AI Is Minting Billions - But Who's Really Winning?

Today, we’re diving into:

Gen AI Isn’t Boosting Profits - Until You Deploy It This Way

Generative AI is not a fleeting experiment. It is a revolution in how knowledge work is performed. But if you're expecting near-term margin expansion from task automation alone, the data suggests you may be disappointed.

A recent study of 800 US public companies found that the industries with the greatest potential for Gen AI automation have not seen commensurate improvements in profit margins.

In fact, sectors such as finance, tech, and media, where Gen AI could automate a large percentage of knowledge work, have experienced flat or declining profit margins after widespread adoption.

Efficiency Alone Won’t Deliver Advantage

The research analyzed job activities in hundreds of different companies, giving each one a score based on how much Gen AI could improve or automate that job. When these scores were compared to profit margins in each industry, there wasn't a clear correlation.

The camera is a great example of this. It didn't make portrait painters more efficient; it changed the entire structure of the industry.

Gen AI could have the same effect. If you simply use it to make your current processes more efficient, you could find yourself hurting your profit margins instead.

AI’s real advantage isn’t automation - it’s converting data and models into scalable revenue.

Book a demo to see the frameworks top teams use:

The Real Opportunity: New Value Pools

Here’s a more optimistic and forward-looking perspective:

“Gen AI is not just a productivity booster; it is a multiplier of capabilities. It enables the creation of products and services previously unimaginable, uneconomical, or too complex to scale.”

But the key change is this:

• No longer wonder how Gen AI can help you save costs.

• Start thinking about how Gen AI can help you unlock new value that customers are willing to pay for.

This is the key takeaway from the HBR analysis: the key is creating a new value pool where AI creates products and services that were previously unimaginable.

A Practical Strategic Framework

As a CEO or business leader, you can also apply a structured approach to this question:

1. Diagnose Commoditization Risk

You should first identify areas where Gen AI could potentially automate tasks in your organization. Next, you should consider the following: 

• If we improve this activity, will our competitors improve it too?

• Will this change impact how much customers are willing to pay?

If the answer to these questions is “everyone can do it,” then improving it simply won't set you apart.

2. Identify AI-Enabled Value Creation

Don't think about simply making your internal operations more efficient. Think about:

• Can we create personalization at scale?

• Can we create high-end advisory services?

• Can we create products from our internal capabilities?

These are opportunities for value creation that can be monetized.

3. Design for Value Capture

Don't innovate without a way to capture a part of the value stream. Once you have identified a new value stream, think about how you’re going to capture a part of it.

• Productization: Leverage AI capabilities as a product.

• Value-based Pricing: Leverage value-based pricing.

• Platform Integration: Integrate AI into a platform.

• Data Advantage: Leverage a data advantage.

The more effective the value capture, the more difficult it becomes for a competitor to attack the business.

Where Gen AI Shows Real Promise

Yet, even in areas where progress may be slowing, Gen AI holds significant potential for upside.

• Hyper-personalized customer experiences

• Decision support in real-time, in complex environments

The key point here is that augmentation, rather than automation, may provide more strategic value to a business. This is because, if AI is used to augment human judgment, rather than replace it, service quality and differentiation can increase.

Those businesses that use Gen AI to enhance their customer experience, innovation, and business models will likely benefit the most.

Why This Is Still Early

We’re still in the early stages of adopting Gen AI.

Early adopters focused on areas where AI was clearly beneficial for efficiency: summarizing documents, creating marketing copy, and writing code. These tasks are simple to replicate; therefore, they do not contribute to lasting competitiveness.

Now, true integration, embedding AI into product architecture, customer interfaces, and decision processes, takes time.

As business leaders develop their AI strategies, the focus is moving from cost savings to expanding capabilities.

That’s a good thing.

Reframe the Question

The question isn’t, "How much cost savings can Gen AI deliver for us?"

What really matters, however, is this: "What new value can Gen AI help us create, and how will we capture it?"

When you think about Gen AI deployment through this lens, Gen AI isn’t about efficiency; it’s about growth opportunity.

For business leaders who are willing to think differently about the value proposition of the industries they serve, the opportunity with Gen AI has yet to be tapped.

The $110B Loop Nobody Is Talking About

The artificial intelligence boom isn’t just powered by code, talent, or breakthrough models. It’s powered by capital, and increasingly, by a tightly woven circle of capital.

Take the latest round of hype: $110 billion raised by OpenAI from three tech titans, led by Amazon. At first glance, this might seem like just another round in the race for AI supremacy. But scratch the surface, and the larger picture comes into focus.

