Trade – Stacking Trades

The Algorithm in the Deal Room

Stacking Trades — Mon, 15 Dec 2025 21:52:05 +0000

The modern M&A process starts the same way it always has. Someone believes one company should buy another, and a small group of people works to support that idea. What sets today apart is how quickly these justifications can be put together.

A banker can now pull up a decade of pitch materials, carve out the relevant pieces, and produce something that looks like experience. A diligence team can ask a system to scan thousands of documents for unusual clauses and missing consents, then spend its time arguing about what matters, not where it is. An executive can stress test a synergy thesis in an afternoon, then show up to the next meeting with a confidence that feels earned, even when it is partially borrowed from a model.

AI is creeping into deals the way spreadsheets once did: first as convenience, then as default.

The new front end of dealmaking

The biggest shift is upstream, before anyone signs an NDA. Deal sourcing has always been a mix of relationship and pattern recognition, with a lot of pattern recognition hiding inside relationships. AI is starting to widen that funnel, turning what used to be a whisper network into something closer to a searchable map.

That shift is visible in the tooling vendors and the acquisitions around them. When dealmaking platforms buy “private markets intelligence” businesses that run on AI, they are betting that the next edge comes from getting to the right target earlier, with a clearer picture of where value might be hiding. Datasite’s acquisition of Grata, described as an AI-driven private markets intelligence platform focused on deal sourcing and due diligence, is a clean example of that direction of travel.

The promise is not that an algorithm discovers the perfect target on its own. It is that the long list gets longer, cheaper, and less dependent on who happens to know whom.

Due diligence gets a first draft

The most practical change is in diligence. This work has always been both costly and distinctly human. It involves reading, summarizing, checking, re-reading, and explaining your findings to people who lack the time to read what you read.

McKinsey’s recent work on “outside-in diligence” describes the new workflow plainly: generative AI can take the first pass, synthesizing large volumes of public and proprietary data, identifying trends and outliers, and proposing hypotheses that analysts might not have considered. The caution in that same framing is important too. Many organizations have not yet cracked the operating model that consistently turns tools into impact.

Vendors are racing to make that first pass feel native inside the deal room. Virtual data rooms, historically built for secure sharing and permissions, are adding assistive AI features like automated redaction and review workflows, with the positioning that speed is valuable only if it stays controlled. Datasite, for example, markets AI-enabled tools such as automated redaction, and has pushed newer “Redaction AI” features that still require human review and confirmation.

This is the dealmaker’s version of a change happening across the knowledge economy: AI compresses the time between raw information and a usable narrative. The advantage goes to the team that can validate the narrative fastest, not the team that can generate it.

“In M&A, the winner is often the side that turns information into conviction first.”

Bankers and lawyers become editors of machines

If diligence is where AI changes the muscle, banking and legal teams are where it changes the cadence.

Reuters has reported that Goldman Sachs rolled out a firmwide AI assistant, with around 10,000 employees already using it at the time of the internal memo cited by Reuters. More broadly, large U.S. banks have been describing measurable productivity gains from AI in operations and coding, and executives have been blunt about the implication that doing more with fewer people is now part of the plan.

In M&A work, this means a subtle shift in how labor is distributed. Junior staff still create models and presentations, but their value now lies in verifying, contextualizing, and identifying what the model missed. The focus shifts from creating the initial version to stress testing it. Teams that use AI effectively begin to resemble newsrooms rather than factories. They produce quick drafts, make constant edits, and maintain a strong emphasis on what is true.

Deal structure is changing because regulators are watching

The most underappreciated disruption is not in the spreadsheet. It is in the legal form of the deal.

As AI becomes strategically central, acquisitions and partnerships in the sector have drawn unusually intense scrutiny. The FTC launched a 6(b) inquiry in early 2024 into generative AI investments and partnerships, sending orders to Alphabet, Amazon, Anthropic, Microsoft, and Open AI. In January 2025, the FTC issued a staff report on certain cloud provider and AI developer partnerships, highlighting concerns like lock-in, switching costs, and access to sensitive technical and business information.

That scrutiny has real behavioral consequences. Companies have learned that if a traditional acquisition triggers a fight, there are other ways to get what they want: talent, models, and distribution rights.

Microsoft’s unusual arrangements with Inflection AI became a reference point for this style of “almost acquisition.” Reuters reported that U.S. regulators were looking into the Microsoft Inflection deal in part over concerns it might have been designed to skirt merger disclosure requirements. The UK’s Competition and Markets Authority opened an inquiry and later cleared Microsoft’s hiring of certain former Inflection employees and associated arrangements, treating the situation as a merger inquiry under UK rules even as it ultimately closed the case.

