Pretty nice database of all cybersecurity investment and M&A deals for the last 5ish years.
Whoa. Nice.
Measured mobile app usage (downloads, daily and monthly active users, and consumer revenue —- No API usage, enterprise contracts, or desktop use) for 2.5 years through multiple model launches.
None of the big 3 one have “it” figured out yet and everyone is “winning” on different trajectories.
6000 execs said “meh” so far to AI driven productivity gains. BUT they have high hopes, so party on Wayne! Some nice exec vs worker bee contrasts in this research.
This is quite a quote, “This contrast implies a sizable gap in expectations, with senior executives predicting reductions in employment from AI and employees predicting net job creation.”
A look at conversion metrics across 200 SaaS companies.
Welp, I’m clearly going to have to level up the positioning agent.
Interesting study that argues
Large global developer survey into the kinds of AI apps developers are working on. While this is a teaser for their research, they argue 29% of developers are working on AI apps on edge devices like phones and IoT… which feels high AF to me, but maybe.
How many professional developers are there worldwide? Slashdata says 38.4 million.
How are marketeers using generative AI.
Looks like a good mix of content generation (meh), content repurposing (for sure), and fear of being replaced (low confidence in your own creativity or working for execs who already think marketing is “easy”) - also kudos to the designers on this. It’s a nice mix of data and external examples pulled from social media.
How do content and products get discovered? Search still rules, but fragmentation is upon us. Great data as always.
A surprisingly sober take on the role of AI in cooking our brains.
“Enterprise listening” company Momentum dove into 2,000 conversations with GTM teams to understand the technology issues helping and hindering them. The punchline? CRM and customer data are a mess, leading to bad experiences for prospects, GTM teams, and others. Also, AI use right now is a mess of indiviual and inconsistent use along with a pile of useless pilos.
I’ve lived this and been part of massive projects aimed at fixing it. Since then, the number of data sources and the criticality of understanding the GTM team reality has only increased by 10x.
Every marketer has seen click fraud, Lunio shed some product data on it and whoooeee, even if they are exaggerating a bit (I’m not saying they are, I haven’t even looked closely) the big greedy ad platforms should be ashamed of themselves.
Never underestimate the ability of a man to be blinded by incentives.
I didn’t have time to read this, but the first bit was hella wordy with not a lot of data or demographics. Sorry.
PwC’s 29th! CEO survey - 4,454 CEOs across 95 countries and territories. I likely missed this in the past because the track record of big consulting and surveys is shaky - and they likely spent their time on topics I didn’t care about, but here we are, they care about AI for shnizzle - 30% even claim revenue increases due to AI.
While this is clearly a pitch to help big companies figure out their AI, it’s a decent take on how CEOs feel about AI’s recent past and future.
A dive into the state of energy from all the different angles with, of course, lots of dissection of where AI infrastructure fits into the picture.
Highly recommend. This is a great way to get a good level set on the state of energy, climate change, geopolitics, and more.
A little different from my standard developer and IT infrastructure focused fare, but when my favorite social network dives into their data and does a survey of 30k users, I’m paying attention. How are folks working to keep their minds and bodies in shape? Strava has lots of answers to that ranging from everyone running more to what shoes are on their feet.
As with any product data focused study, how Strava users use strava can be all over the map and influenced by the growth in smart watches and such, although it’s nice to see them tackle some of that in their analysis. It wasn’t clear in the analysis what data came from the survey vs product data. On the other hand, they did a nice job of promoting a few app features alonside the analysis without going overboard.
Another, deeper than most, effort to understand developer AI adoption.
Mckinsey has their say on AI
Common trend on how execs view the impact of AI on employment: 32 percent expect decreases, 43 percent no change, and 13 percent increases. … TLDR: Nobody knows yet.
Is traditional search dead? AI search everything now? Inquiring minds want to know.
I haven’t read this yet, but past performance is a good indicator that this is good.
I don’t know Dataiku, but I dig their research. This report asks data leaders at $1B+ companies about AI Agents and contrasts many of the answers against a similar survey they did with CEOs back in the spring of ‘25. They also break this out by country which is unique and cool.
“59% have already experienced a business issue or crisis stemming from AI hallucinations or inaccuracies in the past year.” Yowzers. Also, I know it can cost a few extra bucks to have someone like Harris Polls do a survey like this, but at first glance, this is very well done.
