Rita Sheth — Founder, Product Architect & Strategist

At MercuriDash we are in the middle of a shift from being a platform to being infrastructure. As a platform, product strategy lives in one place and you have to come to it. As infrastructure, product strategy is something any agent can call, over any interface, and in return the agents are called on, connected and integrated at MercuriDash. The product stops being a destination and becomes a capability that lives wherever the work is happening.

This is what it means to truly live in a connected world. It is no longer about integrated tools, but about working seamlessly with them across any of our favourite interfaces, with agents finding and applying data wherever it lives, and applying it in appropriate ways. The value moves from the place where the data sits to the intelligence that knows what to do with it.

Viewed this way, platforms are probably short-lived, because we no longer need fixed workflows. Workflows are evolving into custom workflows: each person's own path through the work, shaped by what they are trying to do rather than by what the software decided in advance. Agents make that possible because they can compose the steps on demand instead of following the one flow a product ships with.

The second big shift follows from the first: it is a move from execution to strategic direction. Where can you add value when anyone can get and analyse data? The loop is recursive: once you can get and analyse the data, you can suggest directions, and eventually execute on them autonomously. For our product decision layer this becomes connective, callable, transferable and always on. And the same pattern applies to many other areas. Imagine an always-on CFO fetching data, tracking live spending and suggesting what you should spend that day. Or always-on counsel: live, real time and proactive rather than reactive.

This is the emergence of a new kind of AI, more than agentic. Agentic implies agents carrying out tasks on our behalf. What comes next is less a tool you operate and more a standing capability you consult, one that watches, connects, reasons and acts across everything you already use. It does not have a name yet. We need a new name for it.

Everyone keeps saying there is no moat, that general AI is eating everything. But there will always be opportunities to build moats. I am talking here specifically about technical moats, particularly the kind that develops when a company gets really good at one specific problem.

Companies can base a moat on owning proprietary data on how their users use their platform - but knowing how people use a platform is only useful and moat-creating when it is applied to active outcome improvement. With general AI encroaching more and more on things like memory and context, the moat is not simply whether you hold your company's context and can reason with it. It is how deeply you understand not just the brand, but the brand in the context of a category, market and space, based on thousands of relevant actions and their outcomes. Having general or even brand-specific context is not the same as having accumulated expertise in a particular domain.

General AI is general because it learns across broad sets of knowledge and tasks, with the aim of improving general reasoning. It can handle context too and hold user memory - but there is a distinction between specificity at the user level, where you understand one client's circumstances and particular problem, and specialism at the domain level, where you learn deeply within one area across many relevant cases.

The combination is powerful: interpreting a client's specific context through accumulated domain expertise. Where that involves learning across clients, it needs to be permissioned and sufficiently generalised so that IP and privacy is safeguarded, but this is the foundational premise of much specialist AI, that the category network effect will in turn fuel better outcomes over the network.

General reasoning is also not the same as proprietary learning and decision methods. How much weight should something carry? Which factors matter in this particular use case? What errors in reasoning lead to suboptimal outcomes, and what should change as a result? Those are questions that can inform updates to models and processes around a specific problem. A general model does not automatically possess a company's exclusive feedback, outcome history or methods for making those decisions. It can use agents to connect to more context but it relies on those connectors doing their job.

So today's moat is applying proprietary data and processes relevant to one area and one point in a workflow, and getting really great at that. It is not just creating something that performs better than general AI with context inputs or surface-level reasoning deployed through prompts. It is how that system develops, reacts and evolves through learning, and whether the improvement it produces is difficult for someone else to reproduce.

And that is just the technical moat. Operational advantages, distribution and network effects can create moats too. General AI becoming more capable does not mean every source of defensibility disappears.

The founder's journey is a unique path because, to succeed, founders often have to deny themselves the very things that people most value. Quite sanely, most people value peace, fun, leisure and personal relationships. Founders are not a monolith, they are all different to each other, and of course they are not robots, so they do value these types of things and understand they are good things to experience.

