Crypto Cuts Continue: Algorand Trims 25% Of Workforce

bitcoinistPubblicato 2026-03-20Pubblicato ultima volta 2026-03-20

Introduzione

The Algorand Foundation has laid off 25% of its staff, citing a challenging global macroeconomic environment and a sustained downturn in the crypto market as the primary reasons. Despite the significant staff reduction, the organization maintains an ambitious roadmap for the year, including major updates to its AlgoKit, the launch of a new wallet called Rocca, and ongoing work on post-quantum security. This move reflects a broader trend of workforce reductions within the crypto industry, reminiscent of cuts made by companies like Coinbase and Gemini during the 2022 bear market. With Bitcoin trading 44% below its all-time high, financial pressures on crypto-native organizations continue to mount, potentially signaling further industry-wide consolidation and cost-cutting measures.

Peter Brandt thinks the crypto market has not hit bottom yet. If he is right, the Algorand Foundation’s decision to cut 25% of its staff may be just one of many similar moves still to come across the industry.

A Leaner Team, A Packed Roadmap

The Algorand Foundation announced the layoffs Wednesday, pointing to a rough stretch in global markets and a sustained pullback in crypto prices as the driving forces behind the decision.

The foundation described the move as painful but necessary, saying it had reached a more sustainable alignment between its spending and its long-term goals.

Affected workers were described as top contributors, and the organization said it would help them through the transition.

What makes the timing unusual is what the Foundation has on its plate for the year ahead. Reports indicate the organization is still pushing forward with several major projects — including the next big update to its developer toolkit AlgoKit, the launch of a new wallet called Rocca, and continued work on post-quantum security.

Cutting a quarter of your team while announcing an ambitious workload is a balancing act, and it remains to be seen whether the remaining staff can carry the load.

Bitcoin Down 44%, And Counting

The layoffs did not happen in a vacuum. Bitcoin is currently trading around $70,000 — roughly 45% below its all-time high of $126,000, which it hit in October.

At its lowest point earlier this year, it fell to $60,000. For foundations that hold portions of their treasury in crypto, a drop like that translates directly into less money to pay staff and fund operations.

Algorand has not been sitting still. Based on a December roadmap update, the Foundation reported it had doubled the amount of ALGO staked online — from around 1 billion to 2 billion — over the span of a little more than a year.

ALGOUSD now trading at $0.08. Chart: TradingView

That kind of growth signals momentum on the technical side, even as the financial pressures mount.

This Is Not The First Time The Crypto Industry Has Done This

The crypto world has been through rounds of staff cuts before. During the 2022 downturn, Coinbase reduced headcount by 18%, and Gemini cut 10% of its workforce.
Both moves came as Bitcoin was trading near two-year lows around $21,000.

This week, blockchain data company Messari also announced layoffs and the departure of its CEO, who stepped down as the company shifted its focus toward artificial intelligence.

Bullish CEO Tom Farley recently said the sector could see more consolidation ahead, with larger firms absorbing smaller ones and trimming overlapping roles in the process.

For the Algorand Foundation, the message is straightforward: do more with less, and stay the course.

Featured image from Unsplash, chart from TradingView

Domande pertinenti

QWhat percentage of its workforce did the Algorand Foundation cut?

AThe Algorand Foundation cut 25% of its workforce.

QWhat were the main reasons cited by the Algorand Foundation for the layoffs?

AThe layoffs were due to a rough stretch in global markets and a sustained pullback in crypto prices.

QWhat major projects does the Algorand Foundation still plan to push forward with despite the layoffs?

AThe Foundation is continuing work on the next update to AlgoKit, the launch of a new wallet called Rocca, and post-quantum security.

QHow much has Bitcoin fallen from its all-time high mentioned in the article?

ABitcoin is trading around $70,000, which is roughly 45% below its all-time high of $126,000.

QWhich other major crypto companies conducted layoffs during the 2022 downturn, as mentioned in the article?