What’s actually happening online? This report focuses on consumer behaviors, but I love it from a B2B perspective because there is so much overlap in human behaviors. It’s always so well done. They analyzed 912 million social posts - impressive in its own right, especially given how the number of platforms has expanded. Go read it, I guarentee you’ll learn something about the current psycology of humans online.
Funny story - authenticity and integrity get rewarded and they have the data to prove it.
Survey of sales reps. How can you not read this? Just gold nuggets everywhere.
Attainment is up, but leads are harder to come by. The old demand gen playbooks don’t work anymore.
A nice foray into the elements of SaaS success and failure.
You should read Kyle Polar’s newsletter if you don’t already. It’s fantastic.
If you think no one should care much about fonts, you are wrong and old. This survey put data from 12,000 respondents behind that.
A global survey of over 8,000 people working for companies with over 500 people on AI adoption trends finds it’s still early with only 13% of companies actually getting value from AI projects.
This study dropped with an AI assessment tool and a bunch of other assets. It’s a good example of how to do these things well. I thought they underplayed the global nature of the survey a bit, but then again I only gave it a quick skim.
Mercury data on startups spending on AI tools
Interesting, although I wouldn’t place any bets based on this data.
What spearates the best software teams from everyone else? DORA is back for another year with fresh insights into developer AI adoption and more.
ChatGPT usage data y’all. Consumer only.
Lotta homework. 10% of the world’s population.
A product list with a discussion of features and capabilities. Probably skewed a bit to show off FillApp, but overall a nice overview of agent types and what they do.
An analysis of code analysis data of 7.9 Billion lines of code generated by 970,000 developers using AI tools with investigations into code maintainability, security, reliability, and languages.
This looks very cool on the surface, alas I don’t have time to dig into it much.
By reviewing a collection of mostly non-SVB data sources, SVB puts their own spin on the startup and tech capital markets. With large-scale AI companies incinerating money at unprecedented rates, the rest of the startup market is running lean with low “graduation rates”, and VC LP money threatened by institutional endowment taxes, SVB says, “Yo, be careful out there.”
Good read with some non-obvious insights.
290 Cybersecurity Funding Rounds, 160 Acquisitions, but only 16% of deals are AI?? Feels low, gonna have to dig into that. Comments dispelling quick returns in security are interesting too.
Security is back baby. (for now)
Part 1 of the Pragmatic Engineer reader survey (Software engineering and development leadership and staff)
Must read for anyone selling into dev and eng teams building products.
16 experienced developers in “a randomized controlled trial (RCT) to understand how early-2025 AI tools affect the productivity of experienced open-source developers working on their own repositories. Surprisingly, we find that when developers use AI tools, they take 19% longer than without—AI makes them slower.”
I really like the way the authors discuss the limitations and caveats attached to studying the impact of AI on developer productivity. I need to spend som emore time with this study, but on top of the ‘how fast?’ question, I wonder if AI had any impact on the quality of the code or architectural decisions? (And if the tasks included more important software challenges than code generation)
Study into AI adoption and comfort level across numerous types of work.
POE - a chatbot that can access numerous models shares their userbase data so you can see which models are gaining and loosing popularity.
More of a popularity contest than anything else, but still interesting, espeically on the rise of reasoning models and the types of “brands” models have among POE users.
Carta on the
Full methodology description here: https://clickstream.cc/deep-dive-methodology-google-ai-overviews-ux-study/
The OG of security reports. Every year, this report provides a reality check on how breaches happen. Excellent as always.
Second quarterly (so far) survey of application developers using AI to develop apps.
Nice quotes sprinkled throughout. Findings tout a transition from AI just because we need to do something to finding actual use cases. Little to no demographic description (seriously, Vercel? I dig y’all, but that’s lame. I find it hard to believe the demographics of the Vercel community wouldn’t be a bit of a flex) The findings on AI specialization, leadership, and hiring ARE interesting for sure. OpenAI is still the top dog, although devs seem to be getting Gemini and Claude curious as well.
How well to execs understand and use AI and data inside their orgs? This survey takes a look.
Annual report examining model development, costs, capibilities, and adoption. High quality
Small models get better, regulation moves to the states, and no slow down in sight.
79% of employees and executives exaggerate their AI knowledge
Secondaries aren’t just for employees any more. VCs are taking money off the table.
