AI in the workplace is no longer a theoretical debate — it’s playing out in boardrooms, developer desks, and leadership experiments. One startling recent example is Klarna’s CEO forcing employees to review his AI-generated “vibe coding” prototypes. This case spotlights power dynamics, productivity trade-offs, and the ever-changing boundary between human and machine at work. ( Gizmodo )
In this article, we dive deep:
- What exactly happened at Klarna?
- Why does it matter for AI in the workplace?
- What broader lessons does it teach about automation, leadership, and culture?
- And what are the practical takeaways leaders and employees should know?
What happened at Klarna?
CEO Sebastian Siemiatkowski revealed that he’s begun using AI tools (specifically the cursor-based “vibe coding” approach) to whip up prototype features in about 20 minutes, then bringing them to his engineering team for review.( Gizmodo)
- He admitted he’s never been a coder, yet uses AI to generate functional code as a proof-of-concept.( Gizmodo )
- He frames it as “testing his idea first, before disturbing engineers with half-baked ideas.” ( Gizmodo+1 )
- Critics argue: it burdens engineers with reviewing and polishing something they had no role in designing. ( Gizmodo )
This isn’t just a quirky anecdote. It’s a microcosm of evolving workplace norms under AI.
What is “vibe coding,” and why is it relevant?
Brief Answer: Vibe coding is a style of AI-assisted software development where the human operator doesn’t deeply inspect or edit the code, relying on AI’s iterative feedback loops instead.( Wikipedia+1)
Key points:
- Coined by Andrej Karpathy in 2025, vibe coding emphasizes giving prompts, seeing results, adjusting, and trusting the “vibe” more than exact syntax.( Wikipedia+1)
- The human doesn’t need to read, refactor, or control line-by-line code — they treat it more like an experiment than classical engineering.( Wikipedia)
- It highlights a shift: from being the “hand that writes” to being the “mind that directs.”
In Klarna’s case, Siemiatkowski is using vibe coding not just as a side experiment, but as a way to interface his leadership ideas with engineering execution.
Why does this matter for AI in the workplace?
Short answer: It exposes tensions and trade-offs between efficiency, authority, accountability, and trust.
- Power dynamics & authority
When a CEO bypasses the “chain of creation” and injects AI outputs into engineers’ workflow, it can fracture ownership and autonomy. - Technical debt, quality risks
AI-generated prototypes may work superficially but contain hidden flaws. Developers often spend extra time debugging generated code. (One survey found ~95% of developers spend extra time fixing AI code.( Gizmodo+1)) - Cognitive and psychological load
Constantly reviewing someone else’s AI-generated work (especially from a non-expert) may incur frustration, perceived disrespect, or “don’t tell me how to do my job” backlash. - Symbolic message about AI vs humans
The move suggests leadership sees AI not just as a tool but as a competitive peer — even supplanting traditional roles. That shifts culture in subtle but deep ways. - Blurring responsibility
If AI code fails or causes a bug, who is accountable — the CEO who generated it, or the engineers who “implemented” it?
Thus, the Klarna episode isn’t isolated — it’s a test case for what AI in the workplace might look like when authority, creativity, and code collide.
How is this experiment being received — internally and externally?
Good question — reactions are mixed.
- Engineers & product teams
Anecdotal feedback suggests eye rolls and frustration. Some feel it undermines their domain expertise. Some see it as a way for leadership to “pose as a creator.” “Rather than disturbing my poor engineers … now I test it myself” — a quote that spurred consternation.( Gizmodo ) - Media & analyst commentary
Outlets like Business Insider highlight that Siemiatkowski claims vibe coding saves time for engineers. (Business Insider)
But critics caution this may commodify engineering, erode quality, and overlook hidden costs. - Broader discourse on AI in leadership
Some see this as a sign that even CEOs feel threatened by AI or pressured to demonstrate “hands-on” technical legitimacy. Others warn of performative misuse of AI as a power play.
What lessons does this case teach for AI in the workplace?
1. Prototypes ≠ production
AI can help spin up demos quickly — but real-world robustness, security, scalability, maintainability still demand engineering rigor.
