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Yes. And I think the most important conclusion is not “Is AGI coming?” It is:
> What happens to B2B business development when the cost of producing information, analysis, content, outreach and even parts of execution approaches zero — while the value of judgment, trust, relevance and human accountability rises?
That is where I think this LinkedIn conversation becomes genuinely useful to you.
My read of the conversation
The thread is really three conversations disguised as one:
1. How close is AGI?
2. Who has an incentive to say it is close?
3. What happens economically when AI capability keeps improving, regardless of whether we ever agree that it is “AGI”?
The first question is fascinating.
The second and third are commercially much more important to you.
The original poster makes the strongest point when she asks:
-
benefits from early AGI timelines?
benefits from early AGI timelines?
- Who defines AGI?
- Who is accountable when predictions are wrong?
She explicitly argues that AGI predictions can reflect ideology and incentives as much as evidence.
And the comments demonstrate exactly why this matters: people disagree dramatically about whether today's systems are already AGI, whether LLMs can reach human-level cognition, whether AGI is 5 years away or decades away, and even what AGI means.
That's not a very good foundation for making business decisions.
But AI capability doesn't need to become AGI to radically change B2B sales.
That's the part I would build your business around.
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1. The incentive bias is real — but don't turn it into an anti-AI argument
This is where I would slightly modify the LinkedIn conversation.
It's absolutely reasonable to ask:
“Who benefits from this prediction?”
AI companies benefit from expectations of enormous future capability.
Investors benefit from narratives supporting enormous valuations and capital expenditures.
Consultants benefit from fear and urgency.
Media benefits from dramatic predictions.
Governments benefit from framing AI as strategically important.
Employees may benefit from emphasizing AI limitations.
AI skeptics can also have incentives.
And people who built their careers around a particular technology can become psychologically invested in that technology's importance.
So the incentive test cuts both ways.
The mistake would be:
> “They have an incentive, therefore they're wrong.”
The better conclusion is:
> “Their incentive means I should discount the prediction and examine the evidence separately.”
That's much more intellectually defensible.
And it fits beautifully with your consulting philosophy.
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2. History says humans repeatedly overestimate AND underestimate technology
The thread actually provides a fascinating historical pattern.
One commenter points out that humans have repeatedly believed they were close to understanding intelligence — from ancient theories of the mind through phrenology, IQ, genetics and AI — only to discover that the problem was considerably more complicated.
Another commenter invokes the opposite historical pattern: technological progress can be exponential rather than linear, citing Kurzweil's long-running argument for accelerating technological returns.
Both observations can be true.
That's the important part.
Humans are notoriously bad at predicting nonlinear change.
We tend to:
overestimate the short term + underestimate the long term.
We've seen versions of this with:
- electricity
- automobiles
- aviation
- computers
- the Internet
- smartphones
- cloud computing
- robotics
- biotechnology
- nuclear power
- cryptocurrencies
- autonomous vehicles
- AI
The lesson isn't:
> “Technology hype is always wrong.”
Nor is it:
> “This time everything is different.”
It's:
> Technology adoption is messy, nonlinear and constrained by economics, infrastructure, regulation, organizational behavior and human behavior.
And that's extremely important for your market.
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3. The biggest mistake is making your business dependent on an AGI prediction
I would not position yourself as:
> “I know where AGI is going.”
And I wouldn't build your consulting around:
> “AI won't replace humans.”
You can't know either proposition with sufficient confidence.
Instead:
Position yourself around the transition.
Something like:
> AI is changing what people do. I'm interested in what humans should still do — and how businesses can make the transition without destroying the value they're trying to create.
That's much stronger.
Because it remains relevant if:
- AGI arrives in 2028
- AGI arrives in 2045
- AGI turns out to be a misleading concept
- agents become dramatically more capable
- LLMs plateau
- another architecture overtakes LLMs
Your thesis survives all of those.
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4. And here's the really important economic shift for SMB B2B
AI changes the economics of cognitive labor.