One of the OG’s of cloud adoption
Survey of telco respondents who are or claim to be responsible for deploying and investing in AI solutions. This survey is a good example of the disconnect that can occur between survey perceptions and private commentary from folks who drive investments inside a company. (see comments)
While this survey paints a fairly rosy outlook for AI in Telecom, Tom Nolle has heard almost the exact opposite. I know Tom from my days in networking, and I have no doubt Tom is still as plugged into the telco business as ever. As Tom notes, this disconnect could stem from the types of questions asked or respondents exaggerating. It could also stem from communication disconnects inside telco companies. I suspect both the survey and Tom have the correct views on perception vs reality - and perhaps the time scales of operations. This survey is also a good example of the trend toward MUCH more specificity on the types of personal and corporate use. Alas, even in this survey, if every telco offers a self-service chatbot for support (like AT&T’s POS), then that could be for both improving employee productivity or enhancing customer experience. On the “enhance network operations,” I suspect that one is heavy on the perception that it is a good AI application as opposed to Tom’s observation that AI is nowhere in that realm yet. Lastly, while Tom is plugged into network operations and product management, nVidia has telco customers buying data center gear, and I imagine a good number of the 450 survey respondents are folks in the nVidia Rolodex.
AI adoption focused on business communications with a focus on internal communications (the really important and often overlooked kind) - year 4.
1032 knowledge workers and 254 business leaders - while it would be nice to see the sample size for some of the breakouts, this is a good read for anyone managing knowledge workers using AI tools or building collaboration tools.
Conducted by the Rust Foundation. 7,310 completed surveys in December 2024. Rust is being used more regularly, and it is more likely to be used on the job. (see two charts below that have arrows on them). When asked why they use Rust at work, 47% cite a need precise control over their software, which is up from 37% when the question was asked 2 years ago.
Ongoing analysis of the major LLMs and how well their Gaurdrails behave.
Backblaze datacenter hard drive failure data. Pretty cool.
Year 10! 4T drives are out, 24T drives are in.
The impact of AI on code quality. Lots of discussion of Google’s State of DevOps findings on the same topic with more detail into what’s actually happening.
300 respondents from a B2B panel provider from companies with 150+ developers in the US and Western Europe — this is Port’s target market. Their conclusion about time lost to tool sprawl is based on a very poorly worded question. Read the conclusions carefully — without certain words as caveats, the data can be misinterpreted.
SaaS spending and consumption metrics. Updated frequently.
ChatGPT vs Google behavior data.
A global survey of 715 technical executives and 385 data and artificial intelligence (AI) technologists who work across the fields of data engineering, data science and enterprise architecture. plus interviews with 28 C-suite executives
I’m a fan of inserting the full question asked at the bottom of every slide, as well as the n value for country and other cuts of the data. Also, be sure to check out the Databricks State of AI report in June, which analyzes their platform data.
We know we will make money from AI, but seriously how long until that happens?
Solid round up of cybersecurity deals, funding, drivers, and inhibitors.
Everyone is very excited about agentic AI
Nice mix of survey results and qualitative interviews. Lots of commentary about the relationships and use cases for deterministic robotic process automation (RPA) vs agentic. A definition right at the beginning would have been helpful.
Is ChatGPT threatening Google search?
Nice analysis of actual clickstream data.
A dive into development environments with a lot of detail that makes it tough to understand what’s underway, although as an end user maybe I can see my own content in this survey vs the population as a whole? It’s also clear some more market education in this space is due. -Abner
Survey of 550 software professionals conducted by SlashData. Many of the charts are hard to understand. - Lawrence Heght
3,096 responses to an online survey conducted from June 2024 to September 2024, with 36% of participants being located in Asia, 29% in North America and 15% in Europe. The study was segmented into three parts. Students and Data science and AI practitioners were each asked a series of unique questions. Because of small sample sizes for specific questions, I am not confident in Anaconda's published findings about job security and the types of AI jobs being hired for.
Cool data on AI adoption from customers using Wiz’s platform.
Strangely presented in multiple cases as representative of all cloud customers. For example, 70% of cloud accounts are using an AI service?? That seems super high unless it's 70% of Wiz customer cloud accounts, then I would believe it. It’s odd they don’t just say it’s their massive customer base. Maybe they have limitations based on their MSA, but it feels sloppy. The full report didn’t download when I registered for it, so perhaps the explanation is articulated better in the full report. Otherwise, this looks like a cool study - especially given the profile of the Wiz customer base.
