2025: The Year of AI Agents — The Hype, the Economics, the Reality

The CEO of Nvidia, Jensen Huang, has

called 2025 the year of AI agents. The

CEO of Anthropic said that by 2026 or

2027, AI systems will be better than

almost all humans at almost all things.

This year told us a different story. 40%

of Agentic AI projects will be cancelled

by 2027. 90% of Agentic deployments fail

within 30 days. But at the same time,

something remarkable is happening. Agent

AI companies hit 400% YI growth.

McDonald's is cutting on boarding time

by 65% without hiring a single new

trainer. Walmart's agents save hundreds

of thousands of dollars by reducing food

spoilage. Make it make sense. The math

ain't mathing. Or is it? Today, I'm

digging into the business models and

economics of AI agents. The iceberg of

invisible costs. the business models

that seem to be working and

opportunities hiding inside this

paradox. Let's dive in.

So, I started my research by following

where the money in a gentic business

comes from. And there are two emerging

patterns. Business model number one,

agent as a standalone product or agent

as a service. Agent as a service is a

product dominant, a productled business

model, meaning that the agent is a

product. And business model number two,

agent marketplaces. Marketplaces aren't

really about agents. It's a marketplace

business. Just like Uber or Fiber or

Etsy or Airbnb, it's a business of

distribution that follows the rules and

principles of marketplace economics. So,

let's look at what's working. McDonald's

has introduced a voice activated AI

training simulator that guides new

employees through tasks as they do them.

Employees get instructions on how to

make burgers, take and assemble orders.

The system reacts to variables that

happen around an employee in real time

and modifies or adjusts instructions

based on what they're doing. Did it

change the business? Yes. McDonald's is

reporting about 65% reduction on

onboarding time without hiring a single

new trainer. There is a 20% increase in

the number of candidates completing the

hiring process. Moving on, Walmart.

Walmart's got a creative one. A

selfhealing inventory system. The

self-healing part maintains the balance

for the supply chain operations. They

detect demand surges, adjust

replenishment schedules, reroute

products between distribution centers

and also do all kinds of optimization

activities. In Mexico City, it redirects

products from overstocked warehouses to

facilities with shortages. In Costa

Rica, it reduces spoilage of food

because it maps optimal delivery routes.

It even analyzes social media and sales

data and adjusts supply depending on the

products that are trending. And lastly,

Mercedes-Benz and their MBUX virtual

assistant, which is a conversational

agent that they placed into select cars.

A driver can talk to the agent in plain

natural language, and the agent provides

highly contextual, highly personalized

recommendations. things like guide me to

a fine dining restaurant. I cannot wait

for Google Maps to release the same.

If you look at what's happening under

the hood of a system like MBUX, it's not

just one model doing everything. It's a

system of agents built on top of Google

Cloud and Gemini. And that's a fantastic

example of an agentic implementation

that works. It takes a manual

time-consuming process away from the

driver and makes their life easier and

in many ways safer. And Google is doing

the same thing but for businesses.

Google has been quietly cooking up a

beast in the background called Google

Gemini, a space for enterprises. And

they've done it in the Google way. They

don't scream about it. They just drop it

and let everybody else catch up. The

beauty of it is that it provides exactly

what agents are good at. assist,

empower, and support, not replace. It's

designed to help every person in every

organization remove repetitive, boring

tasks and reallocate that time to high

impact work that actually moves the

business. It allows companies use

Google's AI models and readytouse agents

across multiple teams, sales, HR,

engineering, marketing, product. So,

there is a solution for all teams across

the company. And here is the part that

is often invisible. Bringing and

deploying a system like Google Gemini

into your company takes a lot of

infrastructure and change management.

And that's exactly where Promeo comes

in. Promeo is an official Google Cloud

partner that deploys entire agentic

systems like Google Gemini and they

deploy them into enterprises. They take

care of everything from licensing,

setup, training, governance, and team

optimization. Promeo helps companies

turn AI pilots into real production

grade systems. And by doing that, they

overcome the stages where the vast

majority of agent pilots fail, security,

compliance, and deployment. Remember

that 90% failure figure that I said at

the very beginning. So, if you're

looking at it and thinking, okay, but

who's in the 10% that get it right?

That's exactly the space Primo operates

in. If you're leading an AI initiative

or overseeing an entire enterprise

transformation to AI, you can learn more

about it right here at primo.com/ai.