M&A lawyers are already absorbing the lesson: the substance of control, not just the paperwork, is becoming central. The deal is no longer only what you buy. It is how you access models, compute, talent, and data without triggering the harshest form of review.

AI compliance becomes a diligence item

AI is also changing what counts as a material risk.

A decade ago, diligence obsessed over revenue recognition, change-of-control clauses, and litigation exposure. Now, teams increasingly need to understand a target’s model dependencies, data rights, and governance practices. In Europe, this intersects with the EU AI Act’s general-purpose AI obligations, and the voluntary General-Purpose AI Code of Practice published on July 10, 2025, which is positioned as a tool to help providers demonstrate compliance on transparency, copyright, and safety and security.

Even for buyers outside the EU, this matters because global products rarely stay local, and compliance expectations travel through customers, partners, and regulators. In practical terms, AI diligence starts to resemble cyber diligence: less about whether risk exists, more about whether the company has a credible system for managing it.

In the best deals, this does not slow things down. It changes what “fast” means. Fast becomes the ability to answer hard questions with evidence, not the ability to move past them.

AI will not eliminate the most human parts of M&A: the politics, the ego, the leap of faith.

What it will do, and is already doing, is make every stage more legible and more contestable. When both sides have machines that can draft the story, the advantage shifts to the side that can prove it.

The Next Millionaire Wave

Stacking Trades — Fri, 05 Dec 2025 19:49:35 +0000

A Shift Hiding in Plain Sight

The first wave of internet millionaires consisted of people who saw the value of leverage before everyone else. They understood that software could grow faster than labor, and those who took action early gained the most. A similar situation is occurring now, but the factors are different. Instead of websites, distribution, and code, the new leverage comes from artificial intelligence.

This time, the opportunity is not limited to founders or early employees at tech companies. It is emerging across regular professions, small businesses, and individuals who learn how to combine judgment with automation. AI is not just creating efficiency. It is amplifying the output of people who know how to use it well.

The New Leverage

AI‍‌‍‍‌‍‌‍‍‌ nowadays is not favoring the most technically complex solutions, rather it is favoring their strategic application. Basically, the work that was once done by a group of people can now be done by one person who has the proper tools. Analysts get to do research at a much faster pace. Designers get to make variations instantly. Operators get to automate coordination and lessen the chances of errors occurring. Small business owners get to do marketing, customer support, and operations without the need to hire ‍‌‍‍‌‍‌‍‍‌someone.

Reports from multiple enterprise surveys show that employees who integrate AI into daily workflows can increase their productivity by 20 to 50 percent. In specific fields like analytics, marketing, and operations, the gains are even higher. The upside does not come from mastering every tool. It comes from learning how and when to apply the right one.

This is where new wealth creation begins. People who understand their domain and combine it with AI become force multipliers inside organizations, and profit centers when working independently.

“The next wave of millionaires will be the people who learn to pair their expertise with the leverage of intelligent systems.”

The Rise of the AI Enabled Solo Operator

The idea of a one person business used to imply small scale. AI is changing that equation. A single operator can now handle content creation, product design, customer support, outreach, and internal workflows with tools that run in the background.

Freelancers‍‌‍‍‌‍‌‍‍‌ who have AI-driven workflows mention that they have more clients, their delivery times have decreased, and they can now do jobs that they used to refuse. Initial figures from marketplaces indicate a steady increase in the need for skills in AI-assisted writing, design, analysis, and content ‍‌‍‍‌‍‌‍‍‌scaling.

The opportunity does not come from being an AI expert. It comes from knowing your craft deeply and augmenting it with leverage.

The Corporate Advantage

Inside companies, a small number of employees are quietly outperforming their peers because they understand how to use AI to improve core work, not peripheral tasks. They use agents to handle research, generate structured insights, summarize information, and standardize processes. They prevent error chains before they begin.

Executives are starting to notice the pattern. In several recent enterprise studies, leaders reported that the highest performing employees were not the ones with the longest tenure or formal authority. They were the ones who built internal leverage through automation.

This gap will widen. Those who know how to use AI as infrastructure will rise faster than those who wait for permission or formal training.

Real Markets, Real Money

AI is restructuring value, but not in the way most people expect. The early waves of wealth are coming from:

individuals who adopt leverage early
professionals who integrate AI into core workflows
small teams powering entire businesses with minimal overhead
operators who focus on outputs, not tasks

The opportunity is not abstract. It is measurable. AI lowers the cost of action, increases the speed of execution, and compounds the results of people who know how to direct it.