Massive multi-country study on AI adoption and attitudes. Year 2.
Cool that they published the full data tables (see the “other link” below.
How healthy are VC funds, why do they start, die, and other interesting metrics on the life of a fund.
Another State of the cloud survey - lots of focus on multicloud and AI
KPMG is tracking large enterprise business leaders use and plans for AI each quarter. This is the fifth?
CxOs are using AI more than underlings? LeaderGPT?
Survey of developers working on AI applications. Everyone is using AI-assistants / copilots, building agents, but the tools and processes are immature and very few folks are feeling like they’ve become experts in building AI powered apps.
Analysis of 2024 venture investments. It’s all AI these days.
Q: What are people trying to learn about? A: AI
This is always a cool report - especially for marketing folks to get a reality check on knowing if their products are hot or not. Cloud content has plateaued except for GCP? Interesting.
Folks are super excited about AI Agents. Now quantified with expectations for where and how agents are expected to be “in production”
It appears that the vast majority of respondents came from the MLOps Community. Survey conducted in November 2024. 42% are engineers of some sort. They provide selected segmentation based on company size. Additional analysis can be found at https://yougot.us/news/2025-01-19-Agent-Invasion-2025/ The organization that did the study is a consultant, not a vendor.
Estimate and forcast of total U.S. data center electricity use including servers, storage, network equipment, and infrastructure from 2014 through 2028.
Survey of over 1,300 professionals in August 2024. 60% were from tech companies and 51% had <100 employees 78% are developing agents to put into production.
Year 6 of this report examining trends in startup equity with a focus on equity and diversity.
Required reading for every compensation committee and really anyone working or considering a startup job.
WARNING: This feels and reads like a broad quarterly survey of executives at large companies. It could be awesome, but it never mentions their sample size or respondent demographics. WTF Bain?
No demographics.
Analysis of 6.1m pull requests.
8th year of surveying developer on language, tool usage, and what’s getting developed. Really good.
AI + API = uh oh
Survey on the use of AI agents. 3,400 responses, 71% of them from the US. 46% of participants were in C-level roles. There are some interesting datapoints im here, but at the end the top three agents in use are Perplexity, Replit, and Cursor. Wait. What? Are we calling all AI services that aren’t native LLMs Agents? - AG is confused.
The overwhelming majority of respondents have at least tried Langbase itself. I am not confident that the segmentation based on industry is accurate, and even if it is, most of the industries were 4% or less of the study. providing light onto the types of respondents, 71% said software development was a use case for LLMs, with the next most common use case being text generation/summarization (59%) - Lawrence Heght
IoT developer trends, focus areas, and concerns. Year 10!
A curated knowledge base of real-world LLMOps implementations, with detailed summaries and technical notes
Benedict Evan’s annual round up of all things of AI eating the world.
I think I’ve been impressed by Benedict’s annual take for over 15 years?!?
Marketing organizational and spending trends for SaaS GTM teams
Seemingly impressive study on all the elements needed to adopt AI in large companies. Year 2. (I haven’t read it yet)
One part “how did we get here?” combined with views into best practices, links to examples, and “things you should be sure to consider” - I found this a very accessible and educational view into the state of platform engineering.
The hype felt more like cheerleading for leading software teams as opposed to raw hype. Is that a thing? Good read. 5 stars.
Interesting look at generative AI in large companies through the lens of a company selling private LLMs to large companies for marketing and other apps.
That gate form can burn in hell
Be careful doing time series comparison. Many questions are almost the same, but have changed enough that they shouldn’t be compared. Ignore the “new” section for tech managers — not actionable IMHO. 6028 respondents, worldwide, about half of which were from Europe.
Quarterly analysis of the cybersecurity labor market.
Round up of lots of SaaS metrics and trends - especially on buying behaviors and GTM performance.
The OG. Required reading. 10 years!
Nice dive into AI adoption in SDLC and devops
Year 7 of looking a multiple facets of AI technology, applications, research, and more - 212 DENSE google slides. (start with the blog post)
Year 5 in trends in customer success organizations, staffing, reporting, investments, etc.
Jason Lemkin’s comments on CS reporting to sales are spot on. Failure mode.
SaaS spending trends
Broad survey of the US population on their use of generative AI at and outside of work.
Really good. Required reading.
Trends in corporate venture capital investing (CVC). Motivations, outcomes, compensation, and more.
