“We are leveraging vibecoding to show what a software company can and can’t do, to understand where it fits in the value chain and to understand whether it is the actual code that is the defensible part of the business or something else,”
Though your point that Twitter's value was never in the code stands.
ALL PROGRAMMERS and VIBE CODERS use AI against itself ... vibe code systems that forces AI to pay all of humans for our content! Without our content AI is irrelevant! Why are we giving our content/souls to it freely now so it thrives and we do not??
"Private equity investors are turning to AI-generated replicas of software to assess whether acquisition targets have a competitive advantage
[...]
'We are leveraging vibecoding to show what a software company can and can’t do, to understand where it fits in the value chain and to understand whether it is the actual code that is the defensible part of the business or something else,'"
Management/Strategy Consulting is different from Implementation Consulting (eg. BAH building and operating a .gov site).
You didn't even want the same kind of guy or team doing each one before ai. Turns out, AI is great at prototyping, ok at productizing and terrible at maintenence and scaling
A product where the secret sauce is basically a distribution play is going to need a different strategy and demand a different valuation compared to a product where the platform itself is successfully monetizing on a workflow.
At a minimum, this gives Bain more leverage when negotiating with these companies.
How?
Do you mean by AI? I haven't seen any evidence of this.
It was always possible to get code generated at large volumes for low cost (offshore/outsource market) but we didn't see this upend or replace many (if any) software companies. In my experience it had the opposite effect - companies that replaced talented internal teams ended up suffering.
LLM generated code is similar, but arguably more expensive and lower quality.
We don't see LLM generated product replacement at scale because code generation is a problem, but it's not the only problem. Low quality can kill a product, but high quality doesn't guarantee success.
There's an entire ecosystem around a successful software offering. An ecosystem that depends on adequately functioning code.
LLMs may be useful for certain tasks (...maybe... - we've always had good options for repetitive code generation) but I certainly wouldn't describe it as "very easily replicable".
> any evidence of this.
"Any evidence" you say? I think something has moved.
As of Autumn/Winter 2025, I can say "Here's a complete enough spec of what I want cloned. Crank on it for a few hours. Give me the clone site as I specified." And frontier agentic tooling does a hands-off YOLO job really well. (Notice: I set it up with good specs/rules to scaffold.) Cost maybe an hour of my time to set up/scaffold, and 3 hours cranking on its own, on a $20 or $60/mo sub.
I think taking the same problem to an "offshore house" (or even Fiverr or whatever) would probably easily cost 10-100x more, and quite possibly with worse (less reusable or less best-practices code quality or less functional or less clean) output overall.
So I think your OVERALL point may stand. I'm just nitpicking the specific aspect quoted above, and maybe to some degree the aspect of
> "LLM generated code is similar, but arguably more expensive and lower quality"
Which to be fair obviously depends on what code one is comparing.
I used to say "nah, it can't replicate apps that well by itself", but then I tried it (on the advice of a fellow commenter here on HN), and was surprised myself.
Easy to imagine there are a lot of software products that could be cloned and out-competed by taking 15% profit margin instead of 50%.
Implementation Consulting is always a separate practice under a seprate set of partners and leadership from Strategy Consulting.
Also, BAH doesn't really do Management Consulting anymore.
What Bain is doing is basically testing whether the valuation of acquisition targets actually makes sense or not, which is different from what you are talking about.
Anything substantially new will still require an engineer in the loop. Specifically, if a new design pattern, archtecture, etc is required. AI can only build on its training material - it can't yet have original thoughts.
Oh, so it's only good for 99.9999% of all software?
And people will say things like, "it's limited by training data" without any mention of just how VAST that data is.
While you're here, can you give us some "original thoughts" to demonstrate your ability over Claude?
The more likely scenario is that they "consume" the output of designated offshore teams.
It has always been overpriced and had huge margins.
I think what management consultants are really afraid of is being replaced. By AI.
This is the engineer’s take on things. I am entirely sympathetic to it.
I also think it missed a lot of what management values in consulting. At its best, you can offload a lot of things unrelated to your business to people who are experts. At its worst, you’ve offloaded the blame to a group of over-worked twenty-something’s with impressive degrees who have no idea what they’re doing, but who sound really fucking confident about it.
Can an internal team do it better? Probably. Will they be cheaper? Probably. Will they assuage management’s anxieties and deflect some/all of the blame? Nope, not at all.
A quick addendum: The in-housed money furnace can produce material to reinforce and extend the foundations of information technology
So, yes, asking an AI to secure your app is going to be much better than what the average dev churns out.
If your argument is that LLMs are more effective and less costly than typical low-quality outsourced code, I generally agree with that, depending on the details.
For prototype/POC stuff, absolutely.
For long term deployable product stuff that needs to be maintained, supported with SLAs etc... eh, like everything, it depends on the people/team.
I've been leading dev teams for around 25 years, and I tend to think in terms of total cost of code.
Cognitive debt is a real thing. The cost of a developer that leaves the team, and the struggle to have someone take over their work is real.
With LLMs you have all this hidden cost, plus no "theory of mind", which makes the cognitive debt much worse.
Combine that with atrophied skills, subtle bugs, architecture facepalms, security issues, etc... and the total cost of LLM generated code is a lot higher than just a subscription price.
- A TDD loop where the Indian QA team played the role of the tests. The engineers would yeet some broken code at the end of the day, the poor QA testers would click through all the broken interfaces, and then the engineers would fix it the next day.
- A release process that was so slow and hellish that everyone just went to the DBA to have him add a stored procedure to implement their feature. He could get it in for you the next day.
- A frontend framework discussed in hushed tones, being built by a mysterious monk-like engineer, which was going to be the client's big secret weapon. In reality it was a terrible version of React built on top of jQuery.
- A core in-group of backend devs (most of these guys had advanced degrees for some reason), who would stay late every Friday, going through heroics to do a release of the client's email-templating app. There would be then be lots of back-slapping and congratulations the next Monday for these geniuses who were keeping the business afloat.
- "when in doubt, set timeout". They didn't know about callbacks
Usually consultants are brought in when upper management can tell that there is something very wrong and they can't fix it within the chain of command of their full-time staff.
Having done a stint in consulting, most internal software teams are useless, that's why these companies hire outsiders. They are sick of having to deal with stubborn teams who think they know everything, and refuse to change. You can see that mentality in these threads.
I'm aware — I've used LLMs to find vulnerabilities, myself. But it doesn't follow that because AI can find them that AI can find the optimal fix, because fixing vulnerabilities often involves tradeoffs.
Also, can we please have a civil discussion?
But since you bring it up, those of us that have been around a while have used many code generation tools that took care of tedious work like model creation, forms, validation, class creation, CRUD, component creation, project skeletons, starter templates, visual design tools that generate code, etc... etc... basically most of the stuff that LLMs are good at. Simple things that are tedious and don't require much thought - the sorts of things that statistical pattern matchers like LLMs are good at.
The rest of it, the stuff that requires thoughtful architecture and reasoning is where LLMs fall flat and mostly end up costing time. The research supports this.
So yep, my face is indeed quite straight.