And Vijay Says…

This is the report that wiped out $200B of market cap the following day. https://www.citriniresearch.com/p/2028gic

First – I don’t blame the authors one bit. They painted a scenario – they didn’t predict that’s what will happen. CEOs of the frontier labs and hyperscalers have all painted their scenarios and we were all cool with that. So why not this one and more like it?

Second – I absolutely blame economists and professional investors for being stuck in the past. They have been lazy doing just the first order impact analysis of AI ( picks and shovels will be of great demand in a gold rush so buy Nvidia stock ). That doesn’t even justify their plane tickets to go to Davos and pontificate on AI annually at WEF. Their lack of rigor in their field is what I attribute the current volatility to the most.

I am just an engineer who plays with AI. I have some experience running parts of the P&L for larger companies. I am not an economist by any stretch of imagination. I am going to jot down my thoughts on the scenario painted by the report mostly so that I can come back in 2028 and read about it. So please take it for what it is worth.

As always, these are just my personal opinions.

I will start with the conclusion and explain why I think so across a few bullets. I think the chance of the economy crashing in 2028 because of AI is completely improbable.

Here are 6 reasons why

1. History has always rhymed – every single technology shift in the market has led to more employment and the kind of roles it creates were not known when the tech first came around. That has been the case for Steam engines to ATM to internet.
2. There will be a market driven spending brake. If there are no people to buy products, there won’t be runaway AI investment either. This idea of firing people and investing in AI is not an endless loop like the report says – there is no economic logic that supports it.
3. If and when AI makes everything from food and housing to sneakers cheaper – workers won’t need to make as much money to keep their current standards of living. A four day workweek is a likely outcome in 2028 than economic collapse
4. Just look at public cloud adoption in large companies to get a hint on how regulations and bureaucracy slows down tech by decades. On top of that there are labor unions, courts etc as well that will cause further friction that slows down adoption. The talk on AI is miles ahead today compared to the walk of AI. The layoffs we see are largely from past over hiring – not because AI has taken over jobs yet. It’s just a convenient excuse for companies to shed cost now.
5. White collar workers – not all but many – have access to capital market directly or through their 401(k), IRA etc. The wages they might lose in the doomsday scenario will easily be offset by the capital appreciation they get from Nvidia etc booming if the scenario indeed plays out with the Ghost GDP. Cash is fungible – doesn’t matter a lot which way we earn it . It matters for tax etc but you get the point
6. How likely is financial contagion? If it’s Amex and visa getting disrupted by AI agents who prefer solana blockchain – there are plenty of regulations around KYC/AML that make it happen. Mortgage crisis is a real potential in my mind – it can only be partially offset by the access to capital market that some of the mortgage holders have. But because of the spending brake I discussed above – I don’t see a full blown disaster

So back to who is to blame for this volatility.

Economists need to change their 20th century ways of thinking and start modeling the future. I would like to think the good ones have already started and perhaps just were late in starting. It makes me wonder if economics education model itself needs urgent change. The world we live in is moving so fast and picking up speed that we can no longer just be happy with first order impacts modeled.

Same goes for professional investors. You are playing with the money of people who trust your knowledge and competency. If one doomsday scenario paper is enough to spook you to act this way – you need to reevaluate your investment thesis from first principles

AI may or may not change the economy drastically – but I sure hope it does change the practice of economics and professional investing for the better.

I read this yesterday https://www.hindustantimes.com/india-news/aisummit-2026-it-bpo-services-will-disappear-in-five-years-says-venture-capitalist-khosla-101771182147907.html

Vinod Khosla is a man I deeply admire, and hence when he says something I pay very close attention and spend some time thinking about it. He has seen computing evolve up close and is quite bold with his investments.

His underlying thesis for saying IT and BPO will go extinct is based on AI getting far superior to humans in the next few years. I agree with the rate of progress – with the kind of capital being burnt through, it is only fair to expect that the breath taking innovation in AI will progress .

That said – I don’t think his predictions will come true in the 5 year time line he is putting forward for a few reasons. I have ten things in mind that makes me believe that to be the case.

As usual, these are strictly my personal views and not of my present or past employers.