But here is where the story gets really

interesting. The CEO of NVIDIA, Jensen

Huang, has called 2025 the year of AI

agents. The CEO of Anthropic said that

by 2026 or 2027, AI systems will be

better than almost all humans at almost

all things. Well, 2026 is around the

corner. And to all of you who are

watching this, how many of you have been

replaced by an agent? Write it in the

comments. I'll wait.

Yep, I figured as much. This week,

Dwaresh Patel published a podcast with

one of the most influential researchers

in AI, Andre Carpathy, a Canadian

scientist, by the way, who shared a very

sobering assessment of the industry's

progress, including AI agents. He was

very vocal about how agents produced

very brittle and unpredictable results,

and how they lack basic reliability, how

they don't possess reasoning, and how

they don't learn unless you go and train

them by hand. The market of Agentic AI

is not just weekly commercialized. We

haven't even scratched the surface of

where it can go. The agents we have

today are great helpers. They do

automate basic tasks. They can help you

do research asynchronously. They can

eliminate a lot of admin tasks or save

you plenty of time doing routine work. I

can speak for our channel. For example,

those of you who have been watching us

know that we treat this channel as a

startup and the agents really help us

save money on the headcount. We use

Notion Agent, Google Agent, Perplexity

Agent. They are incredibly helpful in

what they do. They format documents.

They create to-do lists. They assign

tasks. They do all kinds of work that we

would otherwise be doing by hand. A lot

of admin work. And that is exactly what

modern agents are good at. basic

repetitive monotonous tasks, but they're

nowhere close to autonomous operations.

They don't have cognitive abilities.

They don't learn and reflect on past

experiences. Everything they do needs to

be checked, validated, and refined. And

what I am covering right now is

individual/s small business use case.

The real revenue lies in B2B. And B2B

adoption is miles miles away from where

it needs to be. It is miles away from a

point when anyone can say that an agent

can realistically and reliably forget

the word replace at least cut somebody's

workload by 20%. Agent adoption in B2B

is very slow and it is slow because

security teams block those deployments

and there is a reason for it. 90% of AI

agents fail within 30 days of deployment

at enterprises because they cannot

reliably or even unreliably handle messy

and unpredictable business operations.

But as I kept researching failed

rollouts, I found something very

strange. The 90% that failed were all

trying to save money and the 10% that

succeeded weren't trying to save

anything at all. The best example is

Harvey. Harvey hit $100 million in

annual recurring revenue in August 2025,

which is a $400 Yi growth rate. They

charged $1,200

per attorney per month. That is 10 times

more expensive than traditional legal

software. They've got 500 enterprise

customers and their customers are

doubling seat count within 12 months.

How is it possible that the most

expensive agents are the most

successful? Harvey's got a high cost,

hightouch model. Yes, they charge $1,200

per attorney per month and they do it

with 12 months 20 seat minimum

contracts. They've got 10% of their team

consisting of ex lawyers making sure

that the firms which is their customers

hit the usage threshold for renewal.

They provide multimodal orchestration.

They position their product around

preconfigured agentic workflows. So they

make it very clear what the agent is

going to do and how it's going to

automate a certain step. Agents make

sense when task predictability exceeds

90%. Agents make sense when decision

logic is simple and when you require

zero errors. However, if each problem,

each instance is unique. If each

instance requires reasoning across

unstructured data, if it involves a lot

of natural language interaction and it

improves through continuous learning,

agents may either not make sense or they

need to be deployed with variable costs.

Harvey's example is striking because

they're in the minority of Agentic

businesses that managed to find a way to

make it work. So, I kept digging into

the budgets to understand why Harvey

worked. And here is what's really

unusual about agents as a business

model. Every software product you have

used, Salesforce, Slack, Zoom, Microsoft

Suite, they all compete for the IT

budget. And the IT budget is usually

around 2% for any company, 2% of their

total spending. But AI agents is the

first technology in history that

competes for the labor budget. That is

60 to 70% of companies total spendings.

Let me put that in perspective with real

numbers. Think about how a typical law

firm spends money. Out of every $100 in

revenue, $45 to $50 goes to labor,

lawyers, staff salaries, benefits, stuff

like that. $2 goes to technology. It's

all kinds of software that that law firm

is using. Harvey isn't competing for

that $2 technology budget. They're

competing for the $45 to $50 labor

budget. So, when a law firm looks at

Harvey's $1,200 a month price tag,

they're not comparing it to the $2 that

they allocated towards technology. They

are comparing it to a firstear associate

at $13,000 a month. That's $150,000

salary plus benefits. When you're

competing for labor budgets instead of

software budgets, expensive becomes

cheap. This leads me to my final

question in this research. Are agents

supposed to be saving money.

