Nicolas – nsuchaud – insights that matters

Key financial metrics for tech growth

Voici quelques formules à connaître , en anglais pour coller au tableau ci-apres :

Spécial bourse

EPS = Earnings per Share (EPS)
Net Profit / Total Shares Outstanding

PE = Price-to-Earnings ( PER en France) :
Price per Share / EPS

ROI = Return on Investment : Net Profit / Total Investment

ROE = Return on Equity :
Net Profit / Shareholders Equity

ROA = Return on Assets :
Net Profit / Total Assets

Spécial startups

MRR = Monthly Reccuring Revenues :
Operating MRR + new MRR – Churn MRR + Expansion MRR – Contraction MRR

ARR = Annually Recurring Revenues

CH = Churn : lost MRR / Opening MRR

ARPU = Total revenues / number of clients

CLV = Customer lifetime value : average monthly revenue* average month’s active

CB = Cash Burn : Cash from Operating Activities + Cash from Investing Activities

NPS = Net Promoter Score :
% Promoters – % Detractors

Les incontournables

OCF = Operating Cash Flow = Net Income + Other Non-Cash Items – Changes in Working Capital

FCF = Free Cash Flow : Operating Cash Flow – Capital Expenditures

CCC : Cash Conversion Cycle : Days of Inventory Outstanding + Days of Sales
Outstanding – Days of Payables Outstanding

NCF = Net Cash Flow : Operating Cash Flow + Investing Cash Flow + Financing Cash Fl.

DCF= Discounted Cash Flow :CF17 (1+5)1 + CF2 (1+52 + ….. + CEn / (1+r)n, where CF
is cash flow, r is the discount rate, and n is the number of periods.

FV = Future Value : CF x (1+r)^t, where CF is cash flow, r is the interest rate, and t is the number of periods.

PP = Payback Period :
Initial Investment / Annual Cash Flow

CR = Cash Ratio 🙁 Cash + Marketable Securities) / Current Liabilities

GP = Gross Profit : Total Revenue – Cost of Goods Sold (COGS)

EBITDA = Earnings Before Interest Taxes Depreci. & Amort: Net Income + Int Expense – Int Income + Taxes + Depr + Amort

GPM = Gross Profit Margin : Gross Profit / Revenue

NOI= Net Other Income:
Other Income – Other Expense

OM = Operating Margin :
Operating Income / Revenue

ROCE = Return on Capital Employed
Operating Profit / Capital Employed

Beaucoup d’autres ratios & définitions dans le tableau de synthèse ci-dessous, que j’ai emprunté à Josh Aharonoff, CPA (US)

Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen and a performance review to keep us honest.

GPT-4 is the master of all it surveys (for now), beating every other LLM on both classic benchmarks and exams designed to evaluate humans, validating the power of proprietary architectures and reinforcement learning from human feedback.
Efforts are growing to try to clone or surpass proprietary performance, through smaller models, better datasets, and longer context. These could gain new urgency, amid concerns that human-generated data may only be able to sustain AI scaling trends for a few more years.
LLMs and diffusion models continue to drive real-world breakthroughs, especially in the life sciences, with meaningful steps forward in both molecular biology and drug discovery.
Compute is the new oil, with NVIDIA printing record earnings and startups wielding their GPUs as a competitive edge. As the US tightens its restrictions on trade restrictions on China and mobilizes its allies in the chip wars, NVIDIA, Intel, and AMD have started to sell export-control proof chips at scale.
GenAI saves the VC world, as amid a slump in tech valuations, AI startups focused on generative AI applications (including video, text, and coding), raised over $18 billion from VC and corporate investors.
The safety debate has exploded into the mainstream, prompting action from governments and regulators around the world. However, this flurry of activity conceals profound divisions within the AI community and a lack of concrete progress towards global governance, as governments around the world pursue conflicting approaches.
Challenges mount in evaluating state of the art models, as standard LLMs often struggle with robustness. Considering the stakes, as “vibes-based” approach isn’t good enough.