Millions will benefit, but the largest gains will go to those who understand the shift before it becomes obvious.

Going Public in the Intelligence Era

Stacking Trades — Tue, 02 Dec 2025 21:39:32 +0000

The Pressure Behind the Pipeline

After nearly two years of private investment in artificial intelligence, public markets are starting to take notice. Investors are no longer just focused on model companies or chip makers. They are looking at the entire ecosystem: compute marketplaces, enterprise AI platforms, agent infrastructure, synthetic data providers, cybersecurity firms focused on model defense, and vertical AI companies addressing finance, healthcare, and logistics.

The next wave of tech IPOs will not look like the cloud or social media booms. This time, the value is spread across various layers, each connected to the growth rate of AI adoption in different industries. The companies getting ready to go public are focusing on long-term infrastructure instead of short-term trends.

The Catalysts Driving the Next AI IPO Class

Several real world forces are creating pressure for AI companies to enter the public markets.

Chip demand has increased beyond the supply capabilities of Nvidia, AMD, and global foundries. Cloud providers have reported record spending. Companies that provide routing, fine-tuning, retrieval infrastructure, and inference optimization have secured larger private funding rounds. At the same time, businesses are speeding up their AI adoption in areas like productivity, operations, and data analysis.

According to PitchBook and CB Insights, private funding for AI infrastructure companies rose significantly last year. Several late-stage firms have hired CFOs with experience in public markets and have started preparing S-1 level financial reports. Analysts expect the first major filings to come out as markets stabilize.

None of this is speculative. These developments are already visible in venture data, cloud spending reports, and public statements from leading AI infrastructure companies.

“The most valuable AI IPOs will come from companies building the infrastructure that makes intelligence usable, reliable, and scalable.”

Where the Most Promising IPO Candidates Are Emerging

The most likely near term IPO candidates fall into a few categories:

AI infrastructure and orchestration platforms
Companies offering retrieval, fine tuning, model routing, or enterprise deployment tooling have grown rapidly as organizations move beyond experimentation. These firms do not compete directly with frontier model labs. They support them.

Enterprise AI application platforms
Vertical software companies that integrate multiple models plus structured data are seeing demand from industries where compliance, reliability, and traceability matter. Healthcare, finance, and logistics firms are leading adopters.

Specialized semiconductor and networking companies
The global GPU shortage has opened space for companies focusing on interconnect technology, cooling, novel memory architectures, and power optimization. Several of these firms are already late stage and revenue generating.

Security and governance companies
With AI adoption rising, companies specializing in model evaluation, monitoring, and policy enforcement have become essential. Public companies have already begun integrating these tools into compliance workflows.

These segments reflect real economic pressure, not theoretical opportunity. Each is tied directly to measurable enterprise spending.

What Investors Will Look For

For companies considering a public offering, three fundamentals will shape investor confidence.

Revenue concentration
Markets will look for diversified customer bases rather than reliance on a single large contract.

Infrastructure defensibility
Companies that improve inference costs, reliability, or data security at scale have clear moats.

Enterprise retention
High net revenue retention signals that customers are integrating AI deeply into operations rather than running isolated experiments.

The companies that perform best on these metrics are the ones most likely to lead the next IPO cycle.

Why This Wave Will Matter More Than the Last

The upcoming AI IPO cycle is not just about how individual companies perform. It signals a change in how markets assess intelligence infrastructure. In the past, investors concentrated on user platforms or consumer apps. Now, the emphasis is on the systems that drive intelligence throughout the economy.

Artificial intelligence is becoming a long-term operational layer for businesses. Companies that help build, improve, secure, or speed up that layer are likely to become some of the major public market stories of the decade.

The Disruption We Cannot Measure Yet

Stacking Trades — Tue, 25 Nov 2025 20:15:44 +0000

The Shift We Can Feel But Cannot Quantify

Companies have always measured progress. They look at productivity, throughput, efficiency, and margin improvement. Reporting has shaped our understanding of reality. However, a curious change is happening as AI takes on more tasks. The old methods of measuring output can’t reflect the real activity anymore. There is a shift happening beneath the surface that is changing how work flows, but the dashboards remain flat.

In 2025, AI systems will make more decisions, coordinate tasks, and solve complex issues before humans see them. The work is there. The impact is real. However, the metrics designed for human-centered processes cannot capture this. Most organizations monitor what goes through people, not what gets resolved before it reaches them. We are seeing disruption that leaves no trace in the tools meant to spot it.