However, because they are driven by both a broader vision and a frighteningly narrow focus at the same time (a focus on a future no one sees, and a tunnel-visioned, short-sighted approach to the present), they approach life, decisions, relationships, pleasure, enjoyment and satisfaction from a fundamentally different point of view. It can look like these things are not important to them. They can seem odd, because they appear to enjoy things like hard work and suffering (which are seen as 'bad') and shun things like fun and leisure (seen as 'good').

But the truth is, it's not that they can't see that things like leisure and personal relationships are positive things to experience. It's more that they just organise priorities differently on a consistent basis.

What this very different approach to life means is that founders, over a long period of time, end up leaving things behind. The shift in priorities, and a different set of decisions arising from those priorities made again and again, lead to a shift in identity, core beliefs and fundamental values. And because people are drawn to and resonate with others who share similar values, it's no surprise that entrepreneurship redefines and restructures connections to people and environments.

For founders who haven't already experienced this, there may be people who become angry, remain in denial about shifting priorities, call them snobbish or selfish, or are simply baffled by how much they have changed. And the truth is that they have changed, even without realising it. It happens slowly yet surely, and is often more immediately apparent to others than to the founders themselves.

The change is incremental, not sudden, because entrepreneurship changes core identity and establishes a new set of habits and responses only over a long period of consistent action. It also happens not just because of small, consistent changes in behaviour, but because of the things founders go through: pain, rejection, lack of support from people, and constant learning. All of this leads to fundamental changes in core values and shifts in identity that aren't a conscious choice, but a by-product of the journey. This is what moulds founders into new characters.

Choosing to stay in to work, rather than going out at the weekend, for example, leads to certain consequences. Making these kinds of choices again and again leads to compounding consequences, until it becomes inevitable that founders end up in a totally different place from where they started. To take another example, learning about their core procrastination triggers and having to break those habits, or learning who in their old environment felt threatened by their attempts at success, and refining their understanding of people and the world, are all examples of learning as a by-product of this journey. That learning shifts their values and identity and leads them to leave things behind: not just people and places, but their old selves too.

At some point there may be a grieving of an old self, an old life and relationships that didn't make it. Then there may be a long period of walking alone, and no one really tells founders when they start a business that this will definitely happen to them, in small or bigger ways. The walking alone part is the bridge between the old self and the new life. It is not pleasant. It's hard, and it's where a lot of painful pruning and hard-learned lessons take place. But on the other side is a fresh perspective on what they experienced and where they came from, a viewpoint from which they can look back and appreciate that although life isn't necessarily better, it is right, because if they were following their path, it was always meant to be this way.

For a long time, how many people you employed said something about your startup's success. Headcount meant status. Investors used it as a proxy for traction, journalists used it as a proxy for importance, and founders used it as a proxy for progress. Even inside companies, the size of your team signalled how seriously people should take you and your contribution.

AI is doing away with the need for many different functions, and it is changing the types of people you need and when you need them. You could even argue that too many people are now a sign of inefficiency. Rather than being applauded, a big team should be questioned by investors and clients, because the extra costs and overheads reduce returns on one side and push up pricing on the other. When the two biggest reasons companies hired in volume, shipping the product and selling the product, are both being rationalised, team size is no longer a useful way to judge progress or potential. Arguably it never should have been.

When AI fully streamlines these functions, what is left that genuinely needs headcount? Probably a much smaller list than most org charts suggest: perhaps domain-level judgement, perhaps relationships, perhaps creativity, strategy, and organising resources and roadmaps. Much of the rest may just be a hiring habit, and a way of spending investors' cash because that's the way it has always been done.

I suspect the result is not just leaner startups, but a different idea of what a company is. When scaling revenue no longer requires scaling people, successful companies may stay surprisingly small for much longer.

Dario Amodei, the CEO of Anthropic, has predicted that we will soon see the first one-person billion-dollar company. A few years ago that would have sounded absurd. Now it sounds inevitable. AI scales in a way that teams cannot. It offers a one-to-many return on investment: build a process or system once and it delivers over and over again. Training people does not work that way. People leave, and their expertise leaves with them. Even with the most codified processes, someone new still has to learn them.

