At the Seventh Manned Spaceflight Conference in Beijing, Chinese space officials publicly outlined the most detailed roadmap yet for landing astronauts on the Moon "before 2030." Ars Technica reports: China's lunar landings will involve a lunar-orbit rendezvous similar to what NASA did in the 1960s and early 1970s with the Apollo program. However, lacking a rocket as powerful as the Saturn V vehicle, the crew and lunar lander will launch separately to the Moon. According to slides shared on social media, the current plan involves: - An uncrewed test of the Long March 10A rocket (a single-stick variant, with no boosters) and the Mengzhou spacecraft to low-Earth orbit. Target: early 2027 - An uncrewed test of the larger Long March 10 rocket (a core stage with two boosters) and the Lanyue lunar lander, including descent/ascent from Moon's surface. Target: 2028 - A concurrent launch of two Long March 10 rockets, one carrying uncrewed Mengzhou and the other the Lanyue vehicle; they will dock in lunar orbit. Target: 2029 - Dual launch of Long March 10 rockets, this time with crew on the Mengzhou spacecraft, leading to crewed landing mission. Target: "Before 2030" The public release of this new information provides a timeline for gauging China's technical progress in its efforts to land humans on the Moon before 2030 and beat NASA and the United States back to the lunar surface. Notably, the plan (for now, at least) omits a mission similar to Apollo 8 or Artemis II, which involved a crew flying out to the Moon and either entering orbit or passing close by before returning to Earth. The Mengzhou vehicle should still get a workout closer to Earth with humans on board as part of missions carrying astronauts to Tiangong, China's space station in low-Earth orbit.

Read more of this story at Slashdot.

Recorded at LDX3 by LeadDev, Ryan chats with Dan Moore, Senior Director of CIAM Strategy and Identity Standards at FusionAuth, about adapting established OAuth2 protocols for the agentic era, the security necessity of giving autonomous AI agents their own distinct identities, and how AI coding tools are shifting developers' focus toward solving core business problems.

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BONUS: AI in the SDLC Puts Customer Feedback Before Faster Typing

AI is already changing software development, but David Mantica argues that the real shift is not only in code generation. In this BONUS episode, Vasco and David talk about what happens when AI enters the whole software development life cycle: executive decision-making, Scrum Master work, product feedback, data quality, agentic systems, and the human skill of learning to adapt.

From Executive Curiosity to Real AI Use Cases

"You have two directions you can go with AI. One is productivity. The other part of AI that organizations haven't quite tapped into yet is how do you make money with it?"

 

David describes AI adoption as something that, like agile and AWS before it, often starts in the middle of the organization before executives fully understand what is happening. Early conversations were about policy, regulation, and tool choices. Now, the conversation has moved toward use cases: where GenAI helps personal productivity, where agentic systems fit, and how organizations turn the tools into actual business value. David also points out a gap that matters for leaders: many executives know their people are using AI, but they are not using it deeply themselves, which makes it harder for them to see what is possible inside their own business model.

AI Should Move Teams Closer to the Business

"If a team does this right, they're going to keep getting closer and closer to the business. They're going to keep spending more time on what it is that we should be building, not on just building it."

 

For David, faster coding is only one part of the SDLC change. Scrum Masters can use AI to improve backlog work, user story mapping, retrospective follow-up, and team data, but the point is not better administrative output. The point is to free time for coaching, communication, and helping teams return to the agile way of working instead of being trapped by artifacts and process. If developers become faster while product discovery, testing, security, and customer feedback stay unchanged, the organization has only accelerated part of the system.

The Bottleneck Is Value, Not Typing Speed

"Can I test more before I go to production? And can I talk to the client more before I actually lock in?"

 

David and Vasco connect AI in the SDLC to product discovery. Coding tools such as Factory may help teams produce software faster, but David argues that the bigger opportunity is to test more ideas, prototypes, classes, webinars, or internal tools with real customers. This links directly to The Black Swan by Nassim Nicholas Taleb: only the market can tell us what is valuable. In practical terms, AI should help teams bring product thinking closer to the coder and bring customer feedback into the SDLC instead of treating it as something that happens after delivery.

Governance Has To Catch Up With AI Productivity

"Organization uses AI, they see the productivity increases, they hire 10-20% less staff because they're getting that productivity increase, and then they create no real new value."

 

David is blunt that many organizations are not yet changing governance, portfolio decisions, budgeting, or success metrics fast enough. Measuring more pull requests per developer is not the same as measuring more value for customers. Vasco and David explore the need for internal product marketing, lightweight feedback loops, and ways for internal and external customers to touch, test, and react to what is being built. Without that, companies risk using AI to reduce cost while missing the larger opportunity: learning faster than competitors.

Agentic Systems Are Only As Good As Their Data

"The agent is only as good as the pool of information by which it's pulling from to answer the questions by which you're asking it to answer."

 

Data quality becomes much more concrete when agents move from answering questions to taking action. David frames agentic systems as coded agents: they behave like a person using GenAI, but they still depend on the information they can access. That means teams need to think carefully about grounding, retrieval, trusted documentation, and the boundaries around what an agent is allowed to use. A weak data pool creates weak action, and a poorly grounded customer service agent will act like a poorly trained human agent.

Lightweight Testing Beats Big AI Programs

"What is your lightweight way of testing your agents without boiling the ocean?"

 

David warns against treating AI adoption as a top-down program that must be perfect before anyone uses it. Agentic systems will make mistakes, just like people do, so teams need controlled environments, visible feedback, and repeated adjustment. Vasco connects this to startup odds: most new initiatives fail, so the answer is not to plan harder, but to test more ideas and learn faster. David's advice is to keep the experiments lightweight, fund learning, and build the capability to test agents without trying to build an entire AI universe first.

The Human Skill Is Adaptation

"The human skill needs to be teaching people to change and adapt, and be comfortable with getting rid of skills that they thought made them hundreds of thousands of dollars that are totally unnecessary now."

 

David closes with the human side of AI in software organizations. He connects AI adoption to adaptive leadership and the fear people feel when skills that once made them valuable may no longer be enough. For Scrum Masters, leaders, and product people, the work is to help teams learn, test, and adapt without pretending that the future is already clear. The customer remains the final judge of value, and AI makes that truth more urgent, not less.

About David Mantica

David Mantica is Managing Director at SoftEd, where he helps organizations build practical skills for AI, agile, leadership, and business analysis. With 30+ years of experience and 700+ speaking engagements, he makes emerging technology accessible for working teams.  LinkedIn

 

You can link with David Mantica on LinkedIn and find role-specific AI resources at SoftEd.

Download audio: https://traffic.libsyn.com/secure/scrummastertoolbox/20261103_David_Mantica_Tue.mp3?dest-id=246429

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