Before you "googled" anything, you "altavista'd" it. In 2000, AltaVista held 17% of all web search. Google had 7%. Check out these 5 interesting facts #TechHistory #SearchEngines #Innovation #CAPTCHA #SearchEng substack.com/@ariwaller/note…
Ari Waller retweeted
As AI agents accelerate coding, what is the future of software engineering? Some trends are clear, such as the Product Management Bottleneck, referring to the idea that we are more constrained by deciding what to build rather than the actual building. But many implications, like AI’s impact on the job market, how software teams will be organized, and more, are still being sorted out.
The theme of our AI Developer Conference on April 28-29 in San Francisco is The Future of Software Engineering. I look forward to speaking about this topic there, hearing from other speakers on this theme, and chatting with attendees about it. We’re shaping the future, and I hope you will join me there!
It is currently trendy in some technology and policy circles to forecast massive job losses due to AI. Even if they have not yet materialized, these losses certainly must be just over the horizon! I have a contrarian view that the AI jobpocalypse — the notion that AI will lead to massive unemployment, perhaps even rioting in the streets — won’t be nearly as bad as dire forecasts by pundits, especially pundits who are trying to paint a picture of how powerful their AI technology is.
Among professions, AI is accelerating software engineering most, given the rise of coding agents. According to a new report by Citadel Research, software engineering job postings are rising rapidly. So if software engineering is a harbinger of the impact AI will have on other professions, this expansion of software engineering jobs is encouraging.
Yes, fresh college graduates are having a hard time finding jobs. And yes, there have been layoffs that CEOs have attributed to AI, even if a large fraction of this was “AI washing,” where businesses choose to attribute layoffs to AI, even though AI has not changed their internal operations much yet. And yes, there is a subset of job roles, such as call center operator, that are more heavily impacted. Many people are feeling significant job insecurity, and I feel for everyone struggling with employment, whether or not the cause is AI-related. And many other factors, such as over-hiring during the pandemic and high interest rates, have contributed to the slowdown in the labor market, and the notion that AI is leading to unemployment is oversimplified.
In software engineering, I see a lot of exciting work ahead to adapt our workflows. It is already clear that: (i) As AI makes coding easier, a lot more people will be doing it. (ii) Writing code by hand and even reading (generated) code is not that important, because we can ask an LLM about the code and operate at a higher level than the raw syntax (although how high we can or should go is rapidly changing). (iii) There will be a lot more custom applications, because now it’s economical to write software for smaller and smaller audiences. (iv) Deciding what to build, more than the actual building, is becoming a bottleneck. (v) The cost of paying down technical debt is decreasing (since AI can refactor for you).
At the same time, there are also a lot of open questions for our profession, such as:
- In the future, what will be the key skills of a senior software engineer? And for junior levels, what should be the new Computer Science curriculum?
- If everyone can build features, what skills, strategies, or resources create competitive advantage for individuals and for businesses?
- What are the new building blocks (libraries, SDKs, etc.) of software? How do we organize coding agents to create software?
- What should a software team look like? For example, how many engineers, product managers, designers, and so on. What tooling do we need to manage their workflow?
- How do AI agents change the workflow of machine learning engineers and data scientists? For example, how can we use agents to accelerate exploring data, identifying hypotheses, and testing them?
I’m excited to explore these and other questions about the future of software engineering at AI Dev. I expect this to be an exciting event. Please join us!
[Original text: The Batch newsletter.]
ai-dev.deeplearning.ai/
Ari Waller retweeted
Check out my latest article: Spoiler Alert: Your Vibe Code Isn't Impressing Software Engineers: linkedin.com/pulse/spoiler-a… via @LinkedIn
Ari Waller retweeted
💡 RAG is not enough for building #LLM applications: You need advanced RAG techniques to avoid hallucinations or answers that miss the mark.
Check out this blog and discover how teams are using graph-powered retrieval, hybrid indexes, entity graphs, and context pipelines to go beyond the basics.
Is your RAG system really production-ready?
bit.ly/4o0856t
#GraphRAG #Neo4j
Check out my latest article: Spoiler Alert: Your Vibe Code Isn't Impressing Software Engineers: linkedin.com/pulse/spoiler-a… via @LinkedIn
This is an understatement for me anyway
"Think Twice Before Swimming In Georgia's Most Snake-Heavy Lakes And Rivers"
#Venomoussnake #Swimming #Snake
share.newsbreak.com/fisn5zhe
Ari Waller retweeted
🎥 NOW at #GraphSummit London
@emileifrem about our latest #AI announcements 🎉
Read more: bit.ly/3VL1Oze
Ari Waller retweeted
⭐ From experimental GenAI pilots to intelligent systems that scale with Neo4j ⭐
We are proud to announce a $100 million investment to expand and deepen our critical role in the AI ecosystem. This has already helped us develop two new tools to address critical development obstacles:
▶️ The Neo4j Aura Agent (Early access): an agent-creation platform that enables users to rapidly build, test, and deploy AI agents grounded in their own enterprise data in AuraDB.
▶️ The Model Context Protocol Server for Neo4j: increasing the reliability and trustworthiness of agents and AI applications, readying them for production use.