Scalability isn't the only reason a lean structure beats a larger team. Lean is simply better when technology moves this fast. The landscape changes every few months, and the companies that win are the ones that can respond quickly. The more people you have, the more alignment, process and politics you need before anything changes direction. Too many people can mean an inability to adapt, and right now that is a liability rather than a strength.

Being lean doesn't mean being alone, though. Even if you are building on your own, you can still benefit from working with people. They just don't need to be traditional hires in a traditional startup set-up. I think we will see more loose coalitions, networks, contractors, agencies, board members and advisors. They will fill crucial gaps, but also provide the camaraderie, support and resilience that are still needed, both for the business and for the people in it. We still need to work with others, and ideas and connections are still traded. What changes is how influence grows. It used to spread downwards and inwards, through employee numbers and culture inside the organisation. The future may be culture and influence spreading sideways and outwards.

All of this matters when having discussions with investors and with enterprise clients. One of the things investors used to judge a company on, team strength and a balance of specialisms, arguably matters much less now. Instead, you need to show them that one person driving is actually better than decision by committee, because speed and adaptability beat diversity of experience when AI has covered so many of the gaps a broader team used to fill. On the sales side, it can look intimidating when a small team seems to be batting above its weight with enterprise clients. But it makes sense, and clients can be reassured when you explain that automation handles much of the operations, which leaves more time for high-level work and for them.

My prediction is that in a few years, we will be in a new era where team size is just not discussed anymore. Non-technical founders used to be told they needed a technical co-founder. That is now rarely an issue, and it has largely been forgotten as a criterion for judging a startup's success. In the same way, team size and dynamics will no longer be pertinent. What investors and clients will be asking (and what they should be asking) is: how much technology are you using to drive returns, results and innovation, and where headcount is used in place of technology, why that is better? In effect, you will have to justify why each person matters, what their role adds, and why investor money or client trust should move in that direction.

Agents rationalising software development is now yesterday's news, however the new wave of automation is the rise of agents helping with sales and marketing. Traditionally we needed to scale sales teams to scale growth - the two were more or less directly correlated in a very 1-1 way. A lot of GTM needed a sales team to just physically ramp up the amount of outreach you could do, which was supposed to directly feed the bottom line and revenue generation. However, if GTM can instead scale with agents then this opens up, even further, the number of companies that can stay leaner longer and the amount of value that can be generated within smaller teams.

We can already reduce tech team sizes by using AI for much of the work that needed a separate front end developer, backend engineer, designer, dev ops and more. Now AI can do much of that work with some human checks and balances. Sales was always thought of as a fundamentally human skill. But what if you could train your AI to send personalised contextual outreach, follow up properly and do everything up to and including booking a call (and perhaps even taking the initial one?). What possibilities does that open up when one of the core constraints and bottlenecks to scaling-up was the size of your sales force and the speed at which you could ramp them up, train them and get a return on that hire and training period?

The possibility is exciting when most people just take it as a given that at least 30-40% of a batch of reps will churn or underperform, and accept that hiring sales teams is notoriously inefficient, costly and frustrating. If AI enables scaling sales, with all of its constraints and inefficiencies, not just for the initial funnel stages, outreach and lead generation, but the actual sales process, with much less friction, less training and time-lag to results, and perhaps most importably a repeatable, easily systemisable process, scaling becomes predictable rather than the wobbly and unstable ladder it has hereto been.

Sales is thought of sometimes as the last area where only humans can be effective, but consider this: it may actually be that in some cases people may actually prefer to buy from an AI, because they can ask questions frankly, don't feel manipulated or pressured and feel more comfortable saying yes or no. This may actually make for an even better and higher converting sales process, because sales isn't usually about convincing someone to buy, or deploying your skills of persuasion, a lot of times it's about answering questions and providing information.

This is all hypothetical and no one knows whether AI sales reps would perform better or worse than human sales reps. If anyone has done a study, it would make for interesting reading to see whether my instinct about humans maybe preferring to deal with an AI sales rep is true or not. However with AI being used now in first round interviews,  the logical leap to using them in first round sales calls doesn't seem that far and the next breakout company may be a company that nails AI driven sales.