1. Not all BPO and IT deals are FTE based. FTE based model of course will get disrupted much like SaaS models based on seat based pricing is already getting existential threats. The difference is that unlike SaaS where a minority of companies have consumption and outcome based pricing, BPO and IT have quite a lot of outcome based pricing already. Khosla probably is referring to just the FTE based models which is just a subset of the industry. My thesis is that AI will just help IT and BPO providers shift even faster to a fully outcome based model which will be awesome for their clients. Business leaders largely care about guaranteed outcomes when the push comes to shove.
2. There is a lesson on agent proliferation that we should keep in mind from micro services. A decade ago or so, we fell in love with micro services and a lot of IT shops embraced their elegance. In a couple of years, we realised that it’s a real headache to deal with a lot of services and the scaffolding needed to operate them in an enterprise grade fashion took several more years. The basic infrastructure needed for a lot of agents running loose in an enterprise landscape is quite immature today and such platforms don’t happen overnight .
3. Enterprise inertia is a real thing. There is hardly a CIO I know in financial services who hasn’t told me about their goal of replacing mainframes and moving everything to public cloud. And yet – mainframes are still alive and thriving and most companies haven’t moved even half their workloads to public cloud. Change is quite hard in enterprises on all fronts – people, process and technology all usually have secondary and tertiary effects if changes happen quickly and hence corporate leaders tend to move deliberately. They won’t risk breaking things when moving fast. I am not saying that this is a good thing – trust me I have been frustrated all my career with this kind of inertia but I understand why senior leaders are careful
4. Enterprise buying models don’t change fast either. FTE models are considered favorites by most procurement teams because of the ease of managing such contracts – they are easier to negotiate, execute and monitor even if their value is less than outcome based contracts. It will take a lot to switch this behaviour to outcome based models in a mainstream way. Now imagine the challenge of moving this to a software license model !
5. Law making almost never keeps pace with innovation. Laws are written with human actors in mind. If a human accountant does tax fraud – they go to jail. There isn’t an equivalent way today to keep agents honest. The best solution we have is to have humans in the loop. Granted it doesn’t need every human to stay around – but many will be needed to keep AI compliant
6. Jevons paradox can’t be forgotten . For foreseeable future, there are plenty of use cases for AI and I don’t see Jevons paradox failing. So as AI becomes more and more efficient, companies will push even more use cases into production which will need even more humans to be around.
7. GDPR and DPDP type laws won’t allow seamless cross border autonomous workflows. Sovereign AI is important and is here to stay. That essentially means there will a bunch of cross border workflows that still need humans on either end to make it flow
8. BPO has a lot of last mile aspects that are not yet AI friendly. While a lot of the work is repetitive and easy to automate via AI, most enterprise workflows have a last mile part that needs soft skills to navigate the enterprise nuances. Maybe it’s possible to automate some of it over time by reimagining from scratch – but we are not taking about 5 years in this case
9. As long as LLM hallucinates, some human needs to stay in the loop. Yes you can reduce hallucination to some degree – but at its core LLM is autoregressive and unless a very different architecture emerge from research community, we can’t put too many things into production without humans in the loop. Reducing hallucinations is not cheap – the very big models have high inference cost. RAG type solutions have limitations and are expensive to maintain as enterprises evolve.
10. LLMs are static learner’s unlike humans . LLMs are not sample efficient like humans when it comes to learning – we don’t need thousands of cat pictures to know what a cat looks like. Once they are trained, they don’t learn on the job the way a human does. A human BPO agent can be told that they are wrong and need to do a task another way. The way to change an AI agent to act like that is not compute and memory efficient today. When we say an LLM remembered what we said earlier, what we mean is that a layer around it feeds the content of past conversations back to it behind the scenes every time which is quite inefficient and expensive. Short of fundamental research breakthroughs, we will need to keep coming up with better engineering hacks for efficiency.

If you work for a reasonable amount of time, the odds are high that you will work for some bad managers. I certainly have had what I consider a more than fair share of bad (and a couple of absolutely terrible managers). While I have felt anger, frustration, sadness, rage and all that about them for a period of time – I am also strangely grateful for the invaluable experience that taught me a lot about what not to do. I also readily acknowledge that I may not have avoided those pitfalls as a manager myself.

What is a manager’s primary role? At the simplest level – it’s about providing clear direction to the team working under their supervision. In my experience, the one consistent thing I have noticed amongst bad managers is that they had a lack of clarity on where to head. The way they compensated for that lack of clarity was usually why their actions made me think of them as bad managers

Imagine you are quite a good driver trying to drive a car from Phoenix to LA to visit Disney, but you don’t know where LA is relative to Phoenix or when you need to get there. Since you are a good driver – you drive defensively and at the optimum speed to maximize the mileage of your car. You refuse to ask for directions even after your passengers repeatedly beg you to do so. What are the odds that you reach LA? And would anyone enjoy being in that car? Would they ever trust you to even drive you across the street to a McDonald’s after that LA adventure?

