Marketing Analytics & Market Research Company
San Jose, CA
Joined June 2020
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In the latest article from The Arth Group, we discuss '๐ข๐ฝ๐๐ถ๐บ๐ถ๐๐บ ๐ข๐ฝ๐ฒ๐ป๐ ๐๐ต๐ฒ ๐๐ผ๐ผ๐ฟ. ๐ง๐ฟ๐๐๐ ๐๐ ๐๐๐ถ๐น๐ ๐ผ๐ป ๐๐๐ถ๐ฑ๐ฒ๐ป๐ฐ๐ฒ.' and explore how evidence connects with trust and becomes an ally to pathfinders.
linkedin.com/pulse/optimism-โฆ
In Ed 24 of ๐ง๐ต๐ฒ ๐๐ง๐ ๐๐๐น๐ฎ๐ ๐๐
๐ฒ๐ฐ๐๐๐ถ๐๐ฒ ๐๐ฟ๐ถ๐ฒ๐ณ, we introduce ๐๐ฎ๐ฝ๐ฎ๐ฏ๐ถ๐น๐ถ๐๐ ๐ฌ๐ฑ: ๐ฅ๐ฒ๐๐ฒ๐ป๐๐ฒ ๐๐ป๐ฎ๐ฏ๐น๐ฒ๐บ๐ฒ๐ป๐ & ๐๐ผ๐ป๐๐ฒ๐ฟ๐๐ถ๐ผ๐ป and explore how contextual intelligence connects revenue signals to coordinated action.
lnkd.in/gKz98QzV
Architecture in Motion: Designing Campaigns That Orchestrate Themselves linkedin.com/pulse/architectโฆ
MarketScope USA retweeted
In this weekโs ๐๐ง๐ ๐๐๐น๐ฎ๐ ๐๐
๐ฒ๐ฐ๐๐๐ถ๐๐ฒ ๐๐ฟ๐ถ๐ฒ๐ณ, we explore how to turn Marketing Qualified Campaigns (MQCs) into a repeatable, compounding market motion.
๐ Read this weekโs Executive Brief: linkedin.com/pulse/architectโฆ
#GTM #GTMOrchestration #GTMAtlas
Architecture in Motion: Designing Campaigns That Orchestrate Themselves #GTM #Atlas #Marketscope
linkedin.com/pulse/architectโฆ
@gtmatlas
MarketScope USA retweeted
OpenAI Introduces Visual Ads Alongside Image Generation Results tinyurl.com/35ef9fn7 #AI, #DataScientist, #Developer, #MachineLearning, #Deeplearning, #software, #Genai, #ML, #agenticai, #llm, #MCP, #RAG, #LLm
Navigating the AI Frontier: Lessons from Past Crises for Future Resilience linkedin.com/pulse/navigatinโฆ via @LinkedIn @HigheringAI
The GTMAtlas Executive Brief Edition 23: '๐๐ฟ๐ฐ๐ต๐ถ๐๐ฒ๐ฐ๐๐๐ฟ๐ฒ ๐ถ๐ป ๐ ๐ผ๐๐ถ๐ผ๐ป: ๐๐ฒ๐๐ถ๐ด๐ป๐ถ๐ป๐ด ๐๐ฎ๐บ๐ฝ๐ฎ๐ถ๐ด๐ป๐ ๐ง๐ต๐ฎ๐ ๐ข๐ฟ๐ฐ๐ต๐ฒ๐๐๐ฟ๐ฎ๐๐ฒ ๐ง๐ต๐ฒ๐บ๐๐ฒ๐น๐๐ฒ๐' is out now.
lnkd.in/gpFdBgTn
Please read, like, comment, & share the article. #GTM #atlas
MarketScope USA retweeted
Have you ever purchased a product an AI chatbot recommended?
Our study of 2,338 U.S. consumers found that 57.5% of AI users have both purchased a product based on a chatbotโs recommendation and decided not to buy something because of it.
The same AI systems helping brands get discovered can also talk customers out of buying from them.
So what determines which way the decision goes?
Find out in our study: social.semrush.com/4zhivE9.
