✅ It’s for both beginners and advanced founders. We cover the fundamentals of picking a niche, writing your newsletter, and growing your list. Plus, advanced tactics like paid acquisition, sponsorship sales, info products, and much more.

✅ You can join live OR asynchronously. There are live sessions, Q&As, and coaching calls. Everything is recorded. You’ll get the edited videos, notes, timestamps, transcripts, and slides. It’s totally fine to move at your own pace.

Write, Grow, Sell has everything you need to start, grow, and monetize your newsletter.

I created this program based on my own experience and work with 50+ of the largest and most successful newsletters and creators, including:

- 1440
- Chartr
- Milk Road
- Sahil Bloom
- WorkWeek
- Dan Martel
- James Clear
- Money .com
- 1440 Media
- James Clear
- Wondermind
- The Rundown
- Codie Sanchez
- Sherwood News
- The Daily Upside
- And many, many more

I've used the Write, Grow, Sell system to:

1) Drive 10,000,000+ newsletter subscribers for my clients in the past 2 years.

2) Grow my newsletter to 25,000+ subscribers in <2"- Matt McGarry
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⁠Investing visuals
NVIDIA $NVDA Q2 earnings✨

🟢 Revenue: $ 30.0 Bln vs est. $28.7 Bln (+4.5% beat)
🟢 Data center revenue: $27.3B vs Est. $25.2B (8% beat)
🟢 EPS : $0.68 vs est. $0.64 (+6% beat)

🔭 Outlook
• Expect revenue of $32.5B vs estimates of $31.75B
• Announced $50B buyback

📈 Initial stock reaction: down -5%

All eyes are on NVIDIA $NVDA reporting earnings tomorrow after hours.

Here's a quick pre-earnings overview 🔍👇

🔹Est. Revenue: $28.7B (+113%)
🔹Est. EPS: $0.64 (+138%) https://t.co/FlLfzjUscg - Investing visuals
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⁠Investing visuals
RT @Stocktwits: BREAKING 🚨 $NVDA Nvidia just reported earnings...

▪ EPS of $0.68, beating expectations of $0.64 🟢
▪ Revenue of $30B, beating expectations of $28.7B 🟢

Listen to the call live on Stocktwits! Join the convo 👇
https://t.co/N0xGxmED5t https://t.co/2rFcFBRrBQ

Pretty much $NVDA https://t.co/1uWgrX8eBY - Stocktwits
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⁠App Economy Insights
$NVDA NVIDIA Q2 FY25 (ending in July).

• Revenue +15% Q/Q to $30.0B ($1.3B beat).
• Gross margin 75% (-3pp Q/Q).
• Operating margin 62% (-3pp Q/Q).
• Non-GAAP EPS $0.68 ($0.04 beat).

Q3 FY25 guidance:
• Revenue ~$32.5B ($0.8B beat). https://t.co/3T9fGGxUzd
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⁠Quality Investing with Aria
I dont even know why $CRM stock is up

Business as usual, no changes to the thesis. Fair-to-cheap valuation even after the gap up tmr

Cash flow guide increased to 12.8bn from 12bn, for the full year. That’s the only thing i really care about tbh

Good 👍 😅 https://t.co/5ZcatfToF8
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⁠Quality Investing with Aria
250bn market cap company and the operator cant afford a mic better than a 2010 dell laptop

Also Benioff sounds dead tired LOL https://t.co/hwDwbLC2SY
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Aswath Damodaran (Youtube)
Beat you Bot: Building your moat against AI!
As the AI buzz has carried markets and unsettled businesses, with the threat of disruption, the threat of personal disruption also looks. We are being told that AI is coming for our jobs, and for me, that threat became real when I learned of a bot in my name that had read and listened everything that I have ever written or said. In this session, I look at where the AI threat is greatest (mechanical, rule-driven and objective) and what we can do to keep the threat at bay - broaden our horizons, strengthen reasoning skills, work on our weak sides and let our minds wander.
Blog post: https://aswathdamodaran.blogspot.com/2024/08/beat-your-bot-building-your-moat.html

⁠Bourbon Capital
The following companies just reported earning

After hours:

