We Already Live in the Tomorrow We Imagined
Two years ago almost engineer I know still had Stack Overflow and Google on a second monitor while coding.
Today most of them type a natural-language prompt into Copilot or Cursor and hit.
76% of developers now say they already use or plan to use AI tools in their daily workflow – up from 62% just last year. (survey.stackoverflow.co)
For teams like ours at Markhub, the step-change is obvious.
Had we if built this product in 2020,
we would have needed 15-20 engineers, planners and designers to hit the same release quality.
With AI pair-programming, automated QA, and instant design iterations,
we run lean-and-mean with fewer than half that head-count.
But How Do Non-Tech Folks Use All This Power?
Spend five minutes on Reddit or TikTok and you’ll notice an unexpected pattern: people pour their life worries into chatbots. A 2025 Dartmouth study reported that an LLM-based companion reduced participants’ anxiety and boosted “felt support” after only two weeks of use. geiselmed.dartmouth.edu Another Nature paper interviewing everyday users found “high emotional engagement” and a tendency to treat the bot as someone rather than something.
(by nature.com)
Business-insider stories now cover friends who let ChatGPT draft apology texts and even couples who consult AI before quarrels.
(by businessinsider.com)
Apps like Replika market themselves openly as “AI companions,” logging millions of messages a day.
(by adalovelaceinstitute.org)
In short, the mainstream isn’t asking,
“How do I optimize my sprint velocity?”
They’re asking,
“Can this thing understand me, calm me, maybe even cheer me up?”
The Surprise: A Warm Machine
Back in 2022 the dinner-table fear was that AI would feel cold a statistical parrot at best.
Yet users keep describing these agents as empathetic, comforting, even friend-like.
The models aren’t sentient, but they are astonishingly good at mirroring human emotional patterns,
offering reframed perspectives, and remembering context in ways that a tired friend sometimes can’t.
That warmth is an edge we actively design for in MAKi, our in-house AI collaborator inside Markhub.
Where traditional enterprise tools chase raw throughput, we chase psychological safety:
- MAKi stays in the chat stream, reading the same lines humans read, so feedback never feels like it drops from a black box.
- It summarises a heated thread without blame, assigns owners in neutral language, and gently nudges deadlines instead of spamming alerts.
- Teams tell us the bot’s tone often resets tense conversations exactly the “soft” value skeptics said AI could never deliver.
Where Tech-Friendly and Tech-Newbie Finally Meet
We believe this emotional interface is the missing bridge between hardcore devs and people who still find new software intimidating.
The code jockey adopts AI for speed; the newcomer adopts it for reassurance.
Both stay because the product quietly turns loose chat into reliable knowledge.
Inside a Markhub “hub,” MAKi parses every comment, tags the right file version, and stores the distilled decision as a reusable asset.
A month later that once-casual chat has become structured data the whole team can query.
The machine feels warm on the surface yet acts relentlessly systematic underneath exactly the combination modern work demands.
A Future Built by Many but Powered by One More Team-Mate
Yes, the media still runs doom headlines, and new Stanford work reminds us that AI therapy is not a replacement for licensed clinicians. hai.stanford.edu But the everyday evidence is clear: when thoughtfully designed, AI agents add empathy at scale and unlock productivity once reserved for 20-person teams.
Our bet is simple: every team deserves a tireless collaborator who codes, organises, and, when needed, just listens.
The technology is already here; the warmth was the surprise bonus.
Why does your team lean on AI speed, comfort, or something in between?
Written by DongYoon Shin · Founder & CEO, Markhub