It’s not just an investment - it’s circular funding, shaping the course of the entire AI sector.

The $110 Billion Signal

OpenAI has managed to secure an unprecedented amount of funding, to the tune of $110 billion, this time led by a group of major tech firms, including Amazon, as per a report in The Hindu. It is a clear indication of the level of competition between the major tech firms across the world to gain ground in the AI sector.

Why Big Tech Is Doubling Down

You need to understand the stakes involved. The development of frontier AI depends heavily on the availability of significant computer power, huge data centers, high-end GPUs, and long-range infrastructure investments. Not many organizations around the world have the power to invest in this way.

Not only are Amazon and other companies investing in OpenAI, but they’re also ensuring that the need for AI is met through their systems.

That is strategic positioning in the open.

The question for CEOs and business leaders is that this is significant because it shows who will control the AI stack in the future, impacting not only AI startups but the whole tech ecosystem.

Infrastructure Is the Real Battleground

The Hindu explains it this way: “Large amounts of funding today is a reflection of the huge amounts of money needed to stay at the forefront of AI.”

Creating advanced AI models requires:

• Large data centers

• Specialized processors

• Long-term energy investments

• Fast, worldwide connectivity

When the funders also provide the infrastructure, the money becomes a strategic play.

You fund the model.

You house the model.

You benefit from the model’s usage.

The cycle is self-perpetuating. Effective. Powerful.

The question for the leaders in the industry is this: Who owns the infrastructure of AI innovation?

The answer is this: At this point, who owns the infrastructure is becoming the key to having a competitive advantage.

Is Circular Financing a Risk?

The structure can raise questions.

When investors and infrastructure providers blur the lines, the issue of independence and fair competition becomes complicated.

From a business perspective, the rationale makes sense:

The cost of AI R&D is high, and strategic investors reduce risk and provide long-term alignment.

The picture accelerates things.

In the world of markets, speed is king, and speed to execution is everything.

AI’s Industrial Phase

The $110 billion funding round marks a transition.

AI is no longer speculative technology. It is industrial infrastructure backed by coordinated capital flows.

We are witnessing:

• Strategic capital concentration

• Deep infrastructure integration

• Ecosystem-driven competition

Circular financing is not an anomaly. It is becoming the architecture of the AI economy.

Circular Financing Is Quietly Powering the AI Revolution

If you're leading AI efforts today, you're likely aware of one simple fact: scaling costs money. Lots of money. Billions, not millions.

And while the media loves to cover funding rounds and valuation multiples, a different phenomenon has been driving a great deal of the behind-the-scenes activity: circular financing.

What Circular Financing Actually Means

At its core, circular financing is straightforward: an investor receives investment funds from a company, and the company spends some of those funds on infrastructure or a service from the same investor.

In the world of AI, for example, a cloud provider or a chip manufacturer funds an AI startup. That AI startup commits to future usage of the provider’s infrastructure.

A simple explanation:

1. A key player in infrastructure funds an AI startup.

2. The AI startup uses the investment to purchase compute, chips, or cloud.

3. The provider recognizes the usage as revenue.

4. The AI startup gets immediate access to top-tier infrastructure.

It’s a loop. But a productive loop.

In a field as capital-intensive as AI, the loop solves a pressing issue: access to compute.

The Strategic Advantages You Should Recognize

Circular financing succeeds because it addresses actual structural issues in the field of AI. When done transparently and strategically, circular financing provides tangible benefits, which include the following:

1. Faster Infrastructure Deployment

With circular financing, AI start-ups don’t need to find their own compute funding before development. This significantly reduces the time to innovation because infrastructure and funding come together in one package.

2. Aligned Long-Term Incentives

Investors are not passive observers; they are active contributors to the ecosystem. When infrastructure players invest, they are essentially aligned on the financial benefits of scaling, uptime, performance, and integrations.

When the AI firm prospers, everyone wins.

3. Reduced Capital Risk for Startups

Compute is one of the highest operating costs in AI. By tying capital to infrastructure commitments, startups avoid speculative expansion and instead scale with structured backing.

4. Revenue Stability for Infrastructure Providers

From the infrastructure investor’s point of view, the benefit of long-term contracts is that they provide predictability in revenue. This predictability helps the infrastructure investor continue to develop the data center, the chip technology, and the infrastructure necessary for the ecosystem. 

The loop essentially funds itself while developing the necessary infrastructure.