๐๐
๐ฒ๐ฐ๐๐๐ถ๐๐ฒ ๐๐ฟ๐ถ๐ฒ๐ณ ๐๐ฑ๐ถ๐๐ถ๐ผ๐ป ๐ฎ๐ฌ: ๐ข๐ฝ๐ฒ๐ฟ๐ฎ๐๐ถ๐ผ๐ป๐ฎ๐น๐ถ๐๐ถ๐ป๐ด ๐๐๐ : ๐๐ผ๐ ๐๐ผ ๐ง๐๐ฟ๐ป ๐๐ฐ๐ฐ๐ผ๐๐ป๐ ๐ฆ๐๐ฟ๐ฎ๐๐ฒ๐ด๐ ๐ถ๐ป๐๐ผ ๐ฌ๐ผ๐๐ฟ ๐๐ฎ๐ถ๐น๐ ๐ข๐ฝ๐ฒ๐ฟ๐ฎ๐๐ถ๐ป๐ด ๐ฅ๐ต๐๐๐ต๐บ ๐ถ๐ ๐ผ๐๐ ๐ป๐ผ๐.
lnkd.in/gsPYysaH
Pl read, like, and share!
๐๐
๐ฒ๐ฐ๐๐๐ถ๐๐ฒ ๐๐ฟ๐ถ๐ฒ๐ณ ๐๐ฑ๐ถ๐๐ถ๐ผ๐ป ๐ฎ๐ญ: ๐๐น๐ถ๐ด๐ป๐ถ๐ป๐ด ๐ฌ๐ผ๐๐ฟ ๐๐ง๐ ๐ง๐ฒ๐ฎ๐บ๐ ๐๐ผ ๐ ๐ผ๐๐ฒ ๐ฎ๐ ๐ข๐ป๐ฒ ๐ฅ๐ฒ๐๐ฒ๐ป๐๐ฒ ๐ฆ๐๐๐๐ฒ๐บ ๐ถ๐ ๐ผ๐๐ ๐ป๐ผ๐.
linkedin.com/pulse/aligning-โฆ
Please read, like, and comment on the post. #gtm #GTM #Atlas #GTMAtlas
MarketScope USA retweeted
Our latest article, ๐๐
๐ฒ๐ฐ๐๐๐ถ๐๐ฒ ๐๐ฟ๐ถ๐ฒ๐ณ ๐๐ฑ๐ถ๐๐ถ๐ผ๐ป ๐ฎ๐ฎ: ๐ค๐๐ฎ๐น๐ถ๐ณ๐๐ถ๐ป๐ด ๐ฌ๐ผ๐๐ฟ ๐๐ฎ๐บ๐ฝ๐ฎ๐ถ๐ด๐ป๐ ๐๐ฒ๐ณ๐ผ๐ฟ๐ฒ ๐ง๐ต๐ฒ๐ ๐ค๐๐ฎ๐น๐ถ๐ณ๐ ๐๐ต๐ฒ ๐๐ฒ๐ฎ๐ฑ, is out now.
lnkd.in/p/gUGnVu8n
Please read, like, and comment on the post.
๐๐
๐ฒ๐ฐ๐๐๐ถ๐๐ฒ ๐๐ฟ๐ถ๐ฒ๐ณ ๐๐ฑ๐ถ๐๐ถ๐ผ๐ป ๐ฎ๐ฎ: ๐ค๐๐ฎ๐น๐ถ๐ณ๐๐ถ๐ป๐ด ๐ฌ๐ผ๐๐ฟ ๐๐ฎ๐บ๐ฝ๐ฎ๐ถ๐ด๐ป๐ ๐๐ฒ๐ณ๐ผ๐ฟ๐ฒ ๐ง๐ต๐ฒ๐ ๐ค๐๐ฎ๐น๐ถ๐ณ๐ ๐๐ต๐ฒ ๐๐ฒ๐ฎ๐ฑ is out now.
lnkd.in/p/gUGnVu8n
Please read, like, and comment on the post.
#gtm #GTM #go
MarketScope USA retweeted
Another group of OpenAI agents reached the open internet without OpenAIโs knowledge. tinyurl.com/22wh9kpt #AI, #DataScientist, #Developer, #MachineLearning, #Deeplearning, #software, #Genai, #ML, #agenticai, #llm, #MCP, #RAG, #LLm
MarketScope USA retweeted
President Donald Trump and Venezuelaโs interim authorities are promising prosperity, investment and an influx of money after a sweeping oil deal. But many Venezuelans enduring frequent blackouts told AP they cannot take it anymore.
