Aaron Mok

AI agents are pitched as digital coworkers that can work alongside teams. But new research suggests companies are struggling to make that vision a reality.

A new Deloitte survey of more than 500 U.S. business and IT leaders found a wide gap between companies’ ambitions for AI agents and their ability to actually use them at scale. Nearly three-quarters of leaders said they expect half of their businesses to be rebuilt or designed around AI agents within the next four years. However, even though 42% said their organizations have tested or deployed agents, just 15% have scaled multi-agent systems across the business.

The bottlenecks boil down to the architecture surrounding the agents:

• Just 21% of leaders said their business processes are prepared for agentic AI, and only one in five said their organizations are ready to redesign processes for agents.
• Instead, many companies are slapping agents onto existing ways of working in hopes of getting faster returns.
• Leaders also pointed to inaccessible data, difficulty governing agents, and costly integrations as major barriers to scaling.

“Limited, layered-on approaches may create the sense of getting ahead with quick wins, but in reality, they may not be enough,” Laura Shact, Deloitte’s US Technology, Media and Telecommunications AI growth leader, said in the study.

A separate study from Google and MIT Technology Review drills into another one of the biggest obstacles to using agents: data. More than half of the 300 global data and technology executives surveyed said legacy data systems are preventing them from scaling AI agents. On average, companies currently give AI access to just 45% of their enterprise data, which could limit its potential for efficiency gains.

The challenge goes beyond simply opening up more databases. Companies said their data is scattered across disconnected systems, locked away in unstructured formats like emails and PDFs, unavailable in real time, or missing the business context agents need to understand what the information actually means. Those shortcomings become more consequential when agents are expected to make decisions and take actions instead of simply answering questions.

Different levels of data access can compromise the quality of agentic workflows. Among organizations that give AI access to more than 70% of their enterprise data, every respondent characterized their agents’ outputs and decisions as either mostly or consistently accurate. And of organizations giving AI access to 30% or less of their data, just 22% said they trust the accuracy and relevance of their agents’ decisions.

"The so-called 'modern' data stacks that are glued together with disjointed parts were not built for the agentic era," Andi Gutmans, Google Cloud’s vice president and general manager of Data Cloud, said in the study.

Our Deeper View

The bottlenecks point to a bigger problem: Most companies weren't built for autonomous software. Getting agents to work reliably at scale will require more than deploying better models. Companies will need to break down data silos, redesign workflows around what agents can actually do, put guardrails in place for when they act out of line, and train workers to use and oversee them. The technology may be moving fast, but the harder work will be getting the rest of the business ready for it.

AI may be causing the job market to splinter.

As companies adopt AI, UK employers are increasingly prioritizing experienced workers with AI skills, raising the bar for early-career professionals, according to new data from Indeed Hiring Lab. The research finds that graduate job postings in the UK have fallen to their lowest share since 2020, even as AI-exposed occupations like software development, IT, and engineering outperform much of the broader labor market.

"The recent rise in software development hiring has focused on senior and AI-specialist roles, suggesting that employers are prioritizing experience and AI fluency over volume hiring of junior talent," Jack Kennedy, senior economist at Indeed Hiring Lab, told The Deep View. "That makes the skills mix more important than ever."

Overall hiring remains subdued, with U.K. job postings 11% below where they stood at the beginning of 2026. But the slowdown is reversed when AI skills enter the equation.

While hiring remains weak across many white-collar occupations, job postings that explicitly reference AI continue to surge, according to Indeed. Job postings for HR, management, marketing, and finance that mention AI are growing even as overall postings in those fields decline.

"We're seeing the relationship between AI exposure and hiring demand has flipped, with occupations most exposed to AI now seeing stronger hiring growth on average," Kennedy said. "This is true not just for software development, but also other AI-exposed occupations."

The data suggests employers are looking to hire for experience and AI fluency. As AI increasingly handles routine coding tasks, Kennedy said companies are placing greater emphasis on prompt engineering, code review, and quality assurance alongside traditional technical expertise.

"For jobseekers, particularly those in the early stages of their career, investing in that skills combination will be the most effective way to adapt to a market that is being rapidly reshaped," Kennedy said. "Demonstrating genuine AI competency alongside strong technical, creative and problem-solving skills is increasingly what separates candidates who progress from those who don't."