Bad managers waste everyone’s time and build frustration because they try to optimize the wrong things and generally doesn’t accomplish the right results.

From an employee’s perspective – what characteristic makes them think of their boss as a bad manager the most?

I would think “micromanagement” wins that prize in a landslide.

There are two reasons managers tend to micromanage for extended periods of time in my experience

1. They don’t have a great vision and hence the only thing they can manage is the process which they are masters of. This is also a major reason why great sellers and engineers don’t always become great sales and engineering managers
2. They don’t have the skills to recruit, coach and manage people for the job at hand – and compensate by their own time and expertise. The day only has 24 hours and you will just run out the clock without having too much to show for the effort.

Most employees will try to – for at least the initial phase of working for a bad manager – ask questions and make suggestions. Unfortunately, bad managers typically are poor communicators. Some will give you answers that are vague and confusing, some only talk at you and won’t make it two way and some will either not talk at all or will only open their mouth for negative feedback. By wasting the opportunity to course correct and/or gain clarity – they make a bad situation worse

What’s the long term impact of bad managers?

If I could choose one word – I would say Toxicity !

They tend to take credit and deflect blame – and over time I think it happens less out of malice and mostly out of ignorance and lack of self awareness. Doesn’t matter why though – the effect on the team morale is the same.

Being a manager is hard – it’s difficult to balance empathy with business needs. But that’s the job – you can’t just shrug it off.

Unfortunately, toxicity sometimes get rewarded when business results are great. That happens more times than it should especially in larger organizations. This is quite simply a leadership failure.

What can we do about this?

Employees with bad managers : First you need to evaluate whether you are jumping the gun and just blaming the manager. I have done it and realized it later. I also have seen it hundreds of times as an up line manager. Having a network of mentors help a lot with getting an objective understanding and also in many cases will help mitigate the situation. Since power is asymmetrical in balance – if your concerns are not addressed, improve your skills and network with urgency and get the heck away from the toxic manger as quickly as you can. This is also why I am a big fan of constantly improving optionality in life – it helps us face adversity with minimum trouble

Managers themselves : Learn to listen – with the idea of understanding and not just to respond. Ask yourself the hard WHY questions. None of us are super objective about ourselves – so try to get 360 degree feedback to the extent you can. Maybe you are a good manager already – the feedback will make you a great manager. There is no downside really to listening, understanding and tweaking what you do.

One of the things that have made me a better manager is a continuous interest in learning the job of my manager more and more. That helps me understand why my boss wants me to do certain things and that in turn helps me give clear direction to my team.

One last thing – being a manager is usually motivated with the idea of making more money than you previously did. There is no shame in that at all. But if money is all you need and you hate being a manager – try talking to your upline managers on whether you can be a highly paid individual contributor. Failing that – look outside the company for such options. Life is too short to be miserable doing things you don’t like every day. As an up line manager, I usually ask a lot of questions when people ask me for promotions and career development advice . 90% of the time it’s just a proxy for making more money and there are many ways to make that money if you have honest conversations with your own managers. You will do yourselves and your teams a big favor.

Upline managers : You really are the biggest culprit if your chain of command has a lot of bad managers. You have the best chance to be objective compared to employees and their bad managers. You need to constantly listen – and proactively find out – how the culture of your team is evolving and make tweaks. If all you do is watch short term business results, you will often not realize the long term damage you do with your inaction. You have to assume that the aggregated info you see probably hides a lot of actual issues and unless you probe actively – you won’t see what needs to be fixed.

I don’t like senior leaders saying things like “the strategy was perfect and it was just an execution failure”. My strong belief on this matter is that a strategy that fails execution is a bad strategy – it just did not consider the constraints appropriately. How many times have we heard top leaders saying “we are not transforming fast enough” when they have not made the right changes and investments down the line to enable that transformation? . I often remind folks “The big boss is called the Chief Executive Officer for a reason – and the Chief Strategy officer works for the CEO, not the other way around”. Don’t get me wrong – you do need a strategy, but it needs to be grounded in reality !