MarketScope USA retweeted
>be dr. fei-fei li
>born in beijing, raised in chengdu
>dad moves to new jersey, follow at age 16
>speak almost no english
>parents open a dry cleaner
>work the counter on weekends
>get into princeton on a full scholarship
>study physics, because why not
>caltech phd
>everyone in AI is obsessed with algorithms
>bet on data instead
>"this will never scale" they say
>do it anyway
>get 14 million images hand-labeled
>a team from toronto trains a convnet on it in 2012
>error rate falls off a cliff
>deep learning era begins
>become stanford professor
>become google cloud chief scientist
>found stanford HAI
>press starts calling you the godmother of AI
>2024, most of the field is racing on language
>you say the next frontier is spatial intelligence
>co-found World Labs in 2024
>ship Atlas in 2026, a model that turns a few photos into a whole 3D world
World Labs co-founders Fei-Fei Li, Justin Johnson, Ben Mildenhall, and a16z's Martin Casado on Atlas, a world model for spatial intelligence:
LLMs are built on next token prediction. Video models are built on next frame prediction. Atlas is built on new view prediction, and it's the first model to unify pixel generation and pixel reconstruction, two problems computer vision has kept in separate tracks for half a century.
The practical result is a 50 to 100x reduction in what it takes to digitally capture a 3D representation of a space. Previously, you needed 100 to 300 photos of a single room. Atlas can work from just three.
In this conversation, they get into the slow motion shot from The Matrix that took hundreds of cameras and now takes three iPhones, the overnight Slack message that made them bet the company in five seconds, why robotics is bottlenecked on data rather than chips, and the case that new view prediction is AI-complete.
00:00 Intro
01:50 The Matrix slow motion scene now takes three iPhones
02:48 Why new view prediction is the primitive
07:10 Unifying generation and reconstruction
11:15 Gaussian splats became the bottleneck
14:17 Dense capture used to mean 300 photos
17:30 Why reconstruction needs generation to fill the gaps
18:44 The LLM lesson image models missed
23:39 The video that made them go all in
28:04 3D design is 95% revisions
30:50 The problem in robotics is data, not chips
32:48 Why a robot policy can't be trained like an image model
34:44 When the simulator becomes the planner
36:45 Frozen time required footage full of movement
40:57 Why new view prediction is AI-complete
42:43 Nature gave animals eyes but not trees
YouTube: youtube.com/watch?v=qn1QDDBnโฆ
@drfeifei @jcjohnss @BenMildenhall @theworldlabs @martin_casado
MarketScope USA retweeted
Legora's revenue has increased by over 9x from this time a year ago. That's an astounding growth rate for a company of this size. It's not unusual for a startup to grow that fast in the first 6 months, but Legora is 3 years in.
The momentum continues at @WeAreLegora ...we beat our original Q3 '26 plan a full month early. July was the strongest quarter-opening month in Legora's history, and August was even larger.
Total net new ARR grew 36% MoM.
But the story of the month is in-house teams. ARR from in-house is up 15x+ YoY, average ARR per in-house customer is up 2.5x+ YoY, and in-house teams are now almost half of our total new customers.
Most importantly, you earn ARR by building the best product on the market. Our usage climbs every month: 16+ hours per monthly active user.
In September, we expect to add more ARR than in all of Q1 '26 combined, which would make Q3 the best quarter in our history and capture yet another ARR milestone.
LFG!
MarketScope USA retweeted
Moonshot AI CEO Yang Zhilin surprised some of his Carnegie Mellon professors by choosing to return to China. Now, his Beijing-based startupโs latest private funding round values it at $50 billion. on.wsj.com/3SIPgKX
MarketScope USA retweeted
If you're a startup founder, please connect google ads, meta ads, smartlead, apollo, chatbase (if you use it), attio (or your crm) to claude/codex through cli/api (not mcp).
Make each of those tools a folder inside one main repo. Start a session inside each one or in the parent repo if you want them to talk to each other.
Have your agent write code so that when you ask for a "pulse" it fetches all data from all the platforms and tells you what's good, what's not and what you need to fix.
BUT make sure to fully understand the data and don't say yes to every recommendation from LLMs, LLMs are still not good at considering other variables they dont see, not good at correlation/causation, and jump to conclusions too quickly.
This setup is amazing for your to actually understand your business, what your customers want and what the market responds to and be able to run a big part of your gtm conversationally.