Our Deeper View

Indeed's findings complicate the narrative that AI is coming for jobs. Since the introduction of generative AI, economists and business leaders have speculated that AI will automate the tasks of white-collar workers, rendering many roles obsolete. Software engineers, in particular, have gotten the most heat. While studies do suggest coding roles are highly exposed to automation, that doesn't necessarily mean they're at risk of disappearing. Instead, AI is changing the work itself. For developers, that means spending less time writing code and more time reviewing, polishing, and validating AI-generated outputs. The same dynamic is playing out in fields like marketing and advertising, where professionals increasingly use their expertise to refine AI-generated work rather than create it from scratch. The shift suggests AI literacy is becoming increasingly important for employment. That’s especially true for college graduates entering a labor market where entry-level opportunities appear to be shrinking. Still, it raises questions about the quality of the work itself. Editing AI-generated outputs may not be as satisfying as creating something original, and whether workers will embrace that tradeoff remains an open question.

For chronically online youth, AI has evolved beyond a tool for homework and creative exploration. Increasingly, young people are turning to chatbots for emotional support.

A new report from Hopelab and the Center for Digital Thriving found that teenagers are often turning to chatbots to discuss emotional topics when they feel unable or unwilling to rely on the people around them for companionship.

The researchers interviewed 30 teenagers and young adults ages 14 to 22, including LGBTQ+ folks and people of color, about their experiences using generative AI tools like ChatGPT, Claude, and Gemini. Participants included both frequent AI users and young people opposed to using chatbots for emotional support.

Researchers found that those who did turn to AI for support feel more understood, validated, and taken seriously by AI than the people around them. Six central themes emerged from the research:

• They don't want to burden friends and family with their struggles.
• Chatbots won't judge them or use their secrets against them.
• AI responds as though it understands experiences others may not relate to.
• AI is available whenever they need it.
• Some see chatbots as neutral third parties, even while recognizing they can pander to users.
• AI offers concrete, step-by-step guidance for navigating difficult emotions.

The findings add to a growing body of research showing how young people are navigating their social and emotional lives with AI. A Pew Research Center survey found that 12% of US teenagers have used generative AI for emotional support or advice, while research from Surgo Health and The Jed Foundation found that about one in eight young people who reported mental health struggles had discussed those concerns with chatbots.

The report raises broader questions about how AI could shape young people's relationships and emotional development. It’s still unclear what, exactly, the risks may be. But rather than focusing solely on AI's potential risks, the report's authors argue that it's crucial to understand what unmet needs are driving young people toward chatbots. AI has the potential to complement, rather than compete with, human relationships, the researchers claim.

"As we navigate the rapidly changing landscape of AI and set norms for safer use, young people's experiences and motivations must be central," the researchers wrote. "If we focus only on limiting risks without understanding why AI chatbots sometimes feel safer, kinder, or more competent than the humans in their lives, we risk pushing young people away from AI without offering better human alternatives."

Our Deeper View

The line between human and machine interactions is starting to blur as AI increasingly creeps into users' personal lives. Some are using chatbots to text, flirt, and navigate difficult conversations. Others are developing friendships and romantic relationships with AI companions. Young people who grew up with unfettered access to the internet are susceptible to engaging with AI with this level of depth. Researchers are still trying to understand what that shift means. One study found that young people who report loneliness and difficulty making friends are more likely to use AI for social and emotional support. As emotionally responsive AI becomes more common, researchers are increasingly asking how those interactions could shape expectations around friendship and intimacy. Those questions will only become more urgent as AI grows increasingly humanlike. OpenAI, for example, recently updated its Advanced Voice Mode to stutter, pause, and listen more closely, making conversations feel more natural. As AI are trained to be more emotionally attuned, understanding how those interactions shape human relationships may become just as important as understanding the technology itself.

AI doesn't automatically translate into better work. But using it intentionally can.

A new study between KPMG and the University of Texas at Austin found that early-career professionals produced the strongest results when they treated AI as a thinking partner, rather than simply outsourcing work to it. Researchers found that workers who used AI to interrogate their work, challenge assumptions, and iterate on results outperformed AI on its own, revealing the limitations of passive AI usage.

Researchers gave 523 early-career professionals access to an AI agent and asked them to complete role-specific tasks. They then evaluated participants based on both the quality of their work and how they interacted with the AI system, comparing their performance against the AI's standalone output.