When Metrics Miss the Work

The signs appear first in operations. A logistics team reports fewer delays, but there is no clear reason. A support team sees a drop in escalations, but workflows remain unchanged. A finance group closes faster than before, but staffing is static. The output improves without any visible inputs.

Multi-agent systems, routing engines, and autonomous workflows now handle edge cases, reorganize queues, and resolve dependencies without surfacing the activity. Problems that would have appeared in reports simply never materialize. The data shows stability. The underlying work is in motion.

A 2024 analysis by McKinsey found that a significant amount of AI-driven productivity improvements had no clear source in traditional KPIs, particularly in operational layers. At the same time, Gartner predicts that by 2026, half of enterprise AI value will come from machine-only workflows that standard performance metrics do not capture.

We are witnessing the rise of invisible productivity.

“The greatest impact of AI is happening where traditional metrics cannot see it.”

The Economy of Invisible Systems

This is not inefficiency. It is evolution. AI systems work at speeds and sizes that human reporting never anticipated. They rearrange data structures, stop errors, highlight contradictions, fix routes, rewrite queries, and improve queues. However, since no human is involved in the task, the system does not log anything significant.

Organizations built measurement frameworks for human bottlenecks. AI removes the bottlenecks, and the frameworks lose their anchor.

Economists are beginning to question how to measure output when machines generate value without involving human labor. There are no timestamps. No check-ins. No handoffs.
There isn’t a model for this yet. Everyone realizes that something significant is getting lost on the dashboards.

The Strategic Blind Spot

Leadership teams face a new challenge. When the most valuable work leaves no metrics, how do you assign credit? How do you identify what to invest in? How do you manage risk? If AI prevents a thousand issues that never occur, what does success look like? If a cluster of agents makes strategic adjustments before anyone sees the underlying problem, who decides whether the system should continue?

In the early 2000s, digitization created new metrics. AI creates fewer. This is the paradox. As intelligence becomes more autonomous, its contribution becomes less visible. Executives who are used to charts and slides must now learn to manage systems where the value is sensed, not measured.

The Coming Redefinition of Measurement

New tools will eventually appear. New metrics and frameworks will emerge. We will see benchmarks that capture stability, prevention, coordination, and invisible orchestration. But for now, we remain in the gap between ability and understanding.

AI is already creating value that organizations cannot quantify. Competition is shifting in ways that do not show up in reports. The advantage lies not in what companies track, but in what their systems quietly resolve.

The disruption is here. We just have not learned how to measure it.

Trading FLOPs

Stacking Trades — Fri, 07 Nov 2025 22:45:38 +0000

The Invisible Commodity

Training the latest generative-AI models costs billions, not millions. By mid-2025, leading labs reported that compute budgets had surged, driven by rising demand for high-performance GPUs and data-center scale-up. For example, CoreWeave signed a five-year contract worth US $11.9 billion with OpenAI in March 2025.

In September, CoreWeave strengthened its partnership with OpenAI for up to $6.5 billion. This brings their total deal to about $22.4 billion.

In essence: compute has become a bottleneck, and bottlenecks trade like commodities.

From Cloud to Market

What started off as dealing with cloud compute as a cost center is now turning into a way of looking at it as a tradable asset. Nvidia announced the new software platform Lepton, which will create a marketplace for cloud-based AI chip capacity, thus marking the formalization of compute-markets.

Meanwhile, the global “GPU as a Service” market is projected to grow from US $6.54 billion in 2024 to US $8.21 billion in 2025, and further to US $26.62 billion by 2030 (CAGR ~26.5%).

Running a training job is no longer just a tech operation—it’s a market signal.

“We used to buy capacity; now we’re trading it.”

Liquidity of Intelligence

Investors aren’t just backing software anymore; they’re backing compute infrastructure. Firms treat GPU clusters, training pipelines, and model-hosting farms as utility assets.

The global data-centre GPU market is predicted to grow from about US$119.97 billion in 2025 to US$228.04 billion by 2030.

In other words, compute throughput is becoming the next lever of market advantage.

The Speculation Layer

Compute markets are still thinly regulated but rapidly gaining complexity. For example, Nvidia’s deal to invest up to US $100 billion in OpenAI (and supply chips) shows how compute commitments now resemble infrastructure finance.

The question becomes, if you’re buying access to AI compute, what asset class do you own?

It sits at the intersection of technology, finance, and commodity markets.

The Human Parallel

Commodity trading desks once monitored oil tankers and futures curves. Today some firms are monitoring compute pipelines and GPU lease contracts.

The analogies are real: just as power plants once provided scale for industrial growth, model-labs now seek scale via compute liquidity.

