the burden from users and prevent unauthorized access to high-risk environments like executive meetings and financial transactions.

  • RealityCheck Offers Real-Time Deepfake Defense
    Beyond Identity’s RealityCheck tool helps close the AI impersonation gap by integrating provable identity and device integrity into platforms like Zoom and Microsoft Teams. It displays visible identity badges backed by cryptographic verification and continuous risk assessments. This makes it nearly impossible for threat actors to fake their way into sensitive calls or chats.

AI Trailblazer Takeaways: AI-powered impersonation attacks are exploding, outpacing traditional cybersecurity defenses with chilling ease. From fake job interviews to voice phishing on Zoom, bad actors are exploiting the trust we place in faces and voices online. The real problem? Today’s systems rely on probabilistic detection rather than provable identity. As deepfakes grow more convincing, security must move from reactive to proactive—rooted in cryptographic identity, verified devices, and real-time visual trust signals.

AI’s Brainpower Boom May Be Hitting a Wall

As reasoning-focused AI models like OpenAI’s o3 achieve impressive results in math and programming tasks, a new analysis from nonprofit research group Epoch AI warns that their rapid progress may soon stall. The TechCrunch report suggests that the industry’s current strategy—scaling up compute-intensive reinforcement learning—faces both technical and economic limitations. With performance gains likely to plateau by 2026, the findings raise critical questions about the long-term scalability and sustainability of today’s most advanced AI models.

  • Reasoning AI Models May Hit a Wall Soon
    A new analysis by nonprofit research group Epoch AI suggests that the rapid improvements seen in reasoning-focused AI models could slow down within the next year. While models like OpenAI’s o3 have recently excelled in areas like math and programming, sustaining that momentum may prove difficult. This finding signals potential limits to further gains using current training methods.

  • Compute-Heavy Training Boosted Progress—But Only So Far
    Reasoning models use reinforcement learning after initial training to refine their problem-solving abilities. OpenAI dramatically increased the compute used in training o3—reportedly ten times more than its predecessor. However, Epoch warns that there are diminishing returns and hard caps on how much more compute can realistically be applied.

  • Scaling May Soon Plateau, Despite Massive Investment
    According to Epoch analyst Josh You, reinforcement learning performance gains are currently growing 10x every 3–5 months, but will likely align with overall AI training progress by 2026. That convergence could signal a slowdown in the breathtaking pace of reasoning model improvements. This is a concern for companies that have poured resources into these models expecting exponential returns.

  • Costs, Complexity, and Hallucinations Cloud the Future
    Beyond computing limitations, reasoning models face other roadblocks—namely high research overhead and operational costs. They’re also prone to hallucinations, sometimes more than traditional models, raising quality and reliability issues. As optimism gives way to caution, the industry may need new breakthroughs to sustain progress.

AI Trailblazer Takeaways: The runaway success of reasoning-focused models like OpenAI’s o3 has been powered not just by smarter algorithms, but by brute-force scaling of compute. That strategy, however, is running into real-world limits—technical, economic, and ethical. As performance plateaus loom and hallucination rates remain stubbornly high, we’re entering an inflection point where more GPUs won’t guarantee better minds. The implication is profound: The next leap in AI may require not more power, but more paradigm shifts. Just as deep learning once redefined what was possible, we may need fundamentally new methods—less about reinforcement, more about robustness; less about training scale, more about architectural elegance. In short, the future of AI might depend not on how hard we push current models, but how boldly we reinvent them.

Quote of the Week

“Artificial intelligence is the future, but we must ensure it is a future that we want.” 

- Tim Cook, CEO of Apple

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