Read more about these tools and other major updates here: bit.ly/4gRiq1P
#Neo4j #GenAI #MCP
Ari Waller retweeted
So many brilliant minds in one room... all ready to create the next big AI voice 🎙️
Thank you to everyone who came and participated in our hackathon this weekend. And Congratulations to the winners of the coveted Championship Belt @LiquidMetalAI
Watch Live on our Discord Channel!
discord.gg/dDUSCurd?event=14…
Join us in our @discord Hosted #AMA with Jeff Gagnon @neo4j at 8AM PTD / 11AM EST / 5PM CEST discord.com/events/787399249…
I'll be at #NODES2025 online on November 6th! Join me for an incredible day of learning! With 100+ technical and interactive sessions, dive into the cutting-edge innovations in Knowledge Graphs and Gen-AI #neo4j neo4j.com/nodes-2025?utm_sou…
ICYMI 🤖 What if your AI agent could reason, plan & act—not just retrieve data? This blog/video shows how to build agentic AI apps with Google’s Gen AI Toolbox + @Neo4j! 🚀👇
🔗 lnkd.in/eBZVvEGB
#AI #GenAI #KnowledgeGraphs #Neo4j #GoogleAI #GraphRAG
Ari Waller retweeted
A Swedish company, @neo4j , and @emileifrem introduced us to the beautiful world of graphs. We further explored data modeling, ontology, and, of course, vectors, RAGs, and many things.
Ari Waller retweeted
Yes, we did shut down Salesforce a year ago, as we have many SaaS providers—an internal estimate is about 1,200 SaaS shut down.
No, I don't think it is the end of Salesforce; might be the opposite.
Here is what actually happened and how/why we originally intended to NOT share it publicly:
At Klarna, we decided early to explore the potential of AI and LLMs—mostly ChatGPT—while being open to testing all things that seemed to be trending.
We encouraged all employees to do so and allowed them to pursue ideas organically rather than following "management direction" on exactly what they should be building.
In the early days of ChatGPT, we heard a lot:
"this tool allows you to feed all your PDFs, all your data sources to a LLM!"
However, the old universal truth of data scientists still holds true, even in AI: "shit in, shit out."
Feeding an LLM the fractioned, fragmented, and dispersed world of corporate data will result in a very confused LLM.
We started instead exploring a few key concepts: What of our data was actually valuable? What data was duplicative, incorrect, or contradicting? Why was it like that?
While people nowadays can criticize things like Wikipedia, we also reflected on the fact that it is a remarkable achievement—having over 20,000 people collaborate on the largest graph of knowledge that is still fundamentally of high quality, accessibility, and accuracy. What could we learn from this?
A Swedish company, @neo4j, and @emileifrem introduced us to the beautiful world of graphs.
We further explored data modeling, ontology, and, of course, vectors, RAGs, and many things.
Key to our explorations became the conclusion that the utilization of SaaS to store all forms of knowledge of what Klarna is, why it exists (docs), what it tries to accomplish (slides, tickets, kanban boards), how it is doing (sheets, analytics), who is it dealing with (CRM, supplier management), who works here (ERP, HR) and what it has learnt was fragmented over these SaaS—most of them having their own ideas and concepts and creating an unnavigable web of knowledge that required a tremendous amount of Klarna specific expertise to operate and utilize.
We also recognized that enterprise software has a standard set of features that are vital for it to operate—features such as audit, versioning, access and edit management, and similar universal needs. We need them as well, but that fragmentation again adds friction, admin overhead, and more.
So, we decided to start consolidating; to put things together, connect our knowledge, and remove the silos. The side consequence of this was the liquidation of SaaS—not all of them, but a lot of them. And not for the license fees, even though those savings have been nice, but for the unification and standardisation of our knowledge and data.
So no, we did not replace SaaS with an LLM, and storing CRM data in an LLM would have its limitations. But we developed an internal tech stack, using Neo4j and other things, to start bringing data=knowledge together.
Ultimately, we found this very interesting, but more importantly starting seeing serious productivity gains. We allowed our internal AI to use this knowledge, and we realised with the help of @cursor_ai we could quickly deploy new interfaces and interactions with it.
So, I discussed with one of my board members: should we share this publicly?
We decided not to. We hold no grudge against SaaS (not true—I hate some of it, but won't tell you which one). But we are a payments company and a neo bank, there is limited value for us to share this externally.
However, Klarna, being a bank, holds quarterly calls with its investors, and in passing on of these calls, I mentioned that we had removed some SaaS software including Salesforce. It turns out that the recording was leaked to @SeekingAlpha, and they put out a news post about it. And from there, it went crazy.
Suddenly, @Benioff was asked on stage why Klarna was leaving Salesforce. I was tremendously embarrassed.
So, to summarise, what does this mean? Will all companies do what Klarna does? I doubt it. On the contrary, much more likely is that we will see fewer SaaS consolidate the market, and they will do what we do and offer it to others. Those are likely to be your next SaaS.
And it is very likely that Salesforce will be one of those companies. As highlighted many times, they do so much more than CRM today and hence have the opportunity to become that hub of knowledge that modern companies will seek.
But there are also risks for them and others; a lot of our large enterprise SaaS providers suffers from a fallacy. They started as companies with a clear opinion of how to do things, but over time, as they try to satisfy every whim of any random person working at any large enterprise, they become somewhat of a glorified database and lose their opinion. Opinionated software is worth something, as opinions represent an experience of what works, what produces results. And this is the ultimate value.
So I hope with sharing this we can clarify a lot of speculation and misunderstandings and in the end same thing as is always true, just like when mobile came along, we talked about mobile first, now you need to be AI first. Of course all SaaS companies will need to learn adopt and evolve. But if they do there is tremendous opportunity ahead.
Ari Waller retweeted
🚀I’ll be speaking at #Devnexus2025 this March on:
"Building Adaptive Systems with Reinforcement Learning Using Java."
If you're into Java, cloud tech, or AI, don’t miss the largest Java conference in the U.S.!
🎟Save 40% on registration with code SPEAK40!
@atlantajug #Java






















