Live Recap 3: AI Has Burned Billions — Are the Returns Finally Showing Up? Top Tickers Included
1. Live Review Introduction
Tiger Brokers livestream featuring Ross Dong, Founding Partner at Morning Cloud Asset Management, specializing in macro trading and U.S. equities. A former equity trader at firms including J.P. Morgan and KCG, Ross holds a degree in Applied Mathematics from Columbia University.
In this session, the biggest question around AI is no longer whether the technology works — it is whether the returns can justify the enormous spending behind it.
Ross argued that while AI infrastructure remains expensive, cloud revenue, adoption and monetisation are beginning to improve. At the same time, he stressed that technological progress and speculative excess can coexist.
More from the livestream recap series
2. AI Revolution and AI Bubble Can Coexist
Ross does not see AI being revolutionary and AI becoming a bubble as contradictory.
Major technological shifts often come with periods of excessive valuations. AI can deliver genuine productivity gains while individual stocks still become overextended.
He compared today's transition to the early automobile era, when cars and horse-drawn transport briefly shared the same roads before the newer technology took over.
His view: AI adoption is still developing, but the direction is increasingly difficult to reverse.
3. AI Is Already Boosting Productivity
Ross believes AI's biggest economic impact lies in productivity, not just better chatbots.
Tools such as ChatGPT, Claude, Gemini, Kimi and DeepSeek are already being used for research, content production, information search and everyday decision-making.
He also highlighted the rise of the One-Person Company (OPC).
With AI agents, one person may eventually perform work that previously required teams of five, ten or more people. Ross linked this trend to rising business formation in the U.S. and Europe.
The potential return from AI therefore extends beyond AI companies themselves — it could show up through higher productivity across the wider economy.
4. The Big Question: Can Revenue Catch Up With AI Costs?
AI remains expensive to build and operate.
Ross noted that some Chinese and open models, including DeepSeek, MiniMax and Kimi, can operate at lower costs than major U.S. frontier models.
This creates the central economic equation:
Infrastructure spending + inference costs must eventually be supported by revenue.
Earlier in the AI cycle, the market worried that companies were spending far faster than they could monetise.
Ross believes that picture is now beginning to improve, with stronger recurring-revenue and cash-generation trends emerging across leading AI platforms.
The debate is shifting from "Where is the revenue?" to "How fast can the revenue scale?"
5. Hyperscalers Are Spending Heavily — But Cloud Revenue Is Growing
Ross discussed major hyperscalers including:
alongside $Oracle(ORCL)$ and $CoreWeave, Inc.(CRWV)$ .
Their challenge is clear: massive AI CAPEX has placed pressure on free cash flow.
But Ross said recent earnings provided more encouraging evidence, particularly through strong cloud growth.
The cycle is beginning to look more constructive:
Higher AI CAPEX → More cloud capacity → Higher cloud revenue
AWS remains a major player, while Ross highlighted improving market share for Microsoft Azure and Google Cloud.
His view is that the major hyperscalers still possess strong competitive moats, and their recent cloud performance suggests AI spending is increasingly translating into commercial returns.
6. AI Will Create Winners and Losers in Software
Ross does not expect AI to benefit every software company equally.
He distinguished between broad horizontal software and more specialized vertical software, arguing that some traditional SaaS products could face greater disruption from AI agents.
One area he views more positively is cybersecurity.
Companies discussed included:
His reasoning is that while AI improves productivity, it can also increase the scale and sophistication of cyber threats — potentially strengthening demand for security solutions.
Ross also highlighted Snowflake as a company with a meaningful data-related moat.
7. $NVIDIA(NVDA)$ Before Earnings: Ross Wouldn't Chase
During the Q&A, Ross was asked whether investors should add Nvidia before earnings.
His answer was cautious rather than bearish.
After Nvidia's multi-year rally, Ross believes the probability of another similarly outsized move is lower from the current stage.
His position:
He would not short Nvidia, but he would not add at the current level either.
The broader lesson is simple:
A great company is not automatically a great trade at every price.
Closing Takeaway
The AI monetisation story is becoming more convincing.
Cloud revenue is growing, AI agents are improving productivity and leading platforms are beginning to show clearer paths toward monetisation.
But valuation still matters.
AI can remain a powerful long-term trend while individual stocks experience sharp corrections.
The next phase of the AI trade may therefore be less about chasing the most obvious winner and more about asking:
Where will the next dollar of AI spending flow?
That is exactly what we explore in Live Recap 4.
8. Post-Event Resources
Viewers can follow @Ross_Macro_Trading on the Tiger Community, his YouTube channel TMI Partner, or his X account, Ross Dong. More of his market views and research are also available through his official website, tmipartner.com.
Disclaimer: Investing carries risk. This is not financial advice. The above content should not be regarded as an offer, recommendation, or solicitation on acquiring or disposing of any financial products, any associated discussions, comments, or posts by author or other users should not be considered as such either. It is solely for general information purpose only, which does not consider your own investment objectives, financial situations or needs. TTM assumes no responsibility or warranty for the accuracy and completeness of the information, investors should do their own research and may seek professional advice before investing.
