WORKFORCE

Report: Workers are lying about their AI skills

Though enterprise executives are eager to ramp up their AI efforts, the tech may be causing a workforce morale problem. 

A study published this week from GCheck found that, faced with anxiety about how automation could impact job security, 63% of 1,500 workers surveyed reported that they exaggerate their AI skills to appear more up-to-date. That number shot up to 80% among Gen Z workers as the tech threatens early-career and entry-level roles more drastically. 

The GCheck report found that AI is pulling on workers from both ends, leaving them caught between the fear that AI will disrupt their jobs and the pressure to appear as AI power users. 

• Nearly 70% of workers reported that they believe AI will automate part of their responsibilities. And these concerns may not be unfounded: 40% observed that AI tools are already, in part, doing their jobs. 

• Only 38% said they feel prepared to use AI tools effectively, while 22% said they would struggle to use AI or wouldn’t be able to use it at all. 

• Still, many aren’t voicing their concerns. Around 40% said they speak confidently about AI in meetings to avoid appearing behind, and 33% let others assume that they have strong AI skills. 

• A quarter have taken full credit for AI-assisted work, and 16% admitted to outright lying about their AI skills. Largely, employees are doing this due to pressure to appear AI-capable, fear of losing their jobs, or feeling they don’t have a choice. 

However, despite talking up AI, the impact on morale is influencing worker behavior towards the tech, GCheck CEO Houman Akhavan told The Deep View. Around 81% of those surveyed admitted that they discourage or limit using AI at work. Akhavan called this contrast “double distortion.” Rather than relying on AI tools, workers are choosing manual work to avoid them. 

“[It’s] a workforce overstating its AI abilities and quietly undermining AI adoption in the same organizations,” Akhavan said. 

To combat this, enterprise leaders need to approach these anxious workforces with more empathy, Akhavan said. Workers need to feel safe enough to be candid about what they know and do not know, without the fear of job loss looming over them. More than half of the workers who reported exaggerating their AI skills never actually received any training. “That is a learning culture problem dressed up as a technology problem,” Akhavan said.

The foundational truth this survey reveals is that successful and peaceful transformations do not occur under duress. People do not like feeling coerced or forced to engage with anything, let alone something that poses a potential threat to their livelihoods. And we have to be honest that companies using AI as a scapegoat for layoffs are feeding the problem. Rather than empowering workers to safely experiment and explore, companies that are slashing headcount are creating perceptions of AI that make workers resent the technology rather than be curious about it. This has widespread implications. When workers' words don't reflect their actions due to resentment and anxiety, enterprises don't get a clear picture of how their AI deployments are actually performing. And broadly, the fear fuels negative public opinion on AI, which is already creating growing problems for the industry.

TOGETHER WITH IBM

Modernize Java in days, not months

IBM Bob is the AI Development partner built for the modernization work you need to get done.

• Java upgrades

• COBOL refactors

• RPG

• Mainframe modernization

Blue Pearl compressed a 30-day Java upgrade to 3 days with zero post-deployment defects.

IBM Bob ingests your codebase, your standards, and the unglamorous work of refactoring legacy. You can direct IBM Bob to dynamically route tasks to a suitable model based on accuracy, performance, and cost, drawing on a mix of frontier models including Anthropic Claude, Mistral, IBM Granite and others for you.

See what IBM Bob does with your legacy code

CULTURE

Robotaxi reality check: Where humans still win

There are times to take a robotaxi, and there are times when you need a human driver. I found that out the hard way this week. 

It all started with my bright idea on Tuesday morning. I was staying in San Francisco and needed an early ride down to Shoreline Amphitheatre in Mountain View for the Google I/O keynote. "Wouldn't it be fitting to take a Waymo to the event?" I thought. "I'll just leave plenty early." Big mistake. 

I wanted to check into Google I/O when the doors opened around 8:00 a.m. to get a good seat for taking photos. So I planned to leave around 7:15 a.m. since the ride would take about 45 minutes in an Uber. Since a Waymo usually takes a little longer to hail, and it doesn't drive as aggressively, I figured it would take a little extra time. Plus, there might be extra traffic. 

