Singularity and your business: what AI CEOs mean (and don't)

Sam Altman said we're "now in the singularity."

He said it on the Relentless podcast. Coverage is easy to find; The Jerusalem Post quotes him: "We are now in the singularity; this is the moment."

Demis Hassabis talked about the "foothills of the singularity."

MIT Technology Review reported him at Google I/O saying we are "standing in the foothills of the singularity." Stanford GSB has him putting it this way: ten years from now, we may look back and realize we were already in those foothills.

I read that stuff the same way a lot of owners do. Half curious. Half annoyed. If AI in your world still means ChatGPT in a browser, those sentences do not help you quote a job, close the books, or get a truck out on time. They mostly make people feel late. Then they buy tools. Then they still have the same process running late every Friday.

I am not here to join a lab fan club. Or a doomer club.

I want the plain version: what they mean, what they don't, and what is worth doing in a normal company this week.

What they said, without the theater

Altman was talking about a steep curve, not one magic flip day. Progress that keeps stacking. He stretched "singularity" to mean that continuous climb. The podcast is the source; the news is the mirror.

Hassabis used a mountain. Foothills. Early. Big climb still ahead. At Stanford he also used the big industrial comparison: something like ten times the impact of the Industrial Revolution, ten times faster. That is their world: models, science tools, policy pressure. Not your dispatch board.

Both men run shops that build the strongest models on the planet. Vision is part of the job. So is narrative. That does not make every sentence a lie. It does mean they are not writing your Monday checklist.

You do not need their keynotes. You need the gap between a future story and an operations decision.

The old meaning was much harsher

Before the podcasts, "technological singularity" usually meant something sharper.

AI gets good enough to improve itself. Then again. Faster than people can follow. Some people call the extreme version an intelligence explosion.

That is a high bar.

ChatGPT getting better at email is not that.

Your team using AI for first drafts is not that.

Two CEOs using a dramatic word on stage is not that.

Mix those up and you either panic or overspend. I see both.

What I treat as solid. What I treat as soft.

Solid enough for planning:

• Tools got better fast at writing help, first-pass support, search, and simple sorting.
• Big labs fight hard for talent and money. That pressure leaks into every sales pitch you hear.
• Owners feel they must "do AI" even when nobody wrote down how the work actually moves.
• Privacy mistakes and bad handoffs already hurt companies. You do not need sci-fi for damage.

Soft or contested:

• Exact dates for human-level AI across almost everything
• Claims that classic self-improving superintelligence is already here
• Lines like "ten times the Industrial Revolution at ten times the speed" sold as measurement
• The idea that "the experts" all agree. They don't.

Clearer Thinking walks through why timelines split so hard: lab leaders often sound near-term; broader research views often sound later; smart people still disagree. Either side can be wrong. For you the practical line is simple: nobody has settled proof that you must rebuild the company this quarter because someone said singularity.

What I would do if this were my company

Forget the mountain. Forget the podcast clip.

I would sit with one messy process and ask three things.

Where can simple AI help without drama?

Drafting a letter. Summarizing a long email. First-pass translation. Cleaning notes after a call. Rough-sorting tickets. Stuff you can reverse cheaply if it is wrong.

What must stay human?

Prices you stand behind. Legal and tax judgment. Promises to clients. Hiring and firing. Anything where a wrong answer burns trust, safety, or compliance. If you cannot explain the decision in plain language, do not give it to a model alone.

What breaks if the AI is wrong?

Wrong delivery date. Wrong invoice. Wrong quote. Wrong warehouse instruction. Wrong advice in a regulated field. Write the failure down. If you cannot name it, you are not ready to hand that step over.

This is boring on purpose.

Labs argue about governing models. Most companies still cannot see their own workflows clearly enough to govern anything.

You cannot control what you never made legible.

I will publish a separate piece on what an AI operating layer is in plain language. The layer between you and the tools. Not another chat window. Something that makes work visible, repeatable, and checkable. That is the useful bridge from news noise to how a small company can actually run. Hermes shows up there as a lived example of that layer, not as a product pitch.

Skip this junk

• Buying a new AI stack because a CEO used a big word
• Rebranding "AI-first" with no process map
• Pasting client files, employee data, or contracts into random tools
• Waiting for perfect AGI before fixing one painful bottleneck
• Sitting through a vendor "transformation" pitch if they cannot name your failure modes

If the sales call dies on those three questions, end the call.

Monday

1. Pick the process that always runs late or creates rework.
2. Write the steps as they really happen. Not the nice version.
3. Mark each step: human only / AI can assist / never AI alone.
4. Test help on a copy of real work. Keep a human check at the end.
5. When it fails, log one line. Where. Why. That log is worth more than another headline.

If you want the landscape without the fog, start here:

Which AI model should I actually use?

The AI landscape in one read

What to understand before you pay for AI

AI adoption is not about how many agents you run

If you want proof instead of slides, open the work section:

What an owned AI operating layer looks like in production

How I fixed my chatbot

All work and case studies

Close

When lab CEOs say singularity, I hear: change is fast, stakes are high, and we are early.

That can be true and still tell you nothing about scheduling jobs, closing the month, or answering a client on Friday afternoon.

Your edge is not guessing the summit.

Your edge is making work understandable before you hand pieces of it to a machine.

Do that and the next wave of tools gets easier to use. And harder for someone to sell you garbage with.