• The researchers identified three distinct approaches to using AI. Half of the participants were classified as "AI amplifiers," who actively steered AI throughout the task. As a group, they produced work that outperformed the AI baseline.
• The other two groups saw weaker results. "AI delegators," who accepted AI's suggestions with little scrutiny, performed about as well as the AI working alone. "AI apprentices," who engaged critically with AI but often redirected it toward weaker answers, scored below the AI baseline.
• The findings suggest that foundational skills, defined in the study as critical thinking, domain knowledge, and AI literacy, remain essential to effective workplaces. But they aren't what separates the highest performers.

"We weren't simply looking for people who knew how to use AI," Ashish Agarwal, professor at The University of Texas at Austin and co-author of the study, said in a release. "We wanted to understand what enables some individuals to consistently create value beyond what AI can produce on its own."

Workers who paired those skills with deliberate AI collaboration consistently produced the strongest results. It’s a takeaway that researchers suggest companies must consider if they’re looking to reap the benefits of AI.

"This is the most AI-native generation entering the workforce," Rahsaan Shears, who leads AI enterprise transformation at KPMG US, said in the release. "If fluency with the tools isn't what sets the top performers apart, that tells us something about our entire workforce."

Our Deeper View

Using AI with deliberate intention may be what separates quality work from slop. As companies increasingly mandate employees use AI to boost productivity, emerging research suggests the technology can backfire when used poorly. In some cases, AI is creating extra work as employees teach themselves how to use it and spend additional time correcting inaccurate or incomplete outputs. At the same time, some workers are becoming overreliant on AI to complete tasks, raising concerns about deskilling and the erosion of critical thinking and human judgment. It's difficult to pinpoint the threshold that constitutes responsible AI use. But if companies insist employees incorporate AI into their work, they must be equally invested in helping them use it well. That means developing training that strengthens the human skills that make AI most effective.

Software engineering was once seen as a ticket to a stable, well-paying career. Now, the profession is in flux.

As generative AI tools like ChatGPT and Claude Code become increasingly capable of writing code, software engineers are beginning to wonder how their roles will change. A recent Anthropic study found that computer programmers are among the occupations most exposed to AI, with the technology already covering roughly 75% of the tasks they perform, raising questions about the future of the field.

Some business leaders say the answers aren’t so simple. One of them is Michele Catasta, president and head of AI at Replit, an AI coding platform used by more than 50 million people to build software using natural language and AI agents. As one of the companies ushering in a new generation of vibe coders, Replit has a front-row seat to the profession’s transformation.

The Deep View sat down with Catasta to discuss how AI is reshaping the role of the software engineer, the democratization of software development, and why entry-level talent may have a competitive advantage in the AI era. The conversation has been edited for brevity and clarity.

Aaron Mok: In a recent podcast interview, CEO Amjad Masad described Replit’s Agent as "an automated software engineer" that's as capable as a “mid-level engineer at Meta or Google.” That is a bold claim. From your conversations with customers, what are some of the most surprising ways you've seen them use the technology?

Michele Catasta: The thing that's been most surprising to me—even though I've been working on this for 10 years—is that I practically don't hear users say they can't build what they have in mind anymore. That wasn't the case a year ago. We've gone from people creating landing pages to building software that would've taken weeks or months earlier in our careers.

I think what Amjad was trying to explain is you're not going to be able to take Replit’s Agent, teleport it into Meta tomorrow, and replace all the software engineers. It's more that someone who already has that level of skill can suddenly build a new product or start a business on the side.

We're also seeing strong enterprise traction. Eighty-five percent of the Fortune 500 uses Replit to automate repetitive work. Before, if you had an idea for a product, you'd write a product requirements document, hold several meetings, and design prototypes. Now, you can show up to that first meeting with a functional prototype. Decision-makers can immediately decide whether it's worth building or whether it needs another iteration. If you're a product manager or designer today, your skills are basically 10x overnight because you have way more throughput than you had before.

Mok: How are you seeing AI changing the role of the software engineer?

Catasta: I don't think anyone is doing less work today than before. If anything, this has been the most restless period in tech.

What's changing is the nature of the work. The cost of generating code is dropping. What's taking more time now is verifying whether it's correct. AI can generate a lot of code, but engineers still have to review it and decide whether to accept those changes.

It's not just engineers. Managers are overseeing more—and shorter—projects because teams can move faster. At the leadership level, the speed of the company means far more information is flowing upward, making it harder to process everything. We're all rethinking our jobs on a daily basis.