Historically, if an SMB wanted:
- research
- prospect lists
- competitive analysis
- market research
- personalized emails
- proposals
- presentations
- content
- CRM administration
- meeting summaries
- account research
- sales enablement
…it needed people.
Now a significant portion can be produced by software.
That means something unusual happens:
The supply of information explodes.
And when information becomes abundant, information itself becomes less valuable.
That's already happening.
There are going to be millions of:
- AI-generated LinkedIn posts
- AI-written emails
- AI-generated proposals
- AI-produced research reports
- AI-generated sales sequences
- AI-created websites
- AI-created videos
- AI-generated “thought leadership”
So the scarce resource increasingly becomes:
attention → judgment → credibility → trust → decision-making.
That's your opportunity.
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5. Your biggest opportunity isn't “AI consulting”
I would be careful about calling yourself an AI consultant.
That's becoming crowded very quickly.
Your better territory is:
Business development in an AI-abundant economy.
That's different.
You understand sales.
You understand relationships.
You understand SMBs.
You understand the uncomfortable reality that technology doesn't automatically produce revenue.
And you can help people answer:
> “Now that AI can do more of the work, what should my people actually be doing?”
That's a much more valuable question.
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6. The human value doesn't disappear — it moves
This is probably the most important strategic insight I'd take from the entire conversation.
AI doesn't necessarily eliminate the value of humans.
It moves the value chain upward.
Think about it this way:
Yesterday's value Increasingly commoditized
Finding information AI
Summarizing information AI
Writing first drafts AI
Basic research AI
Generating prospects AI
Basic outreach AI
Administrative CRM work AI
Producing content AI
But:
Increasingly valuable Why
Deciding what matters Judgment
Asking the right question Context
Understanding the customer Human insight
Establishing trust Relationship
Recognizing political/organizational reality Experience
Challenging assumptions Judgment
Knowing when NOT to automate Judgment
Taking responsibility for a decision Accountability
Getting people to agree Human behavior
Creating commitment Leadership
And that's almost exactly where good business development has always lived.
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7. Which means your “Defender of BizDev” idea is more relevant, not less
This is where I think you've stumbled onto something considerably more interesting than simply becoming another AI educator.
Your argument can become:
> BizDev isn't obsolete. The low-value parts of BizDev are being automated.
That's a completely different proposition.
And it avoids the ridiculous binary:
AI vs. humans
The real competition is:
humans using AI vs. humans who don't.
But there's another layer:
humans who use AI without judgment vs. humans who use AI with judgment.
That second distinction could become central to your brand.
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8. I would add a third category to your “Bored / Confused / Invisible” audience
You've already identified:
Bored
Good people trapped doing tactical grunt work.
Confused
Good people overwhelmed by AI and technology change.
Invisible
Good people who know something but don't know how to become visible.
I'd add:
Devalued
People who are discovering that the things they used to get paid for are becoming cheap.
That's going to be enormous.
A salesperson who spent 20 years becoming excellent at:
- research
- presentations
- proposal writing
- email
- prospecting
- CRM
- sales collateral
may suddenly discover that AI can do much of it.
That's psychologically destabilizing.
But their experience hasn't necessarily become worthless.
The question becomes:
> Where does that experience create value now?
That is a terrific consulting conversation.
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9. There is a massive “human transition layer”
This is where I think your phrase “human transition layer” has real legs.
Businesses are going to have technology.
They are going to have AI.
They are going to have agents.
They are going to have automation.
They are going to have data.
But somebody still has to answer:
> What should we actually do with all of this?
And SMBs are particularly vulnerable because they don't have armies of:
- CIOs
- AI architects
- transformation consultants
- data scientists
- change-management specialists
- enterprise architects
They need someone who can translate:
technology → business problem → customer → revenue → human behavior.
That's much closer to your sweet spot.
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10. The other huge force: trust becomes more valuable as synthetic content explodes
This is the piece I think many AI conversations miss.
Imagine a world in which everyone can create excellent-looking:
- websites
- proposals
- videos
- presentations
- sales emails
- case studies
- LinkedIn posts
- market reports
The problem becomes:
How do I know what's real?
The answer increasingly becomes:
relationships and reputation.
