I Just Committed $150,000 Into a Hyperscaler stock. Why I was wrong about Enterprise AI
Disclaimer: Not a recommendation or advice to buy/sell any of the below-mentioned stock/s. This article documents my own investment thesis and por
A few months ago, I wrote an article explaining why I was skeptical that enterprise AI would generate attractive returns on the enormous amount of capex being deployed into it. In short, I believed that the hallucination rate of LLMs would make them unsuitable for large parts of enterprise work, where certainty matters far more than the average consumer use case.
I now believe I got that conclusion wrong.
In this article, I cover the following:.
- What changed my mind
- Why Enterprise AI's potential is enormous
- The "Tollbooth Company" that benefits the most from Enterprise AI (🔒premium tier)
- 3 reasons why I invested $150,000 in this one stock (🔒premium tier)
- Latest Portfolio allocation and Next Buy Plan (🔒premium tier)
What changed my mind
I first started coding with GPT and Claude around 2-3 years ago. Back then, I constantly found myself trapped in loops. The AI would build something, encounter an error, attempt to fix it, create another problem, and eventually reach a point of an inescapable infinite loop.
That experience, together with the hallucinations I regularly encountered using earlier versions of ChatGPT and Claude, seeded the idea in my head that next-token prediction (the way LLMs generally work) would eventually hit a ceiling in the enterprise space.
What I underestimated was how much the entire system around the base model would continue to improve through better data, post-training, reinforcement learning, longer context windows and more versatile agentic harnesses.
For me, the turning point was Claude Opus 4.8, released on 28 May 2026. That was the first model where I felt a surprisingly large variety of enterprise work could be completed at a level comparable to a competent knowledge worker, despite hallucinations still technically existing. More importantly, I was no longer constantly getting stuck in loops and could actually finish complex tasks end-to-end.
Over the past month, I tested this firsthand by coding extensively, using Claude Code Fable 5 as orchestrator, with GPT Sol and Astra as executor. I was able to vibe code an entire app into production merely by chatting with AI, shipping Palabud, a food app I built to disrupt the traditional Michelin Guide/ World's 50 best Restaurant leaderboard model.
...Shameless plug: If you're a hardcore foodie, do check out my app and follow me at @turtle_eats. Over 30 users have already joined since I launched a few days ago. The more people we have on the network, the more useful it becomes to us foodies...
The newest frontier models also seem to have developed surprisingly good "taste". I can now discuss architecture, user experience, database design, bugs or UI decisions with the model and increasingly feel like I am working with a seasoned product manager, software engineer and designer sitting beside me.
A task that may previously have taken weeks can sometimes be compressed into a day. This productivity increase, alongside an expansion of agentic capabilities and reduced hallucination are what completely changed my view of enterprise AI.
Why Enterprise AI's potential is enormous
I used to think consumer AI would have the cleaner near-term monetisation opportunity. I now think enterprise AI could drive a disproportionate share of incremental inference consumption over the next few years.
The key reason is agency.
The average consumer typically asks a few questions, gets an answer and moves on. There is simply less incentive to keep prompting, iterating or running agents for hours until a task is fully completed.
Enterprise users are different because they are paid to accomplish something. A developer needs to ship code. An analyst needs to finish a financial report. A salesperson needs to research an account. A marketer needs to analyse a campaign.
Because there is a clear economic objective at the end of the task, often tied directly to someone's performance at work, users are far more likely to keep prompting, calling tools and running agents until the job is done. That naturally drives much higher inference consumption per user.
This is also why I think enterprise AI adoption can move faster than mass-market consumer AI. Consumers still need more fully formed products that hide the complexity of prompting and orchestration. In enterprise, the incentive to learn these tools already exists because the productivity payoff is immediate.
I am seeing this firsthand at work. Across both my previous and current jobs, colleagues are already using AI to research questions, analyse information, draft reports, classify data and even prepare entire presentation decks after the AI reads meeting transcripts and related documents.
The use case is clearly not confined to coding alone.
We can see a similar breadth in OpenRouter's share of spending by tasks below.
OpenRouter obviously represents only traffic going through OpenRouter, not the entire AI market. But I think it provides a useful proxy into how broad real-world model consumption is becoming.
Which companies benefit the most from Enterprise AI?
On 29 June 2026, Rihard Jarc wrote this article entitled "Why Token Optimization Is a Gift to the Hyperscalers" which convinced me on the concept of owning an AI tollbooth.
The basic argument is simple. Businesses will increasingly stop using the most expensive frontier model for every task. As cheaper models improve through better training, distillation, post-training and architectural advances, an increasing amount of routine inference can be routed away from frontier models.
I believe that the majority of enterprise inference will eventually be handled by cheaper models that simply clear the intelligence threshold required for the task.
This is precisely how I built Palabud - I used the frontier Fable model for the hardest work such as planning, architecture and orchestration, then routed more mechanical coding towards cheaper models like GPT Sol, Astra and Claude Opus.
I see the same cost optimisation beginning to happen inside enterprises. At my current workplace, default model settings are already increasingly designed around cost rather than maximum intelligence. In my previous workplace, which was one of the more AI-forward enterprises I have worked at, management was similarly beginning to limit expensive frontier-model usage while encouraging employees to use cheaper alternatives for simpler tasks.
This is exactly what Rihard calls the shift from token maxing to token optimisation.
At first glance, this sounds bad for cloud providers. If every individual token keeps getting cheaper, surely everybody makes less money?
I think the opposite may happen.
Whichever model serves the request still needs to run somewhere. The enterprise still needs compute, databases, storage, networking, memory, security, observability and an environment for agents to execute tools.
Someone also needs to orchestrate the whole thing, deciding which model handles which task, managing agent state, routing requests and connecting the models to enterprise systems.
That is where hyperscalers like Microsoft, Amazon and Google increasingly become the AI tollbooths.
As model costs fall, Jevons paradox kicks in. Cheaper inference makes it economical to run more agents, for longer periods, across more tasks. A company may spend less per token but consume vastly more tokens overall. Rihard makes the same argument, noting that cheaper models enable companies to let agents run loops, read entire codebases and repeat tasks that previously would have been too expensive.
The model layer may commoditise but the infrastructural hyperscaler layer underneath it still gets paid.
Why I have chosen this specific company to deploy $150,000 into
Out of the three hyperscalers (Microsoft, Google & Amazon), I believe only one of them has the strongest position in enterprise AI. In the last 2 weeks, I've bet up to $150,000 on this one company (through transactions here with real broker receipts). I totally sold out of one other company to raise funds for this!), making it my 2nd largest position at the moment.
In the following section, I cover,
- The "AI Tollbooth Company" that benefits the most from Enterprise AI (🔒premium tier)
- 3 reasons why I invested $150,000 in this one stock (🔒premium tier)
- Price levels I bought this stock at recently, including 3 transactions with real broker statements (🔒premium tier)
- Latest Portfolio allocation and Next Buy Plan (🔒premium tier)
This company I've deployed $150,000 into is none other than....