Macro analysis on the way brands are discussed and interact on social media networks. 718 million mentions across 347 brands.
AI power demand for north american data centers
Experiences and perceptions of the use of AI in software development at companies with more than $50m in revenue. Compares adoption and views across respondents in U.S., U.K., Japan, France, Canada, Australia, India, and Germany.
Bait and switch form
Nice checkin on SaaS vendor optimism and struggles. Optimism and sales cycles are both up over 2022.
What do data and analytics folks think the impact of AI is on data and analytics? Big, needs a new stack, and like all data culture surveys, hopes for the democratization of data usage and analysis.
Very fancy landing page/site. Although, for a survey on data and analytics, this felt pretty light on the data. Nice methodology and demographics slide tho.
Insight into the data center people.
How are developers using AI in software development and what do they think of those tools?
55% of technology purchases involve analysts and in many cases through out the purchase process.
Respondents named case studies as their top 3 influence on vendor selection… Those must be fed to them by the sales teams, because they don’t click on things on their own.
Two surveys in one doc! #1 - Kubernetes is bloody hard, now with numbers. Also platform engineering, eh? #2 - Generative AI and AI: are you and can you run it?
Consumer views toward privacy and security.
A few charts and graphs would have helped. Also, the ROI on this seems to be connected to referrals to various data protection services? Kinda odd. It’s not clear why U.S. News would invest in this research.
Nice example of a VC firm doing research in a space to benefit SaaS builders. In this case vertical saas opportunities and strategies.
Analysis of performance review HR data. Super interesting.
I think they used data from their product, but it’s not really clear where the data from the 23,000 reviews came from. In their 2023 survey they used both review data and survey data.
Startup compensation data. Required reading for startup folks.
The big kahuna of developer surveys. Always well done.
Some of the AI answers feel aggressive in terms of usage, maybe the survey is suffering from projection or maybe everyone is getting more done?
DevX from the perspectives of managers and developers able to highlight the differences between their views.
Manager vs doer/front line perception gaps are some of my favorite data points.
Github actions YOLOs software into deployment.
26 privacy regulators from the FTC and similar organizations manually inspected over a thousand sites to document their compliance with privacy and consumer protection regulations.
AI is so hot right now
Downtime and other incidents cost a fortune to diagnose and fix.
This study doesn’t seem to have a name, so I named it. It could also be The State of Customer Impacting Incidents, but that’s a mouthfull.
Cut of F5’s annual application strategy report with an additional 75 respondents responsible for AI technology surveyed with 45% from companies providing digital services as their primary business.
Roughly 10 to 15% of companies are actually “good” at using the cloud.
Q1 2024 cloud cost trends.
Always a good read. Gold star.
Searching for AI value… How and are enterprise product dev and IT folks adopting AI tech?
Cool comparison between Oct ‘23 and Feb ‘24, especially on gen AI use cases on expectations vs reality. Also - security is now far less of a concern than “we don’t have the data to generate value.”
Databricks platform data on the adoption of AI models and consumption.
10,000 customers including 300 of the Fortune 500 - which is a hint of demographics and a humblebrag all in one. I’ll allow it.
Engineering management is under pressure.
Well done
A view into developer tools and productivity inhibitors/drivers.
Customers and prospects are sick of you thinking the world revolves around you.
Pay. The. Product. Marketing. Team. And listen to them.
Study and survey of enterprise developer attitudes, usage, and perceptions of using an AI coding partner. Used a mix of product APIs and questionaires
Pretty rosy. While they did fail to note the sample size, they have done a pile of work to enable dev leaders to measure their own adoption.
Wild romp through the digital media business and Google behaviors. Required read for anyone publishing content.
Review of trends in funding and exits in cybersecurity
How 40 AI apps are priced and packaged.
F5’s 10th annual application security study. API security takes center stage this year.
High quality as always. Also check out their 10 years of insights page.
Public tease from ETR on who security folks are interested in talking to at RSA.
Solid survey on developer behaviors and attitudes, especially on AI.
I’m always skeptical of self-reported assessments of how developers spend their time. However, 21% on new code vs 26% (15%+11%) of time on code optimization and maintenance does seem to pass the sniff test.
Sales moral and attainment by company. Insightful AF.
What are 1100 security folks worried about? Ransomeware, a lot.
Year 7 of this survey into research across the AI industry. Solid overview of the state of AI models, training costs, performance, and more.