$NVDA Nvidia -5.66%🔴
$CRM SalesForce +3.52%🟢
$CRWD CrowdStrike +4.03%🟢
$HPQ HP -2.91%🔴
$VEEV Veeva Systems +3.59%🟢
$NTAP NetApp -0.96%🔴
$PSTG Pure Storage -14.66%🔴
$COO The Cooper Companies +2.31%🟢
$OKTA Okta -6.98%🔴
$NTNX Nutanix +12.85%🟢
$AFRM Affirm Holdings +15.61%🟢
$FIVE Five Below +7.82%🟢
$FL Foot Locker -0.24%🔴
$VSCO Victoria's Secret & Co. +3.14%🟢
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⁠Bourbon Capital
NVIDIA $NVDA Results

- EPS: $0.68 vs. $0.65 expected
- Revenue: $30B vs. $28.9B expected

EPS is up 151% YoY🟢
Revenue is up 122% YoY🟢
Net income is up 168% YoY🟢
Gross margin is up 5% YoY🟢
Operating income is up 174% YoY🟢

It looks like we have another 3 months of bull market if Powell allows this
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Musings on Markets
Beat your Bot: Building your Moat against AI
It seems like a lifetime has passed since artificial intelligence (AI) became the market's biggest mover, but Open AI introduced the world to ChatGPT on November 30, 2022. While ChatGPT itself represented a low-tech variation of AI, it opened the door to AI not only as a business driver, but one that had the potential to change the way we work and live. In a post on June 30, 2023, I looked at the AI effect on businesses, arguing that it had the potential to ferment revolutionary change, but that it would also create a few big winners, a whole host of wannabes, and many losers, as its disruption worked its way through the economy. In this post, I would like to explore that disruption effect, but this time at a personal level, as we are warned that we risk being displaced by our AI counterparts. I want to focus on that question, trying to find the middle ground between irrational terror, where AI consigns us all to redundancy, and foolish denial, where we dismiss it as a fad.

The Damodaran Bot

I was in the eleventh week of teaching my 2024 spring semester classes at Stern, when Vasant Dhar, who teaches a range of classes from machine learning to data science at NYU's Stern School (where I teach as well), and has forgotten more about AI than I will ever know, called me. He mentioned that he had developed a Damodaran Bot, and explained that it was an AI creation, which had read every blog post that I had ever written, watched every webcast that I had ever posted and reviewed every valuation that I had made public. Since almost everything that I have ever written or done is in the public domain, in my blog, YouTube videos and webpage, that effectively meant that my bot was better informed than I was about my own work, since its memory is perfect and mine is definitely not. He also went on to tell me that the Bot was ready for a trial run, ready to to value companies, and see how those valuations measured up against valuations done by the best students in my class.

The results of the contest are still being tabulated, and I am not sure what results I would like to see, since either of the end outcomes would reflect poorly on me. If the Bot's valuations work really well, i.e., it values companies as well, or better, than the students in my class, that is about as strong a signal that I am facing obsolescence, that I can get. If the Bot's valuations work really badly, that would be a reflection that I have failed as a teacher, since the entire rationale for my postings and public valuations is to teach people how to do valuation.

Gauging the threat

In the months since I was made aware of the Damodaran Bot, I have thought in general terms about what AI will be able to do as well or better than we can, and the areas where it might have trouble. Ultimately, AI is the coming together of two forces that have become more powerful over the last few decades. The first is increasing (and cheaper) computing power, often coming into smaller and smaller packages; our phones are now computationally more powerful than the very first personal computers. The second is the cumulation of data, both quantitative and qualitative, especially with social media accelerating personal data sharing. As an AI novice, it is entirely possible that I am not gauging the threat correctly, but there are three dimensions on which I see the AI playing out (well or badly).

1.
Mechanical/Formulaic vs Intuitive/Adaptabl[...]

e: Well before ChatGPT broke into the public consciousness, IBM's Deep Blue was making a splash playing chess, and beating some of the world's greatest chess players. Deep Blue's strength at chess came from the fact that it had access to every chess game ever played (data) and the computing power to evaluate 200 million chess positions per second, putting even the most brilliant human chess player at a disadvantage. In contrast, AI has struggled more with automated driving, not because driving is mechanically complicated, but because there are human drivers on the surface roads, behaving in unpredictable ways. While AI is making progress on making intuitive leaps, and being adaptable, it will always struggle more on those tasks than on the purely mechanical ones.