2. Don’t weaponize AI as top-down edict
Using AI outputs to impose direction without consultation may backfire. Collaboration and feedback loops remain crucial.
3. Define accountability clearly
When AI is involved, clarify who owns errors, fixes, and improvements. Shared blame is messy.
4. Support developer autonomy
Allow engineers to accept, reject, refactor, or reject AI-driven suggestions. The human-in-the-loop must remain active.
5. Use AI as augmentation, not takeover
Best practices position AI as assistant, not authority — letting humans steer narrative, ethics, strategy.
6. Lead by transparency
If leadership experiments with AI outputs, being open about methods, failures, trade-offs can de-escalate suspicion.
How should companies govern AI-based leadership experiments?
- Policy guardrails: define zones where AI-generated proposals need separate review or limits
- Code review stages: mandate that AI-generated code goes through the same peer-review pipeline
- Ethics and oversight committees: cross-functional team to monitor AI misuse
- Transparency logs: track who generated what AI output, prompt versions, iteration history
- Iterative feedback loops: get buy-in from engineers, product, design teams before pushing AI concepts
- Training & guidelines: educate teams about AI strengths, limitations, and risks
Frequently Asked Questions (FAQ)
Below is a suggested FAQ section with schema markup (for bots and SEO). Use JSON-LD or your CMS’s FAQ block to embed.
What does it mean when a CEO “vibe codes”?
It means the CEO uses AI tools to generate prototypes (code or features) based on prompts, rather than writing or deeply reviewing code themselves.
Why might asking engineers to review AI work be controversial?
- It can feel like bypassing expert authority
- It may introduce low-quality code or hidden bugs
- Engineers bear the burden of debugging, interpretation, and integration
Is AI in the workplace inherently bad for employees?
No. Thoughtfully integrated AI can boost productivity and innovation — but it must respect domain expertise, ensure transparency, and avoid power imbalance.
How can a company adopt AI leadership tools responsibly?
- Require human-in-the-loop validation
- Maintain accountability and code review policies
- Pilot experiments transparently and share learnings
- Monitor impact on culture and morale
Will we see more CEOs doing “vibe coding”?
Possibly. The trend reflects deeper pressures: leaders wanting to speak the language of engineers, validate ideas quickly, and stay competitive in AI adoption.
Multi-format assets & content ideas
- Infographic: Comparison — traditional prototyping vs vibe coding pipeline
- Flowchart: Decision tree for when AI proposals should be accepted, rejected, or refined
- Pull-quote box: “I test it myself … what do you think?” — Sebastian Siemiatkowski
- Code snippet examples: show how a prompt → AI code → bug → fix loop might pan out
- Embedded tweet/thread: engineer reactions or community commentary
- Diagram: ownership map (who owns idea, code, review, deployment)
A deeper take: AI in the workplace is stratified, not uniform
One underlying takeaway is that AI’s impact depends heavily on where in the org it’s used.
- At leadership level, it’s symbolic, experimental, or strategic.
- At engineering level, it’s tactical assistance, maintenance, or augmentation.
- At operations or customer service level, it’s automation, scale, or monitoring.
When leaders adopt AI tools in their own domain (as Siemiatkowski has), it signals the boundaries are blurring. But boundaries matter — because trust, expertise, code quality, responsibility, and autonomy all rest on them.
In effect, AI in the workplace isn’t one wave — it’s many concentric ripples, overlapping in tension and promise.
Final Thoughts
Klarna’s CEO using AI experiments is not just a tech gossip nugget. It’s a timely illustration of how AI in the workplace is reshaping authority, accountability, and organizational culture.
If AI is the new co-worker, we still need to define what roles humans play — not just in coding, but in questioning, curating, judging, and integrating. The weirdness in this moment helps us see the gaps we must fill before AI becomes a default director, not just a tool.
If you like, I can generate the JSON-LD FAQ markup for your CMS or suggest relevant images or embed code to make this article even more compelling. Do you want me to build that? Let’s talk!