So I wasn't surprised when the Waymo picked me up at 7:20 a.m.and calculated an estimated arrival time of 8:32 a.m. Sure, that sounded a little slow, but I didn't sweat it since seating at Google’s keynote wouldn't begin until 8:30 a.m. anyway. 

The trip went smoothly from pickup at my hotel in Mission Bay to getting on Highway 1 and cruising for about 30 miles to getting off on Exit 400A and getting onto Amphitheatre Parkway, about a mile from the venue. We were still on track for an on-time dropoff. And then things fell apart. 

The traffic in Mountain View was completely backed up and was being routed in very specific ways. At one point, the Waymo waited very patiently in a line of cars that didn't move for about 10 minutes. An experienced Uber driver would likely have switched lanes and found a different route. After finally moving through the traffic snarl, the Waymo then did something unexpected. 

It turned left and went onto a back road where few other cars were going. That ended up putting us in the parking lot of a Google campus building. Once there, the Waymo started acting as if it was uncertain which way to turn. So, I tapped the Support button on the backseat screen, and a Waymo agent came on the speakerphone to help. As we talked through the situation, the Waymo eventually circled around the perimeter of the parking lot and pulled onto a street where it could take me to the drop-off point in Lot B of the Shoreline Amphitheatre. 

The Waymo dropped me off at 9:09, 37 minutes after the originally calculated drop-off time and almost two hours after picking me up. The ride cost $102.72, and afterward, Waymo sent me a $10 credit to apply to a future ride, for my troubles. Luckily, Sabrina Ortiz saved me a seat at the Google I/O keynote, and all ended well. 

Based on this experience, I've come up with a list of situations where I'll avoid Waymo (and other robotaxi services) in the future and take an Uber instead:

• When I'm in a hurry and have a really important meeting or event, and it's critical that I be on time

• When there could be irregular traffic situations and road closures

• When there are unnamed roads and places that could put the vehicle in unfamiliar territory

• When there is the potential for heavy traffic that may demand the ability to think creatively about finding alternative routes

To be clear, I've had lots of good rides with Waymo. I like the service, and I'll continue to use it. If you're not familiar, Waymo started out as the Google Self-Driving Car Project and is still owned by Alphabet, Google's parent company. That's why I thought it would be apropos to take a Waymo to the main event at Google I/O 2026. That’s also why it's so ironic that, of all places to get lost, the Waymo got lost on Google's campus. This incident was a good reminder that there are situations where a human's ability to "think on their feet," navigate ambiguity, apply common sense, and deal with uncertainty remain key advantages over AI, at least for now. It’s also a reminder that self-driving vehicles still have miles to go before we can start relying on them for our most important rides.

TOGETHER WITH GRANOLA

Real Conversations = Rich Context

By now, you almost certainly know how much all of us at The Deep View love Granola, the notetaking app that saves us around 10 hours per week per person. But their latest update, Spaces, is taking that seamless collaboration and documentation to the next level… and we’re experiencing it firsthand. 

Essentially a team workspace with folders and chat built in, Spaces uses your conversations to give context to any question your team asks. From sales asking “Why are we losing this deal?” to researchers wondering “What are users consistently asking us for?”, you can ask anything and Granola will read all of the Spaces content to immediately give you an answer. 

Gusto, Asana, and more already love Granola Spaces – try it for yourself right here.

HARDWARE

How the compute crisis defines the future of AI

In this episode of The Deep View Conversations, we sit down with Robert Brooks IV, chief commercial officer at Lambda, to talk about the massive AI infrastructure buildout now underway.

Lambda’s mission is to build supercomputers for superintelligence. But Brooks argues that the story is bigger than GPUs, data centers, and rising demand. It is about why compute is becoming one of the most strategically important resources in the AI economy, and why Lambda believes compute is not a commodity.

The conversation goes deep on Lambda’s vision for democratizing AI, why the company invests in research, and how its experience building physical infrastructure shapes what it can offer AI labs, hyperscalers, enterprises, and researchers.

Topics covered include:

• Why Lambda thinks “one GPU per person” is achievable

• The hidden complexity behind modern AI data centers

• Why compute demand keeps surprising the industry

• His $40,000 robot experiment and what it taught him about the future of work

• How AI is changing the way leaders spend their time

If you want to better understand the physical and economic foundations powering the AI boom, this conversation is worth your time.

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