2.
Rules-based vs Principle-based: Expanding the mechanical/intuitive divide, AI will be better positioned to work smoothly in rules-based disciplines, and will be at a disadvantage in principle-based disciplines. Using valuation to illustrate my point,  accounting and legal valuations are mostly rule-based, with the rules sometimes coming from theory and practice, and sometimes from rule writers drawing arbitrary lines in the sand. AI can not only replicate those valuations, but can do so at no cost and with a much closer adherence to the rules. In contrast, financial valuations done right, are built around principles, requiring judgment calls and analytical choices on the part of appraisers, on how these principles get applied, and should be more difficult to replace with AI.

3.
Biased vs Open minded: There is a third dimension on which we can look at how easy or difficult it will be for AI to replace humans and that is in the human capacity to bring bias into decisions and analyses, while claiming to be objective and unbiased. Using appraisal valuation to illustrate, it is worth remembering that clients often come to appraisers, especially in legal or accounting settings, with specific views about what they would like to see in their valuations, and want affirmation of those views from their appraisers, rather than the objective truth. A business person valuing his or her business, ahead of a divorce, where half the estimated value of that business has to be paid out to a soon-to-be ex-spouse, wants a low value estimate, not a high one, and much as the appraiser of the business will claim objectivity, that bias will find its way into the numbers and value. It is true that you can build AI systems to replicate this bias, but it will be much more difficult to convince those systems that the appraisals that emerge are unbiased.
Bringing this down to the personal, the threat to your job or profession, from AI, will be greater if your job is mostly mechanical, rule-based and objective, and less if it is intuitive, principle-based and open to biases.

Responding to AI

While AI, at least in its current form, may be unable to replace you at your job, the truth is that AI will get better and more powerful over time, and it will learn more from watching what you do. So, what can we do to make it more difficult to be outsourced by machines or replaced by AI? It is a question that I have thought about for three decades, as machines have become more powerful, and data more ubiquitous, and while I don't have all of the answers, I have four thoughts.

1.
Generalist vs Specialist: In the last century, we have seen a push towards specialization in almost every discipline. In medicine, the general practitioner has become the oddity, as specialists abound to treat individual organs and diseases, and in finance, there are specialists in sub-areas that are so esoteric that no one outside those areas can even comprehend the intricacies of what they do. In the process, there are fewer and fewer people who are comfortable operating outside their domains, and humanity has lost something of value. It is the point I made in 2016, after a visit to Florence, where like h[...]

undreds of thousands of tourists before me, I marveled at the beauty of the Duomo, one of the largest free-standing domes in the world, at the time of its construction.


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The Duomo built by Filippo Brunelleschi, an artist who taught himself enough engineering and construction to be able to build the dome, and he was carrying on a tradition of others during that period whose interests and knowledge spanned multiple disciplines. In a postright after the visit, I argued that the world needed more Renaissance men (and women), individuals who can operate across multiple disciplines, and with AI looming as a threat, I feel even more strongly about this need. A Leonardo Da Vinci Bot may be able to match the master in one of his many dimensions (painter, sculptor, scientist), but can it span all of them? I don't think so!

2.
Practice bounded story telling: Starting about a decade ago, I drew attention to a contradiction at the heart of valuation practice, where as access to data and more powerful models has increased, in the last few decades, the quality of valuations has actually become worse. I argued that one reason for that depletion in quality is that valuations have become much too mechanical, exercises in financial modeling, rather than assessments of business quality and value. I went on to make the case that good valuations are bridges between stories and numbers, and wrote a book on the topic:


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At the time of the book's publication, I wrote a post on why I think stories make valuations richer and better, and with the AI threat looming, connecting stories to numbers comes with a bonus. If your valuation is all about extrapolating historical data on a spreadsheet, AI can do it quicker, and with far fewer errors than you can. If, however, your valuation is built around a business story, where you have considered the soft data (management quality, the barriers to entry), AI will have a tougher time replicating what you do.

3.
Exercise your reasoning muscle: I have never been good at reading physical maps, and I must confess that I have completely lost even my rudimentary map reading skills, having become dependent on GPS to get to where I need to go. While this inability to read maps may not make or break me, there are other skills that we have has human beings, where letting machines step in and help us, because of convenience and speed, will have much worse long term consequences. In an interview I did on teaching a few years, I call[...]