AI is officially better than humans on several fronts, but wow is it getting expensive to be in front.
What CEOs want out of AI (and customer service)
Do CEOs know where to best apply AI in their company? Maybe.
LOTS of folks want their data deleted. These are the receipts.
2023 threat and vulnerability analysis
It’s all platforms these days.
Compensation trends and data for technology, product, and design leadership roles. Data is based on candidates placed in 2023 across seed to enterprise companies.
Unclear how many placements were used to generate the data.
The OG of language ranking studies. If you aren’t already paying attention to Redmonk, are you really interested in developers?
Be sure to check out the 2012 to 2024 analysis as well. That’s where the banner image came from.
It’s all fake and you can’t trust anything you see in your web analytics tools.
Spy vs Spy
Private market valuations and trading trends.
63% of the last funding round. That’s the current clearing price of most private companies trading on the private secondary market right now. It’s up from a low of 52%. Say AI one more time baby.
Cool study on the impact of AI in the call center—one of the early and obvious generative AI applications. Although I’ve yet to have a good experience, non-AI call centers hired 89% more agents than AI-powered call centers!
Not vendor sponsored and the full report is paywalled, but I’ve always had lots of respect for the author/analyst and the findings are pretty interesting.
Cloud Development Envirorments - what’s the deal? (Jerry Seinfeld voice)
Salaries plus equity grant data. Solid.
Comparison and analysis of AI models across key metrics including quality, price, performance and speed (throughput & latency), context window & others.
What are telecom providers doing with AI? Or at least what do they aspire to do.
ETR’s spending panel is always interesting. This one looks at DataDog and it’s not great. How well will it align with DD’s actual results? We’ll see.
Rollicking trip through the infrastructure of a startup.
Great fun. Required reading.
2024 venture capital funding stats
Lots of down rounds, bridge funding, and lack of option exercises. A comparison to 2008 or 2001 would be more telling.
What do O’Reilly readers read and train themselves on? It’s all here.
Microservices are down 20% YoY. Long live the monolith?
Findings from 25 developer interviews
Developer demographics and hot topics
A bit heavy on “non-professional” developers in the sample.
How are financial services technology teams using AI? Now in year 4
Crazy weak demographics
A view into Generative AI in the enterprise. Lots of testing, but also some production apps. Folks expect productivity and revenue gains. Very few expect headcount changes.
Vantage platform data on cloud spend. Now includes visibility into DataDog and comparison of service consumption across multiple cloud.
Phenomenal data. The shift in spending on EC2 is an eyebrow raiser.
Analysis of GDPR enforcement actions
Teaser doc for a survey on Developer experience and perceptions for testing and management tools.
Quantification of Gen AI model hallucinations.
Super cool
Spending trends in SaaS based on actual data across 1000+ companies
Very cool trends
Are designers happy? In what areas is collaboration with product teams and others going well, or not?
So many datapoints on SaaS company metrics. Kyle rocks, read his newsletter.
Trends in venture backed investment, valuations, firms, dealflow, etc.
How do we feel about the kubernete in 2023?
Container adoption survey data basck to 2017! Now it’s everyone.
The view from the account rep seats. Indexed. Over time.
Fantastic
Will Gen AI drive more cloud spend? Inquiring minds have already bet Billions $$$$$
Review of industry benchmarks and adoption indicators. Year 6.
What’s hot out on the interwebs among the whippersnappers and everyone else?
Midjourney tops the list with a mere 11 people building the service. Small software teams can have a huge impact.
Analysis of cyber insurance claims data.
How much of your devops process is actually automated? Not much. Also deploying LLM is throwing a wrench in the works.
Comp data from Carta’s customer base. Hiring trends. Matches equity grants and base salaries for the first time.
This is awesome data on multiple fronts. Good work Carta! Constrained option pools and only 28% of options exercised for options expiring in Aug is fascinanting.
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How and when do UX researchers use AI?
How do devops and devsecops leaders use and views on using generative AI differ?
Expert romp through the world of AI Agents. A little out of sync with everything else here, but I’ve become a sucker for The State of [insert tech here]
It’s easier to use AI tools than deploy it in a product. Everyone is worried about sensitive data leaking into and out of LLMs.
Not everything is a cloud service. Lots of software is being deployed in customer environments.
The people responsible for managing identity in large companies all say they are hosed. Now they have the data to know they aren’t alone.