Agentic business and agents as a service

are fundamentally incompatible with

traditional SAS economy because in SAS

model once infrastructure is deployed

adding an extra user costs near zero for

the business because software can be

replicated infinitely at minimal cost.

Going back to the examples that I

provided before, at Shopify, every new

business that wants to become Shopify's

customer doesn't cost Shopify anything.

They acquire them at almost zero cost.

In an AI agent model, marginal cost is

very far from zero. But in the AI

business model or AI agent model,

marginal cost is very far from zero.

Every action burns GPU compute and

energy and costs do not trend to zero

even at scale. Compute costs in AI is

the primary variable in aentic products

and they come in two primary ways per

interaction basis and per token basis.

Foundation models like GBT4, GBT 5

charge per 1,000 tokens. But Agentic

systems can consume from five to 20

times more tokens than simple chains.

Because agents have loops, retries,

multi-step planning. Every routing

decision, every tool selection, every

context generation can trigger multiple

LLM calls. And that is what becomes your

multiplier. And going back to our

example, here is what McDonald's doesn't

publicize. The 65% reduction in

onboarding time cost them $12 million to

deploy across 200 locations. That's

$60,000 per location and that is more

than a new hire will earn in 2 years.

The agent in their case did not replace

labor cost. It frontloaded it. On top of

this, there is a layer of invisible

costs that don't count as agentic costs.

But if you do the proper math on how

much it costs to deploy an agent, it

should count. And that bottom of the

iceberg that you don't count is in the

pre-eployment data work. You have to

curate and prep training data because

the agent is only as good as the data it

operates with. In addition to that, you

need rag knowledgebased construction. It

requires embedding generation, chunking

strategy design, and semantic indexing.

On top of it, you have to be really

smart with the amount of context. You

have to dduplicate data sets and get rid

of irrelevant context because if you

don't, your storage can go up close to

25%. And that's more tokens that you'll

be burning through, more unnecessary

tokens you'll be burning through and

paying for it. On top of it, add data

costs, cloud infra that can cost you

between $2 to $10,000 and storage

optimization. This is why agents are not

and should not be marketed as automated

labor.

So we're approaching the end of 2025,

the year of AI agents. And the reality

is there is a huge hype reality gap in

enterprise and business agentic AI

adoption. AI agents come with

significant often invisible overheads.

From technical debt to integration

costs, security is the number one

concern because autonomous systems

increase possibility of cyber attacks

that were not predicted before.

Nevertheless, we are at the very

beginning of a gentic era and even now

there is already a series of emerging

opportunities in Agentic AI. Listen

closely. Agent ops is slowly forming

into a new business function. It is not

mainstream yet, but there has been an

analysis of 3,000 plus AI job postings,

and there are already signs of job

requirements around agents,

orchestration, and multi- aent

workflows. There are already at least 17

agent ops tools on the market, and there

is evidence of major platforms embedding

agent ops functionalities into their

core offerings. Now, one quick thing

that isn't databacked. It's just my

personal opinion. Personal opinion based

on experience and observations. I

personally think that the agent ops as a

function is going to be a lot more

spread than DevOps for example. I don't

think it is going to be such a

centralized function as DevOps because

fixing debugging rerouting agents is

going to be a lot more accessible

accessible to people on non-technical

team and there will be lots of agents

across all kinds of departments customer

support project management finance even

accounting maybe in addition to agent

ops there is a space that I would dare

to call particularly hungry for startups

and ideas and it is infrastructure

layers for agent management. In other

words, tools for teams like agent ops.

There is nothing on the market that can

match the quality of tools that are

available for DevOps. There are

fragmented tools, but there is no clear

market leader. So to all entrepreneurs,

if you're looking for ideas, this one is

still very green, but definitely worth

pursuing because agent ops is definitely

going to be a thing.

We are approaching the end of 2025 and

it wasn't the year when agents replace

people. It was however the year when we

learned what agents actually are. Not

cheap labor, but a new category of

software that competes for labor budgets

instead of it budgets. It was the year

we learned we've got a long way ahead of

us when it comes to Agentic AI. 2026 is

around the corner. Let's see what it's

going to bring us. These are really

interesting times we're building in. As

always, we hope this was helpful. Let us

know what you think in the comments.