Mok: It sounds like AI is creating more work than less. Agents can generate thousands of lines of code, but someone still has to verify it and make decisions. Is it a misconception that AI is making software engineers less important?

Catasta: Yes, fundamentally I think that's a misconception.

I don't want to discount the fact that there will be some job displacement in the interim. Small software shops that don't adapt are going to struggle as companies become capable of building more internal tools themselves. Some shifting will happen, like it always does in this industry.

But the companies that have traction and are building something valuable. I don't know any founder in my space who isn't hiring like crazy when their company is growing, because that's the amount of work there is to be done.

Even if the role of an engineer is changing, maybe they're spending less time sitting in front of a screen typing lines of code. If you walk into the Replit office today, the amount of debate happening between engineers is even higher than before. You can leave an agent writing a pull request while you're discussing with your colleagues: Should we be doing X or should we be doing Y? What are the trade-offs?

As the cost of generating code drops, there's more time to think. There's more time to debate. There's more time for engineering teams to sit down with designers and product managers and exchange ideas. That's leading the industry to build better things.

Mok: There's been growing debate over whether AI could "deskill" software engineers by automating more of the coding process. Do you think that's a fair concern?

Catasta: I think it's a bit of a misconception. If you ask most staff engineers today how compilers work or how to write assembly code, a lot of them probably couldn't do it. That's not because they're worse engineers. It's because computing has evolved by creating better abstractions so people can focus on higher-level problems.

I think AI is another step in that evolution. Even if an agent writes most of the code, engineers still have to decide whether it's correct, whether it should be deployed, and whether it's the right solution. That's still a skill.

If AI automates more of that work, then we'll move another layer up the abstraction stack and spend more time understanding users and solving different problems. That's how software engineering has evolved for decades.

Mok: There's growing concern among computer science students and recent graduates that AI could reshape—or even reduce—entry-level software engineering jobs. How do you see the career path for junior engineers changing?

Catasta: I know some large companies have stopped hiring below a certain level, and I think that's a huge missed opportunity.

For startups like ours, it's actually been fantastic because we're hiring exceptional graduates who grew up in the AI era. They started college around the time ChatGPT came out. They’re AI natives. They've been using AI coding tools from the beginning, and no one is more ready for this revolution than them. Startups can also give them much more scope early in their careers, which is a great opportunity.

Mok: So is AI literacy becoming a competitive advantage?

Catasta: Absolutely. AI literacy has become table stakes. The employees we hire and the companies we sell to are AI-forward. They expect people to know how to work with these tools. As chaotic as this shift can be, it's also incredibly exciting because people can build much faster.

Six months ago, I would've looked at an ambitious project and told my team it would take us a quarter. Now I can say, "It's going to take three weeks. Let's do it." It's extremely empowering to feel like we can move fast and make things happen.

Mok: As AI coding tools become more capable, who do you think stands to benefit the most?

Catasta: Our focus has always been on knowledge workers who don't come from a technical background.There's a lot of raw intelligence in today's AI models and tools, but very few people know how to turn that intelligence into something genuinely useful. That's why so many companies are still asking what the return on investment from AI actually is.

Software is a very broad concept. It's not just about building applications. It's also creating scripts, automations, internal tools, and agents that help people do their jobs. I want that superpower to be in the hands of everyone.

American tech companies are bearing the brunt of this year's tech layoffs.

About 82% of the 156,975 tech jobs eliminated globally in the first half of 2026 came from US companies, according to a new report from TradingPlatforms, a UK-based research firm that analyzed workforce reductions using data from TrueUp.io, Layoffs.fyi, TechCrunch, and state WARN filings. Almost half of the cuts were tied to US companies restructuring around AI and automation, led by bellweathers such as Oracle, Meta, Block, Cisco, and PayPal, according to the report.

Cloud computing and software-as-a-service companies recorded the highest number of layoffs globally, TradingPlatforms found. Many of those reductions came from Oracle, Microsoft, and Salesforce, among other firms. E-commerce and marketplace companies, including Amazon and eBay, were the second hardest hit. California-based companies accounted for the largest share of US layoffs among the states, followed by Texas, Washington, and New Jersey, according to the report.

Compared to other sectors, tech companies are often the first to feel major economic and technological shifts because they're quicker to adopt new tools, according to Stanislava Savisheva, an analyst at TradingPlatforms.