Lateral attacks and ransomware are so hot right now. Pretending you are Rose in finance has always been cool. Every bit of writing on this assumes everyone knows what a PAM is. Spell out your TLAs, please.
DevRel roles, responsibilities, comp, and more
Datadog data on where serverless is used, how it’s deployed, and more. Spiffy.
The role of Terraform in building serverless apps is growing and a really big deal in large orgs.
If your design and UX teams can collaborate well with your product team, you’re going to do well.
Consolidation of AI related laws and legislation.
What services do cloud customers use? Great stuff.
They did a study, but where did they put the data? Am I blind? Inquiring minds want to know.
This survey says folks are worried about mucking up their cloud deployments with insecure infrastructure and they hope learning about cloud will result in career advancement.
Happier developers get more done. Larger orgs, larger teams, bad context/observabilioty, and interruptions drag it all down.
5th annual study into UX researcher tools, trends, and behaviors.
Github would like to help you be more productive and everyone else is experiencing good things while using Copilot, so you should too.
Github sourced a bunch of feedback over twitter. Kinda neat.
In it’s third year, the Hashicorp State of the Cloud Survey is consistently well done and insightful. Despite a skew toward the Hashicorp user base, the study is a good portrayal of the state of large cloud users.
One of the OGs of developer surveys. Lots of AI, cloud, language, and developer behaviors across multiple cohorts. Well done as always.
Everyone is using AI tools. Can managers keep priorities updated and in line fast enough?
Okta MFA data over time and segmented by industry and other factors.
Myth-buster: More stringent forms of MFA don’t seem to impact user experience.
Oooh, Databricks customer usage data on AI.
A glimpse into cloud spending patterns via the Vantage customer base. Well done. This is pretty awesome and based on comments from the team that published it, I expect it will continue to get even better.
Trends in growing the people already on your team.
Nice report series on appsec processes, software deployment, and more with an additional AI survey for good measure.
Some of the questions suffer from “the author’s world revolves around Github” and come out a bit odd. A question about using AI in the software development process is really “Do you think AI built into Gitlab tools could be helpful?” but is written as “Will you use AI in the software development process”. Read with care.
Very few, if any companies have this whole remote work thing nailed down. Different jobs, work styles, and meeting cultures are all over the map. Miro tries to put some data around how folks are doing.
What defines an elite software development team? CircleCI takes a crack at it using data from how customers use their platform. Very cool.
The **6th** look at the state of AI by AI developers and researchers, plus a collection of other data relevant to AI.
It used to be ball bearings, but now it’s all APIs.. and constant changes + valuable resources at the other end mean API security is A THING. Kudos to my friends at Salt for using both survey AND data from their platform to provide their insight.
Real-world incident data from the Fire Hydrant platform. Rock-on.
5th annual survey on where enterprises are running their apps, why they are making those hosting decisions, and what’s hard about managing all that.
Multi-cloud is everywhere and it’s a PITA. Everyone is using Kubernetes somewhere and cares about sustainability, but 2/3 don’t know where their data resides.
Potentially a very cool report on software team behaviors thwarted by a complete lack of insight into demographics and/or data sources.
How many people did you survey?!? “Hundreds” WTF.
Additional analysis of the 2022 CNCF survey
Year 12!
This survey is focused on cloud migration and adoption in Hong Kong S.A.R., Malaysia, Singapore, the Philippines, Indonesia, Thailand, Japan and South Korea.
Analysis of how developers use O’Reilly content and learning platform.
I’m a fan of the differences between search trends and content consumption. Great stuff.
As the world changes, how does the tech companies use change? Okta sees it all across their customer base and they are kind enough to share it. Pretty awesome. Now in year 7 (I think)
Kandji and Grammerly are killing it. Good for them. If you read this report in Feb, saw that travel was taking off, and bought airline stocks, you would be up huge. Stay on your toes.
This is a “high-level overview” survey, not a “fill out this form” survey.. that said, TeleGeography has long been the source of awesome subterranean cables. They just have a solid view of global bandwidth trends that can help inform decisions for anyone making global investments.
How are cloud strategies changing due to the economic climate? Google’s quarterly survey of 1900 tech and business leaders asks that question.
A glimpse into cloud spending patterns via the Vantage customer base. Well done. This is pretty awesome and based on comments from the team that published it, I expect it will continue to get even better.