Value is shifting upstream: from software features to underlying infrastructure.

What It Signals

If compute becomes a core asset class:

• Infrastructure providers (not just hardware vendors) gain strategic importance: marketplaces, leasing platforms, and secondary GPU markets become key nodes.

• Software companies face pressure: increasingly the differentiator is access to compute + data, not just features.

• Investors need new metrics: compute utilisation, petaflops per dollar, latency arbitrage become valuable indicators.

• Markets may shift: competition becomes about compute liquidity as much as talent or data.

10 Ways AI Is Changing What “Edge” Means in the Market

Stacking Trades — Thu, 06 Nov 2025 03:31:14 +0000

1. Edge Is No Longer About Speed — It’s About Foresight

For decades, traders believed speed was the ultimate advantage. The faster your execution, the tighter your latency, the greater your edge. But in the AI era, speed is table stakes. The new form of advantage isn’t reflex — it’s foresight. AI models anticipate order flow before it occurs, detecting micro-signals of sentiment or liquidity shifts long before humans interpret them. The edge has moved upstream, from reacting to predicting — from reflex to reasoning.

2. The New Alpha: Behavioral Intelligence

The market has always been an emotional machine disguised as a rational one. What’s different now is that emotion has become measurable. Modern AI systems don’t just collect and process data — they interpret human behavior encoded within it. They track hesitation in execution, tone in analyst calls, and stress in social sentiment. Edge is no longer about knowing something others don’t, but about understanding how everyone feels before they realize it themselves.

“Markets don’t move on logic — they move on behavior.”

3. Pattern Recognition Becomes Strategy

Intuition once separated great traders from the rest — the ability to notice patterns hidden underneath chaos. Now, AI finds those patterns automatically, at a scale no human can ever match. The challenge isn’t seeing patterns anymore; it’s deciding which ones matter. The strategist’s edge lies in curation — translating machine recognition into human conviction. In a way, AI has made intuition accessible to everyone, forcing professionals to evolve from pattern seekers into interpreters.

4. Emotion Has Become a Measurable Variable

Greed and fear were once metaphors for market cycles. Now they are metrics — quantified in volatility spikes, sentiment deltas, and even the language used on trading forums. AI parses these signals faster than psychology ever could. It knows when optimism turns to mania and when caution turns to panic. Edge now lives in ascertaining the amount of emotion before it shows up in price.

5. Data Volume Isn’t the Advantage — Data Context Is

In an age where everyone has access to the same overflow of information, data alone no longer provides an edge. What matters is how it’s organized, weighted, and contextualized. AI doesn’t need more data — it needs relevant data. The winning systems are those that recognize relationships invisible to humans: cross-market echoes, semantic linkages, behavioral triggers. Context has become the rarest form of intelligence.

“Everyone has data. Few have context.”

6. Models Don’t Replace Gut — They Audit It

Contrary to the narrative of man versus machine, the best decision-makers use AI as a second opinion. The gut hasn’t disappeared — it’s been upgraded. Traders now test intuition against probability, using models to substantiate, challenge, or refine their instincts. The advantage no longer comes from having a feeling, but from knowing when that feeling is statistically sound. The gut is still there — it just submits itself for review.

7. The Rise of Collective Intelligence

Markets have evolved from individual games of wit into systems of shared perception. Edge now arises not from isolation, but from integration — linking multiple data feeds, models, and human perspectives into a single adaptive loop. Intelligence has become elaborate web. The traders and funds who thrive are those who know how to orchestrate human and machine insight into a living, self-correcting organism.

8. Narrative Is Now Quantifiable

Every market runs on story — conviction disguised as narrative. What’s changed is that story itself is now data. AI tracks how narratives form, spread, and mutate across platforms. It can detect the first tremors of like-mindedness before the crowd realizes what it believes. Edge has always belonged to those who understand story flow; now, machines are listening to the same whispers, quantifying belief in real time.

9. Strategy Has Become a Living Model

The static playbook is obsolete. In an environment where conditions evolve by the hour, strategies must evolve too. AI systems learn continuously, refining tactics with each data point. The new edge is adaptability — the ability to let your strategy think for itself. The best funds no longer write plans; they train behaviors.

10. The Quiet Edge: Understanding the Machine’s Mindset

In a sense, the final edge isn’t the machine’s capability but the human capacity to interpret it. Traders who can read what their models are “thinking” — why a prediction changed, why confidence dropped — hold the advantage. The goal is no longer to beat the algorithm but to understand its worldview. Because in a market now driven by synthetic reasoning, the quietest and most powerful edge belongs to those who can think about how intelligence thinks.