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Paperback. Between the last trading day of 2022 and now, roughly nine trillion dollars ($9T) of market value went to the companies that design and fabricate silicon. The software companies that own the systems of record, the businesses nearly every strategy framework had named as the winners, added two hundred and forty-six billion ($246B) and trailed the index.An AI company's ceiling is set by where it stands in the stack. Position determines gross margin, how much capital a dollar of forward revenue requires, when cash arrives relative to when it is spent, and how often the underlying asset has to be rebuilt. Execution decides where a company lands inside that band. Position decides which band it is in, and the bands barely overlap. Two companies with identical revenue, growth and churn can be entirely different financial objects, and nothing on a standard metrics dashboard separates them.Who Makes Money in AI maps twelve positions in the stack and puts a price on each one.- Where every dollar of end-customer spend ends up, tier by tier, and how much each layer keeps- How long each position's advantage lasts, with the observable that tells you the clock is running out. The shortest one on the board is three to four months.- Why revenue and cash are different numbers in this market, and where the gap between them kills companies that are growing fast- What one accelerator earns against what it cost, and the single number no operator in the industry discloses- How much capital each position needs, from whom, and in which instrument- What a lender will advance against recurring revenue, and the covenant that quietly decides who ends up owning the company- What an acquirer pays for each position, and the three observable properties that set that price- A due diligence sequence for investors: the ten questions a data room answers in an afternoon, the reporting stack to require at the first board meeting, and what still works when a company has fourteen customers and no revenue history to run the numbers onWritten for founders deciding what to build, operators running a profit and loss where the cost of goods moves every quarter, and investors and due diligence teams who can price a company and cannot yet price a position.Nitin Kumar is a global chief executive officer and scale operator who has built and scaled five businesses and taken two of them to exit. He has added value to more than a thousand mergers and acquisitions across commercial, operational and technical due diligence, and on the integrations and separations that follow them. He is a globally recognized pricing expert retained by venture and private equity funds as an operating partner, a former management consulting partner with leadership roles at Deloitte, PwC and FTI Consulting, and currently co-founder and Chief Executive Officer of The F* Word. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…
N° de réf. du vendeur 9798992302424
- Titre
- Who Makes Money in AI (Paperback)
- Auteur
- Nitin Kumar
- Éditeur
- Aiv
- Année de publication
- 2026
- État de l'article
- new
- Reliure
- Paperback
- Langue
- anglais
- ISBN à 13 chiffres
- 9798992302424
Between the last trading day of 2022 and now, roughly nine trillion dollars ($9T) of market value went to the companies that design and fabricate silicon. The software companies that own the systems of record, the businesses nearly every strategy framework had named as the winners, added two hundred and forty-six billion ($246B) and trailed the index.
An AI company's ceiling is set by where it stands in the stack. Position determines gross margin, how much capital a dollar of forward revenue requires, when cash arrives relative to when it is spent, and how often the underlying asset has to be rebuilt. Execution decides where a company lands inside that band. Position decides which band it is in, and the bands barely overlap. Two companies with identical revenue, growth and churn can be entirely different financial objects, and nothing on a standard metrics dashboard separates them.
Who Makes Money in AI maps twelve positions in the stack and puts a price on each one.
• Where every dollar of end-customer spend ends up, tier by tier, and how much each layer keeps
• How long each position's advantage lasts, with the observable that tells you the clock is running out. The shortest one on the board is three to four months.
• Why revenue and cash are different numbers in this market, and where the gap between them kills companies that are growing fast
• What one accelerator earns against what it cost, and the single number no operator in the industry discloses
• How much capital each position needs, from whom, and in which instrument
• What a lender will advance against recurring revenue, and the covenant that quietly decides who ends up owning the company
• What an acquirer pays for each position, and the three observable properties that set that price
• A due diligence sequence for investors: the ten questions a data room answers in an afternoon, the reporting stack to require at the first board meeting, and what still works when a company has fourteen customers and no revenue history to run the numbers on
Written for founders deciding what to build, operators running a profit and loss where the cost of goods moves every quarter, and investors and due diligence teams who can price a company and cannot yet price a position.
Nitin Kumar is a global chief executive officer and scale operator who has built and scaled five businesses and taken two of them to exit. He has added value to more than a thousand mergers and acquisitions across commercial, operational and technical due diligence, and on the integrations and separations that follow them. He is a globally recognized pricing expert retained by venture and private equity funds as an operating partner, a former management consulting partner with leadership roles at Deloitte, PwC and FTI Consulting, and currently co-founder and Chief Executive Officer of The F* Word.
« Synopsis » peut appartenir à une autre édition de cet ouvrage.
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