Mulesoft with a survey of IT folk making the (correct) arguement that APIs are critical to transforming the massive pile of enterprise applications you have into a better user and developer experience.
Very cool study on kubernetes usage using Dynatrace product data. (Apologies to the Dynatrace graphics folks for the fugly header)
Rust focused derivative of the JetBrains 2022 Developer Ecosystem study
ETR does some cool spending trend surveys among a nicely selected panel.
If you want to understand the state of equity grants and compensation, this report and. the Carta reports are both super informative.
Ooh this one is cool. Incidents - most of what you thought you knew is probably wrong. Root cause analysis? Not meaningful. MTTR? Not a great way to measure distributed systems because averages.
Migration and modernization - what, where, why, and what’s next.
The state of Open Source software
Some cool data on cloud security posture management, data sources, BUT… how many people did they survey? Also, I’m now completely confused by what Vectra does.
No N!!! WTF?
What skills do developers have and what’s in demand? Measured by the Hackerank platform.
On one hand, this survey grates my gears because Adam Larson and I did the very first version of this before New Relic made decisions about infrastructure monitoring. On the other, this has turned into a pretty amazing piece of work that charts the growth of one of the fastest technology adoptions ever. It’s fantastic they copied our first study and kept it going all these years.
Nice mix of SRE pains in survey data and quotes.
Summer of 22, SREs are skeptical of AI.
Everything you wanted to know about Jamstack community demographics. Really good methodology overview.
Cool survey on developer productivity and retention that argues Low Code can increase developer happiness.
Datadog’s first published look into security data on AWS. IAM is hard.
The Frankencloud is here, and enterprise companies don’t have the skills to manage the monster.
Damn it, this isn’t an actual survey, it’s Accenture’s take on Cloud = greener computing. I’m going to leave it up tho becuase I think they are right and they do share some data on ESG business benefits. I might delete this tho.
Mostly a discussion of Chronosphere with a dash of data from 451 Research’s Voice of the Enterprise: Cloud Native, Observability 2022 survey. I really like the “what we thought we were going to get vs what we actually got” question format.
“On behalf of the Go team, thank you to the 5,752 people who told us about their experience working with new features introduced in Go 1.18, including generics, security tooling, and workspaces. You’ve helped us better understand how developers are discovering and using this functionality, and as this article will discuss, provided useful insights for additional improvements.”
[Read the post on Silicon Angle] Where are the gaps between the high flying private company valuations of 2021 and company performance? ETR takes a look and whooee is this interesting.
The discussion of churn for Rubrick and Cohesity sounds like actual churn vs survey results. This is a nit, but $1 says there are slacks and emails grumbling about that paragraph at both companies. When you throw a vendor’s business under the bus like this, some caveats and color on little sample size and bias is super helpful.
Is multicloud really a thing? What makes multicloud or for that matter cloud work for companies? And what holds them back?
Is multi-cloud management a thing? App Portability? Cost controls? Tool sprawl? Nice presentation, no demographics. “US and UK Decision Makers”
The granddaddy of developer surveys. Required reading every year.
Good view into the state of zero trust - TLDR: it’s lagging on assuming breaches will occur with optimistic and/or overly reliant on monitoring.
If you are professionally paranoid, what’s keeping you up at night? This survey reveals those topics with data since 2015. Good fun, ignore at your peril.
Bessemer does a nice job of tracking and analyzing SaaS metrics across public and private markets - this report summarizes and illustrates trends in that dataset. It’s mostly SaaS apps vs my tendency to focus on infrastructure, but I like their stuff, so here you go.
The 15th!!! granddaddy of security incident surveys. Required reading for security folk. Cool data cuts by industry. And written by folks not afraid of riffs like, “if the slanted areas of two (or more) bars overlap, you can’t really say one is bigger than the other without angering the math gods.”
Cloud survey for tech marketeers. Some good decision making and brand affiliation stuff like only 40% of responents feel their primary cloud is a “strategic partner”
In year 11, the Flexera, ne Rightscale report is the GrandDaddy of “multicloud is a thing” “how can we model cloud spend by company size” and “Azure might be bigger than we thought” - goes a bit harder into “cloud waste is out of hand” - if you aren’t reading this thing are you even into cloud research?
I’m oddly happy they included more body than face in their depiction of “cloud guy” this year over last year. (also, scroll down to the bottom for stupid martech tricks.
Trends in early stage financing metrics among the top VC firms.