AI Stack's Profit Paradox: Infrastructure Earns Big While Model Makers Lose

·1 views

A chart published by Apollo's Torsten Slok reveals a striking inversion in AI industry margins, with semiconductor and equipment firms posting around 41% operating margins while model and application companies sit at roughly -59%. Unlike traditional software, where the customer-facing layer typically captures the most durable profit, AI's margin strength currently lies furthest from the end user. Bulls argue this mirrors early buildout phases of past technologies like cloud and fiber, where infrastructure investment preceded widespread adoption and monetization. Goldman Sachs projects global AI-related investment will surpass $1 trillion in 2026, suggesting the capex cycle is not yet showing signs of slowing. Oracle illustrates the financial strain this creates, reportedly carrying nearly $130 billion in debt and around $260 billion in lease commitments tied to AI infrastructure, alongside negative free cash flow.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

AI-Farmed 'Drive-By' PRs Are Wasting Open Source Maintainers' Time

Open source maintainers are increasingly dealing with 'drive-by' pull requests — contributions submitted with no intention of follow-through, often generated by AI tools pointed at repositories en masse. The pattern typically involves using a large language model to identify fixes, submitting the output across dozens of repos, and never responding to maintainer feedback. These abandoned PRs waste reviewer time and clog project queues, making repositories appear unmaintained to genuine contributors. Maintainers are advised to set explicit response deadlines, close stale PRs without hesitation, and state anti-drive-by policies clearly in contributing guidelines. Contributors are urged to read the code they submit, engage with feedback, and only open PRs they are prepared to see through to completion.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

How Angular HTTP Interceptors Automate JWT Auth and Token Refresh

A developer has shared a technique for managing JWT authentication in Angular applications using an HTTP interceptor. The interceptor automatically attaches an access token to every outgoing API request, eliminating the need to add authorization headers manually in each service. When a request returns a 401 Unauthorized error, the interceptor attempts to silently refresh the access token using a stored refresh token before retrying the original request. If the refresh token is also expired or invalid, the user's session data is cleared and they are redirected to the login page. A key safeguard prevents infinite loops by detecting when the failed request is itself the refresh endpoint, triggering an immediate logout instead of another refresh attempt.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

How to Validate GitHub Webhooks Using HMAC SHA-256 in PHP and Node.js

GitHub webhooks can be exploited if endpoints blindly trust incoming payloads, making signature verification a critical security step. When GitHub dispatches a webhook, it computes an HMAC SHA-256 signature using the raw request body and a shared secret, sending the result in the X-Hub-Signature-256 header. Developers must recalculate this signature server-side from the raw body and compare it against the header value using constant-time comparison functions to prevent timing attacks. In PHP, hash_hmac() and hash_equals() handle this securely, while Node.js offers createHmac() and timingSafeEqual() from the native crypto module. A key implementation detail is that validation must use the raw request body before any JSON parsing, since even minor byte-level changes will produce a different HMAC and cause legitimate requests to fail.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

India's DPDP Rules Notified; Most Compliance Deadlines Fall in 2027

India's Digital Personal Data Protection Act, 2023 received presidential assent in August 2023 but remained without operative rules for two years. On November 13, 2025, the Ministry of Electronics and Information Technology notified the DPDP Rules, 2025, and formally established the Data Protection Board of India. The Rules introduce a staggered enforcement timeline: provisions for Consent Managers take effect from November 13, 2026, while the bulk of obligations — including consent requirements, data principal rights, and breach notification penalties — become enforceable from May 13, 2027. The Act defines three key roles: Data Fiduciary (companies deciding how data is processed), Data Processor (vendors handling data on their behalf), and Data Principal (the individual whose data is collected). While most deadlines are roughly 18 months away, legal experts note that the regulatory framework is now active, giving product teams a finite window to build compliant data architectures.

0 comments Read more at DEV Community