This Storyboard - which we call our "stain" chart - shows you at a glance how strong or weak a given narrative is right now relative to its history.

For each narrative or "semantic signature" listed on the left of the chart, we have a series of blue dots on the right, each of which represents a specific weekly density or volume of that narrative. reading from within the date range that we are covering. The red arrow is the most recent reading, so it's just like the "YOU ARE HERE" spot on a map. The x-axis scale shows the range of index values.  If a dot is at 100, that means that story is 100% more present in media than usual. If it’s at 0, it means it’s at its normal level.

The light blue shaded box covers the middle 50% of readings across the date range, so you can see quickly if the current reading is typical (inside the blue box), depressed (left of the blue box), or elevated (to the right of the blue box).

If you hover over a specific blue dot, you will see the specific date and measurement that the dot represents.

The Pulse

Deepfakes Dominate Headlines as AI Spending Accelerates and Productivity Narratives Mature

Deepfake Concerns Reach Record Levels Amid High-Profile Incidents

On May 5, 2026, as millions tuned in for Met Gala coverage, AI-generated images of celebrities flooded X and Instagram with such convincing quality that many viewers had no idea they were fake. Perscient's semantic signature tracking the density of language arguing that deepfakes on social media represent a significant problem rose by 436 points in the past month alone, reaching a current value of 533—more than five times above its long-term average.

This represents one of the largest single-month increases observed across all our semantic signatures. As Forbes noted, the same AI tools fabricating glamorous Met Gala outfits are being deployed for financial scams, election interference, and identity fraud. When platforms fail to label synthetic content and AI search tools authenticate fakes, ordinary users bear the consequences through eroded trust, manipulated public opinion, and real-world financial losses.

Cybersecurity firm DeepStrike estimates an increase from roughly 500,000 online deepfakes in 2023 to approximately 8 million in 2025, with annual growth nearing 900%. Europol has projected that 90% of online content may be generated synthetically by 2026.

Voice cloning has emerged as a particularly concerning vector. According to recent research, voice cloning has crossed what experts call the "indistinguishable threshold"—a few seconds of audio now suffice to generate a convincing clone complete with natural intonation, rhythm, emphasis, emotion, pauses, and breathing noise. This has enabled a new wave of impersonation scams. Social media users have warned that human accuracy for spotting deepfake voices can drop below 30% in real scenarios.

Ahead of the 2026 midterm elections, U.S. Senator Mark R. Warner urged leading social media firms, generative AI platforms, and media editing software providers to take action against maliciously manipulated media.

Legislative responses are emerging at the state level. The Missouri House passed a bill aimed at curbing minors' access to social media, banning the distribution of deepfakes, and setting new requirements for social media platforms. Under this legislation, it would be a felony to share or threaten to share an AI-generated or other digital depiction of someone to harass, threaten, or harm them.

Our semantic signature tracking the density of language arguing that AI-generated news is rising remains at a highly elevated level at 468, though it declined by 11 points this month. The combination of these two signatures—both well above average—suggests growing concern about synthetic content across multiple media formats. As one commentator noted: we are moving from a world where we try to spot the fake from the genuine to one where we have to assume that content may be fake unless there is a reason to trust it.

Corporate AI Investment Skepticism Moderates as Spending Commitments Accelerate

Perscient's semantic signature tracking the density of language asserting that businesses increasingly doubt large AI spending declined by 53 points, falling from 87 to 34. While still above average, this moderation suggests that corporate caution may be easing as major technology firms double down on AI infrastructure with substantial commitments.

Following recent earnings calls, both Evercore and Bank of America placed 2027 capital expenditure estimates in excess of $1 trillion, with 2026 estimates rising to between $800 and $900 billion. As one analyst summarized after reviewing hyperscaler earnings: Google Cloud's backlog doubled to $460 billion, AWS spent $43.2 billion in a single quarter, and Azure grew by 40%.

The biggest technology firms are on track to spend $700 billion on their AI ambitions this year, double their 2025 spending, according to Goldman Sachs. That figure could swell to over $1 trillion next year. Worldwide spending on AI is forecast to total $2.52 trillion in 2026, a 44% increase year-over-year, according to Gartner, which noted that because AI is in the "Trough of Disillusionment" throughout 2026, it will most often be sold to enterprises by incumbent software providers rather than bought as part of new moonshot projects.

Our semantic signature tracking language asserting that AI powers sustained market gains rose by 11 points to -8, approaching its long-term average. Tech CEOs are projecting confidence about their artificial intelligence investments as evidence of monetization, such as ramping cloud revenue, flows through to the latest earnings reports.

Yet the picture remains nuanced. A National Bureau of Economic Research study published in February 2026 found that despite 90% of firms reporting no current impact of AI on workplace productivity, executives projected that AI would increase productivity by 1.4% and output by 0.8% in coming years. As one prominent AI researcher observed, a significant portion of this spending may be driven by competitive fear rather than demonstrated returns—nobody wants to be the company that did not invest in AI when everyone else did.

Our semantic signature tracking the density of language asserting that promised AI efficiency improvements have not occurred declined by 9 points this month to 26, remaining above average but moderating.

Productivity and Transformation Narratives Show Divergent Trajectories

The productivity evidence emerging from AI adoption contrasts with the more speculative transformation narratives in other domains. Perscient's semantic signature tracking language connecting AI to efficiency improvements and universal basic income rose by 5 points to 35, remaining stronger than average and continuing to strengthen.

The evidence base for productivity claims is growing more concrete. According to research published in the Journal of Labor Economics, industries with higher exposure to generative AI from 2017 to 2024 experienced greater productivity, employment, and wage growth, with a 10% productivity increase, 3.9% job growth, and 4.8% wage growth per standard deviation of AI exposure. The Stanford AI Index 2026 documented measured results: +14% productivity in customer support (+34% for novices), +26% more completed developer tasks, and +4% short-run EU firm-level labor productivity.

Survey data reinforces these findings. Overall, 88% of respondents said that AI has had an impact on increasing annual revenue in some or all parts of their business. Nearly a third reported increases greater than 10%, with another 33% reporting gains between 5% and 10%.

Our semantic signature tracking language asserting that AI will fundamentally change healthcare delivery rose by 9 points this month to -17. Though it remains below its long-term average, this uptick aligns with high-profile developments. Profluent and Eli Lilly announced a landmark partnership valued at up to $2.25 billion, focusing on advancing next-generation gene editing technologies using artificial intelligence to design and optimize therapeutic proteins at scale. As industry observers noted, this represents Lilly's third major AI deal in the past two months, following partnerships with NVIDIA and Insilico.

With over 1,000 AI-powered tools already FDA-cleared, the discussion is shifting from AI's potential to its measurable impact on efficiency, care coordination, and patient experience.

In contrast, our semantic signature tracking language asserting that AI will fundamentally change scientific research declined by 15 points to -19, and the signature tracking claims about AI transforming education fell by 3 points to -33. Both remain below their long-term averages, suggesting that transformational narratives are concentrating around economic and healthcare applications rather than broader societal domains.

According to PwC's 2026 AI Performance study, just 20% of organizations capture 74% of all AI-driven economic value. This concentration may explain why productivity narratives tied to business outcomes are strengthening while broader transformation narratives lag.

Research from manufacturing already shows what some call an "AI J-curve," in which initial adoption slows productivity before delivering stronger long-run gains. As analysts have observed, this pattern is likely to repeat across services.

Pulse is your AI analyst built on Perscient technology, summarizing the major changes and evolving narratives across our Storyboard signatures, and synthesizing that analysis with illustrative news articles and high-impact social media posts.