"Nearly every major technology company is currently looking for ways to operate with leaner teams, greater automation of workflows, and more efficient cost structures, with AI accelerating a shift that was already underway," Savisheva wrote in a statement.

The report comes as US tech companies reorganize their workforces as they bet billions on AI. On July 6, Microsoft announced it would immediately cut about 4,800 jobs, 1,600 of which were across its Xbox gaming division, as it continued ramping up AI investments. In June, Oracle disclosed that it had reduced its workforce by 21,000 employees over the past year to invest in AI. A month prior, Meta cut about 8,000 employees, shifting thousands more workers into AI-focused roles. That same month, Cloudflare eliminated about 1,100 jobs as CEO Matthew Prince said the company was preparing for the "agentic AI era."

"The next phase of this transformation will not simply be about replacing jobs," Savisheva wrote, but about "fundamentally changing how businesses build teams, distribute work, and measure productivity in an AI-driven economy."

Our Deeper View

AI-driven restructuring is becoming the norm. But companies may want to think twice before cutting staff in anticipation of greater efficiency. Ford, for instance, recently hired more than 350 veteran engineers, some of whom were former employees, after finding its AI-powered quality systems couldn't perform on their own. At the same time, many companies are still struggling to show meaningful returns on the billions they're pouring into AI. As investors and boards push executives to justify those investments, layoffs may prove easier to announce than to live with. According to data from PwC, the companies that benefit most from AI are often the ones that use it to augment employees rather than replace them.

AI promises to automate workflows and save employees time. But a lack of alignment among company leaders may keep those productivity gains out of reach.

While 85% of C-suite executives report deploying AI across their organizations, only 54% of managers say AI is a top priority, according to new data from Nitro, a document software company, which surveyed more than 1,300 executives, managers, and directors across the US, UK, and Canada. Barely half of managers say AI has reached at least some of their own workflows, revealing a disconnect between executive aspirations and day-to-day realities.

"At an executive level, it can be about the message: 'Yes, we are using AI,'" Cormac Whelan, Nitro's CEO, told The Deep View. "At the manager level, it is often about solving for the next level of detail on how we are using it."

Among managers whose teams have adopted AI, 37% primarily rely on standalone AI tools, such as copying and pasting content into ChatGPT. Only 12% say AI is fully embedded into their document workflows.

Much of the work remains manual. More than half of managers say employees spend the most time extracting data from documents into spreadsheets, followed by editing and formatting files, managing documents, summarizing content, and redacting sensitive information. Because of that, 62% of managers say employees spend six or more hours each week on manual document tasks, with nearly a third estimating those workloads consume 11 or more hours.

"If AI isn't built into the workflows, work surfaces, and systems people use every day, with the outcome in mind, it doesn't matter how many tools you buy or how many tokens [you burn] or how much budget you commit," Whelan said. "The work stays manual."

The findings underscore a growing challenge in enterprise AI adoption. Businesses are betting big on AI to boost productivity and cut costs, but many managers appear to be taking a more measured approach to rolling it out across their teams.

Managers cite security and trust as their biggest barriers to AI adoption, followed by integration complexity and implementation costs. More than half also say sensitive documents are being uploaded to public AI tools, while only 43% report having clear, actively enforced AI policies.

To close the gap, the findings suggest that companies need AI embedded into the software and workflows employees already use every day, with security and governance baked in from the start. Otherwise, AI risks become another set of tools rather than the productivity engine executives expect it to be.

Our Deeper View

AI adoption is as much an organizational challenge as it is a technological one. While executives focus on transforming the business to appease stakeholders, managers are responsible for making AI work day-to-day. As layoffs shrink middle management and spread teams thin, many are juggling a mounting list of responsibilities. They're not only expected to lead their teams, but also figure out how to integrate AI into existing workflows and systems. Closing that gap goes beyond creating an AI strategy. It requires building a culture where employees have the time, training, and trust to experiment with AI out of curiosity rather than purely top-down pressure.

Companies are deploying AI faster than they can secure it.

Nearly eight in ten enterprises reported encountering AI-related security issues over the past six months, according to new research from security firm DigiCert, which surveyed 1,001 IT and cybersecurity leaders across the US, UK, and Australia.

Approximately 50% of respondents experienced at least one security incident caused by an unauthorized or misconfigured AI agent, while another 28% identified AI-related vulnerabilities without experiencing an incident, a spokesperson from DigiCert told The Deep View. Science and technology organizations reported the highest rate of AI-related incidents or vulnerabilities, followed by banking, financial services and insurance, telecommunications and media, and retail.