What This Means for You as a Leader

If you are running a company, guiding investments, and directing strategies, circular financing is not something to be debated about, but rather something to be thoughtfully engaged in.

Ask yourself:

• Is our use of infrastructure really driving product development?

• Is the contract designed for scalability?

• Is the partnership accelerating value creation for the customer?

When the answer is yes, circular financing is no longer just talk. What circular financing accomplishes:

• Execution risk is eliminated

• Speed to market is accelerated

• Better partnerships are forged

Most notably, circular financing delivers capital to actual technological capabilities rather than superficial balance sheets.

A Quiet Engine of Acceleration

The AI surge is not hype. It’s backed by solid infrastructure, coordinated funding, and strategic alignment.

Circular financing is exactly where all three of those elements come together.

Instead of seeing this as a red flag, we should recognize that this is actually an important tool that helps to align capital intensity with infrastructure needs.

In an environment where the player with the most computing power is the one who wins, financing models that help ensure computing power aren’t distorting the market; they’re turbocharging it.

The companies that understand this concept won’t just be along for the AI ride. They’ll actually help shape the direction of the wave.

ChatGPT Uninstalls Spike 295% After OpenAI’s DoD Deal, As Claude Climbs the US App Store Charts

When trust is uncertain, loyalty shifts quickly.

This is the tale of what’s happened since OpenAI announced its partnership with the U.S. Department of Defense. Within a few short days, uninstall rates for the ChatGPT app rose by a whopping 295%, and Claude, OpenAI’s competitor, soared up the U.S. App Store rankings.

The Trigger: OpenAI’s DoD Contract

OpenAI announced it had landed a contract with the U.S. Department of Defense to provide AI tools to support work in the realm of national security. However, the company was at pains to emphasize the fact that these tools would not be used in the creation of weapons.

But how people feel about something is sometimes quicker to change than the nuances of the issue.

Shortly after the announcement, consumer response became measurable and sharp.

For some, the issue was not the details of the contract, but what it represented. For them, AI is or should remain neutral, or at least separate, from the military.

The Data: A Sudden User Reversal

A 295% jump in uninstalls indicates that there was a sudden behavioral change. Though uninstalls don’t necessarily mean people will stop using the product altogether, they do indicate one thing for certain:

Trust in the markets of AI is tenuous.

The article in the Indian Express indicates that the jump in uninstalls occurred immediately after the deal was made public, suggesting causality.

And for the leaders of the brand, that’s the key takeaway: in the realm of new tech, branding is less about marketing and more about governance.

Opportunity in Competitor Missteps

As ChatGPT downloads faltered, Anthropic’s AI assistant Claude moved in the opposite direction.

Meanwhile, Claude’s downloads increased in the US App Store rankings in the same period, thanks to:

• Increased visibility due to the controversy

• Focus on safety

• Lack of connection to the military

It’s not that this is luck; it’s simply the natural correction of the market. If a leading platform is suffering from reputation risk, people will naturally look to alternatives. Claude became the solution for those people looking for generative AI without compromising their values.

It’s interesting to see the speed at which the market can shift in the world of AI.

Why This Matters to You

If you lead a business integrating AI tools, three strategic realities emerge:

1. Ethics Directly Impact Adoption

AI buyers, whether consumers or enterprises, are increasingly value-driven. Partnerships, contracts, and governance choices can influence retention and churn.

2. Transparency Is Non-Negotiable

OpenAI clarified that its DoD work would focus on non-weaponized use cases. But reactive clarification rarely outpaces initial headlines. Proactive communication matters.

3. Switching Costs in AI Are Low

Unlike legacy enterprise systems, AI assistants are relatively easy to replace. If trust declines, users can uninstall and migrate within minutes.

That creates a fluid competitive environment.

A Signal for the AI Industry

This is an interesting development that underscores a larger change in the world. AI is no longer simply a productivity tool; it’s becoming a form of infrastructure that matters to governance.

The world is watching. Closely.

For OpenAI, the increase in uninstallations is a reputation risk stress test. For Claude, the challenge of rising in the App Store is to prove that prioritizing safety and alignment leads to market success.

The lesson for you is that there is a clear strategic direction involved in AI vendor selection today. Brand risk, values-based stakeholders, and trust are now inextricably linked to the decision of whether to use AI or sell it to others.

Competitive advantage now belongs to teams that control data, infrastructure, and value capture.

Your opinion matters!

Hope you loved reading our piece of newsletter as much as we had fun writing it. 

Share your experience and feedback with us below ‘cause we take your critique very critically. 

Thank you for reading

-Shen & Towards AGI team