Bessemer ✓
@BessemerVP · joined 22 Jul 2009
For the entrepreneurs who want to build revolutions of their own.
178 784Followers
1 249Following
13 108Posts total
57.7KViews on collected posts
Последние посты
Ищем новые посты…
@BessemerVP Curious what convinced you at this round: net revenue retention on the agentic seats past month 12, or pure new-logo growth? Agentic pricing tends to look great until usage resets at renewal.
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@BessemerVP Deployment-as-moat only holds if the deployed accounts expand — what NRR are you underwriting for applied AI seats like this, and how does gross margin look once inference is inside COGS?
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@BessemerVP Thank you for being our long-term partners @BessemerVP
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1/ "We won't use AI in our codebase" is no longer a defensible security posture.
The attacker is using AI against you regardless, so opting out doesn't lower your risk. It just means you find your vulnerabilities second.
CISOs and engineering teams must cover the attack surface
5.5K views · 15 likes
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🚀 We're quadrupling down on @wonderful_ai with a $550M Series C.
In 20 months, Bar Winkler and @lalazar have scaled to 100+ enterprise customers across 35 markets.
They're building the agentic OS that will define how enterprises operate in the AI era.
Just as cloud became htt
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@BessemerVP The distribution point is key. We've seen teams where a few parallel agents burn through most of the budget, while the rest barely use it. Worth tracking per-developer spend to catch runaway usage early.
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6/ This is just one piece of the larger picture when it comes to AI transformation.
@liranesh and @AdamRFisher published The Agentic Awakening ⭐️— the AI-pilled playbook on building the right infrastructure, creating evangelists, and restructuring orgs around agentic
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5/ The measurement advantage is simple: define a peer set (same stage, sector, architecture class), instrument three numbers (AI-attributed code %, effective FTE per human engineer, token spend per engineer), update quarterly. Almost no one does this systematically yet.
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4/ @Uber burned through its entire 2026 AI budget by April—84% of engineers agentic, 70%+ AI-generated code.
@Salesforce is substituting tokens for hiring: ~$300M annually. Neither had systematic measurement before the spike hit.
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3/ One company hit $12K/engineer/month as an org-wide average. 1,700/engineer/month with public claims of 30% productivity gains.
Same input intensity, radically different costs: packages, caching, and model routing matter.
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2/ Teams often have super-engineers producing extraordinary output—or inefficient token-maxxing. The only way to separate them is through quality-adjusted throughput and cost per accepted result. But high spending doesn't tell you which. https://t.co/E4zyA5qjUj
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1/ We spent H1 2026 talking to 20+ CTOs about agentic AI adoption in engineering teams.
On average, organizations spend $1-2K per engineer per month on tokens (many spend less!). But the average hides a wide internal distribution: a few heavy parallel-agent users at https://t.c
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На фоне аккаунтов своего размера
14 постов за последние 90 дней рядом с диапазоном 100K–1M подписчиков. ниже своих по обоим показателям — и по охвату, и по отклику.
Медианные просмотры598этот аккаунт5 983медиана для 100K–1M
Охват, %0.33%этот аккаунт1.49%медиана для 100K–1M
Вовлечённость, %0.64%этот аккаунт0.93%медиана для 100K–1M
| Показатель | Этот аккаунт | Медиана для 100K–1M | Отношение |
|---|---|---|---|
| Медианные просмотры на пост | 598 | 5 983 | 0.10× |
| Охват (просмотры ÷ подписчики) | 0.33% | 1.49% | 0.22× |
| Вовлечённость | 0.64% | 0.93% | 0.68× |
Другие в этом диапазоне → Сравнить с другим аккаунтом → Как считаются эти ориентиры →
Growth & engagement
How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.
Views per post
6.5K27 Aug
674
290
219
590
606
428 Aug
4.3K1 Sep
32.4K2 Sep
6.2K
5.5K
372
37
413 Sep
Last 14 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.
Engagement rate per post
0.97%27 Aug
0.59%
1.38%
1.83%
0.68%
0.83%
0.00%28 Aug
0.16%1 Sep
0.35%2 Sep
0.82%
0.40%
1.08%
0.00%
0.00%3 Sep
Reactions — likes, reposts, replies and quotes — divided by views. Median for 100K–1M accounts is 0.93%.
What the audience does
Likes46.0%245 in total
Reposts3.9%21 in total
Replies2.8%15 in total
Quotes0.2%1 in total
Bookmarks47.1%251 in total
Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.
The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.
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