While companies weren't asked to identify specific incidents they experienced, Brian Trzupek, DigiCert’s senior vice president of product, said that unauthorized or misconfigured AI agents can pose a range of security risks, including prompt injection attacks, data poisoning, unauthorized access to sensitive systems, and a lack of traceability.

“AI agents are non-human actors operating at machine speed, and most enterprises haven't extended the identity, authentication, and audit controls they already require of every user, device, and application to cover them,” Trzupek told The Deep View.

The risks are outpacing companies' ability to manage them. While 90% of organizations have discussed AI governance, only half have dedicated budgets and formal governance programs in place, according to the study. Nearly half also lack full visibility into how AI systems arrive at their outputs, making it difficult to trace which model produced a decision or investigate what went wrong.

The potential for unintended consequences is becoming more urgent as companies accelerate AI adoption. Over the past six months, 75% of organizations deployed four or more AI-powered systems, while 35% deployed more than 10. As AI becomes embedded across business operations, every new model, agent, and integration introduces new security risks. Without stronger governance, companies could face legal, regulatory, operational, and reputational repercussions.

The report argues that enterprises need stronger identity and governance controls, including the ability to verify AI identities, audit AI decisions, and trace outputs back to the underlying models and data. Companies appear to be moving in that direction, with 86% reporting that they have formal or informal processes for revoking access to compromised AI systems.

“Without governance, organizations lose the ability to answer basic questions: what AI is running, what it can access, and who is accountable when it fails,” Trzupek said.

Our Deeper View

Enterprises adopting AI have stepped into uncharted territory. Before AI, companies were already dealing with a slew of vulnerabilities and cyberattacks. Now, AI has only added to the challenges as the threat landscape evolves. Companies must not only prevent their AI systems from leaking proprietary data, but also protect them from exploitation by bad actors. And that only scratches the surface. As AI advances, threat actors are becoming more sophisticated, potentially opening the door to new attacks that haven't yet been discovered. If companies are serious about baking AI into their workflows, security and governance will have to become a higher priority.

Despite fears of AI replacing workers, companies making the biggest bets on the technology are still hiring.

Companies that invested most heavily in AI grew their headcount by 10% over the past two years, according to a recent study from Ramp and Revelio Labs. The researchers analyzed AI spending and workforce data from more than 21,000 U.S. companies. Entry-level hiring rose by 12% among the heaviest AI adopters.

AI adoption was uneven across industries. Companies seeing the strongest headcount growth were larger, more engineering-intensive, and more likely to be venture-backed, particularly in information, finance and insurance, and professional and technical services. Adoption was far less common in industries such as healthcare, accommodation and food services, and arts and entertainment.

Employment growth was broad across job functions. High-intensity AI adopters saw statistically significant increases in engineering, sales, customer service, finance, and administrative headcount, suggesting that AI usage augments the workforce and increases overall economic activity.

"If AI lowers the fixed cost of building software, handling administrative work, doing analysis, or improving customer support, the gains can drive outsized growth and unlock new revenue streams that previously required higher fixed costs in the form of new salaries," Ara Kharazian, Ramp's lead economist who worked on the study, wrote in a blog post.

The findings challenge the narrative that AI adoption inevitably leads to fewer jobs. Headlines about companies citing AI in layoffs, along with warnings from AI leaders about widespread automation and the loss of entry-level jobs, have fueled fears about AI eating the labor market. This study paints a more complicated picture: among companies making the largest investments in AI, hiring continued to grow.

The researchers caution that the findings shouldn't be generalized across the broader economy. Instead, they offer an early look at how heavy AI adopters are changing their hiring patterns.

"We believe [employers] are selecting for a new set of skills, specifically, people who know how to use AI and use it well," Kharazian wrote.

Our Deeper View

These findings should be taken with a grain of salt. The study focuses primarily on larger, tech-forward companies and doesn't capture how small businesses are using AI or whether companies rely on free AI tools. It also shows a correlation between AI spending and hiring, not that AI directly caused companies to add workers. Even so, the study points to an important distinction: companies seeing the strongest growth don’t just slap AI onto their workflows. They invest heavily in it and integrate it across their organization. While it's still too early to know which jobs AI will ultimately replace, it's important to note that AI literacy is quickly becoming a baseline expectation for workers.