That's extremely good news for someone whose philosophy is consultative selling and customer success.
Because you're not selling:
> “I can generate more stuff.”
You're selling:
> “I can help you figure out what is actually worth doing.”
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11. And this is where I think the opportunity direction goes
I'd rank the emerging SMB B2B opportunities roughly like this:
1. AI-enabled business development
Not AI education.
Using AI to improve actual revenue-producing behavior.
2. AI + sales judgment
Teaching sellers where AI helps and where human judgment must remain in control.
3. AI-assisted customer discovery
Better questions.
Better research.
Better preparation.
Better listening.
Better synthesis.
4. Human/AI workflow design
Not “automate everything.”
Instead:
What should the machine do → what should the human do → where does the handoff occur?
5. Trust and differentiation
Helping companies avoid becoming indistinguishable AI-generated competitors.
6. Fractional strategic BizDev
SMBs that can't afford a sophisticated full-time business-development leader may increasingly need someone who can provide judgment without a giant consulting engagement.
7. Community
And this is where your LinkedIn/community idea becomes strategically interesting.
People don't just need information.
They need other humans navigating the same transition.
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12. Your consulting offer could therefore become much sharper
Instead of:
> “I help SMBs improve sales.”
I'd explore:
> I help SMB B2B teams figure out what AI should do, what people should do, and how the two should work together to create revenue.
Or, in your more David-ish language:
> AI can do more of the work. That doesn't mean it knows which work is worth doing.
That's a very good positioning line for you.
Another:
> Don't automate your advantage.
And another:
> The question isn't whether AI can do the job. It's whether it can create the outcome you actually want.
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13. Your “Reverse BANT” concept gets more important
This also strengthens something you've already been developing.
Traditional qualification asks:
Budget? Authority? Need? Timeline?
But in an AI-disrupted environment, I'd increasingly ask:
What changed?
What actually matters?
What can AI do?
What can't we trust AI to do?
Where does human judgment matter?
What outcome are we actually buying?
That's much closer to the future of consultative BizDev.
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14. The economic wildcard you shouldn't ignore
There are forces beyond AI.
The next decade is likely to involve some combination of:
- AI-driven productivity gains
- labor displacement/reallocation
- aging workforces
- demographic changes
- geopolitical fragmentation
- reshoring/onshoring
- energy constraints
- enormous infrastructure investment
- cybersecurity risks
- regulatory changes
- concentration of technology power
- declining cost of certain kinds of intellectual work
- increasing value of scarce human capabilities
That means “sales” itself will change.
Companies will constantly be asking:
> Should we hire?
> Should we automate?
> Should we outsource?
> Should we use an agent?
> Should we buy software?
> Should we retain a human?
> Should we change the process entirely?
That's a business-development problem, not merely a technology problem.
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15. The most important recommendation I'd give you
Don't try to predict the future.
Become useful during the transition.
That's your lane.
You don't need to know whether AGI arrives in 2029 or 2050.
You need to understand:
What changed?
What is becoming cheaper?
What is becoming more valuable?
What are customers afraid of?
What are businesses getting wrong?
Where does human judgment still create disproportionate value?
How does that translate into revenue?
That is an extraordinarily good niche for an experienced B2B BizDev person.
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And my blunt recommendation for your brand
I would move you away from “AI pundit” and toward:
The business-development guy who helps people navigate the human side of technological disruption.
AI is the current catalyst.
It isn't your product.
BizDev is your product.
Judgment is your differentiator.
Trust is your moat.
Revenue is the outcome.
And AI is the force changing the playing field.
That gives you a position that remains valuable whether the AGI optimists, skeptics, or everyone in between ultimately turns out to be right.
The LinkedIn conversation itself inadvertently makes that case: it contains radically different predictions, competing definitions of AGI, skepticism about LLM reasoning, arguments for exponential technological progress, and a recurring question about economic value relative to investment.
You don't need to settle that argument.
You can make money helping businesses operate intelligently while the argument is still going on.
And frankly, that's a much more defensible business than trying to predict when the robots arrive.
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