ADuring the 2022 downturn, Coinbase reduced headcount by 18% and Gemini cut 10% of its workforce.

Letture associate

Claude's Major New Feature: Screen Recording + Voice, Distilling Your Skills into AI Tasks in One Click

Claude has introduced a major new feature called "Record a Skill," available for Pro, Max, and Team users. This function, found in the Claude desktop app's CoWork menu, allows users to create reusable AI skills simply by recording their screen and providing voice narration while performing a task. Claude then automatically analyzes the recording and generates a functional Skill. A hands-on test confirmed the feature works seamlessly. Users start recording via the Skills manager, perform their workflow while verbally explaining the steps and logic, and avoid including sensitive information. After recording, Claude processes the content and creates the Skill, which can be saved and later invoked with a slash command (/). This eliminates the need for manual adjustments or writing complex instruction files. The innovation goes beyond mere efficiency. Previously, creating a Skill required writing a detailed SKILL.md file in Markdown—a significant barrier for non-technical users. "Record a Skill" bypasses this by directly capturing both actions and the implicit reasoning shared in the narration. This lowers the barrier to knowledge transfer and automation, addressing a core challenge in corporate knowledge management: the difficulty of getting experts to write and maintain documentation. However, the feature also highlights a shift in the nature of work. A case study from March 2026 showed a freelancer whose five-year client relationship was effectively replaced by a hand-coded Claude Skill automating their content workflow. With the even lower barrier of screen recording, the ability to distill personal expertise into automatable skills accelerates this trend. The "moat" for work is moving from simply knowing how to do a task to mastering tasks that are difficult or impossible to automate.

marsbit2 min fa

Claude's Major New Feature: Screen Recording + Voice, Distilling Your Skills into AI Tasks in One Click

marsbit2 min fa

Feeding AI "Noise" Can Also Boost Scores, This Work Enables Positive Transfer with Noise

Feeding "Noise" to AI Can Improve Performance: A Method Enables Positive Transfer from Noise This work, Semi-Supervised Noise Adaptation (SSNA), introduces a Noise Adaptation Framework (NAF) that challenges traditional transfer learning. Instead of requiring a labeled source domain of real data (e.g., images, text), NAF uses randomly generated Gaussian noise as the source. For a target task with C classes, it constructs C noise clusters by sampling from Gaussian distributions. Although this synthetic noise contains no semantic meaning, NAF trains it to form a discriminative class structure in a shared representation space—clustering same-class noise and separating different classes. The key is aligning this learned structure from the noise domain to the real, sparsely labeled target domain. A small number of target labels are still essential to establish the correspondence between noise clusters and actual classes. The training objective combines: 1) supervised loss on the few labeled target samples, 2) classification loss for the noise to build its structure, and 3) a distribution alignment loss (using Negative Domain Similarity) to minimize the gap between the noise and target domains in the shared space. Experiments show significant gains in few-label settings. With just 4 labels per class, NAF with a ResNet-18 backbone improves accuracy over a standard supervised baseline (ERM) by +12.35% on CIFAR-10, +7.61% on CIFAR-100, +4.38% on DTD-47, and +2.74% on Caltech-101. It also benefits fine-grained datasets and scales to ImageNet-1K (with 100 labels/class) and text classification (AG News). NAF can be integrated into existing semi-supervised methods like FixMatch for further gains. Ablation studies confirm the transferred benefit comes from the discriminative structure of the noise, not randomness itself. Collapsing all noise into a single point causes negative transfer, while increasing separation between noise cluster centers improves performance. The amount of noise per class is less critical once a basic structure forms. In conclusion, this work demonstrates that for positive transfer, the semantic content of source data may not be necessary. What can be effectively transferred is the *organizational structure* of categories within a representation space. This offers a promising alternative for scenarios where real source data is unavailable due to privacy, copyright, or procurement constraints.

marsbit4 min fa

Feeding AI "Noise" Can Also Boost Scores, This Work Enables Positive Transfer with Noise

marsbit4 min fa

Trading

Spot
活动图片