Wiley Science and Engineering Content Hub·2 months ago·
29
A whitepaper examines how R&D waste persists even as organizations adopt AI, finding that over one-third of companies waste 25–40 percent of R&D budgets on projects never reaching market. The median cost of a failed project during development is over one million dollars. Organizations apply AI mainly to execution tasks like data analysis rather than decision support, leaving the early ideation phase—where intelligence would prevent waste—unaddressed.
OpenAI organized its first brand trip, inviting influencers on an all-expenses-paid vacation to promote the company. The trip took place over a weekend and generated social media posts from multiple influencers. The initiative drew criticism for projecting frivolousness and triggering negative reactions from both uninvited influencers and the public.
Hank Green paused his YouTube production after criticism over using large language models for research sourcing, acknowledging the practice as unhealthy. Green clarified he used AI to find sources rather than write scripts, but the controversy highlights tensions between creator authenticity and AI tools trained on uncompensated work. The incident raises questions about where creators should draw lines with AI use without compromising their credibility.
Large language models amplify the value of domain expertise rather than eliminating it, as demonstrated by mathematician Terence Tao's superior results with ChatGPT compared to non-specialists asking the same model. Tao's approach—short precise queries, identifying when outputs seem overcomplicated, pushing back without direct contradiction, and making independent suggestions—relies entirely on deep mathematical knowledge. Users with specialized knowledge in their field can steer LLMs toward better solutions by recognizing what good outputs look like and iterating strategically, whereas those without domain expertise can only accept whatever the model produces first.
Grassroots organizations like AI Salon and AI Collective have launched chapters globally since 2023 to hold conversations about AI's societal impact beyond Silicon Valley's dominant narrative. AI Salon operates in three cities with ~20-person gatherings, while AI Collective has grown to 200,000 members across 200 chapters in 50 countries, mostly run by volunteers. These groups aim to broaden AI discourse and build local agency, though critics note participants still tend to be tech-industry workers reproducing existing power imbalances rather than reaching ordinary people.
Box's VP of DACH discusses how companies can adopt AI securely by consolidating unstructured data and automating governance controls rather than choosing between security and usability. He emphasizes that scattered data limits AI effectiveness, citing examples like service technicians receiving outdated information when documents sit in separate systems. Organizations should prepare their data infrastructure and user permission systems before deploying AI models, reducing security risks and Shadow AI adoption where employees use unsanctioned tools.
Every AI story that matters,
in your inbox by 8am.
TLDRocket reads all relevant sources, removes duplicate coverage, and summarises the
day in two minutes. Follow companies and topics for alerts, or get the
briefing in Slack. Free, no spam, unsubscribe anytime.
Reading TLDRocket needs no cookies, and the readership counts we rely on come from
our own cookieless analytics. Google Analytics is the exception: it sets cookies and
reports to Google, so it stays switched off until you allow it. You can change your
mind any time from “Cookie settings” in the footer.
Strictly necessary
Session security and form protection (tldrocket-session,
XSRF-TOKEN, 2 hours). The site cannot work without them,
so they need no consent.
Always on
Google Analytics 4 (_ga,
_ga_<id>, up to 2 years). Measures which
stories and sections readers use. Google acts as a third-party processor and may
store the data outside the EU. No advertising, no profiling, no data sold.
